From a28bb021f7159bab1baacec09df086574a0bf8de Mon Sep 17 00:00:00 2001 From: Mathieu Rene Date: Wed, 11 Nov 2020 14:31:22 -0500 Subject: [PATCH] tf 2.3.1 + generated files & protos --- README.md | 17 + go.mod | 5 + go.sum | 2 + tensorflow/go/BUILD | 41 + tensorflow/go/README.md | 103 + tensorflow/go/android.go | 20 + tensorflow/go/attrs.go | 246 + tensorflow/go/attrs_test.go | 193 + tensorflow/go/context.go | 109 + tensorflow/go/context_test.go | 57 + .../allocation_description.pb.go | 136 + .../framework/api_def_go_proto/api_def.pb.go | 524 + .../attr_value_go_proto/attr_value.pb.go | 420 + .../cost_graph_go_proto/cost_graph.pb.go | 473 + .../device_attributes.pb.go | 303 + .../function_go_proto/function.pb.go | 347 + .../core/framework/graph_go_proto/graph.pb.go | 153 + .../graph_transfer_info.pb.go | 611 + .../kernel_def_go_proto/kernel_def.pb.go | 238 + .../log_memory_go_proto/log_memory.pb.go | 457 + .../node_def_go_proto/node_def.pb.go | 243 + .../framework/op_def_go_proto/op_def.pb.go | 546 + .../reader_base_go_proto/reader_base.pb.go | 114 + .../remote_fused_graph_execute_info.pb.go | 218 + .../resource_handle.pb.go | 197 + .../step_stats_go_proto/step_stats.pb.go | 635 + .../framework/summary_go_proto/summary.pb.go | 767 + .../tensor_description.pb.go | 111 + .../framework/tensor_go_proto/tensor.pb.go | 327 + .../tensor_shape_go_proto/tensor_shape.pb.go | 167 + .../tensor_slice_go_proto/tensor_slice.pb.go | 174 + .../core/framework/types_go_proto/types.pb.go | 266 + .../variable_go_proto/variable.pb.go | 334 + .../versions_go_proto/versions.pb.go | 118 + .../for_core_protos_go_proto/autotuning.pb.go | 599 + .../bfc_memory_map.pb.go | 439 + .../for_core_protos_go_proto/cluster.pb.go | 147 + .../for_core_protos_go_proto/config.pb.go | 2263 + .../control_flow.pb.go | 435 + .../conv_autotuning.pb.go | 200 + .../critical_section.pb.go | 143 + .../for_core_protos_go_proto/debug.pb.go | 352 + .../debug_event.pb.go | 1109 + .../device_filters.pb.go | 186 + .../device_properties.pb.go | 261 + .../eager_service.pb.go | 1654 + .../error_codes.pb.go | 239 + .../graph_debug_info.pb.go | 237 + .../for_core_protos_go_proto/master.pb.go | 1213 + .../master_service.pb.go | 53 + .../for_core_protos_go_proto/meta_graph.pb.go | 1168 + .../named_tensor.pb.go | 102 + .../queue_runner.pb.go | 130 + .../remote_tensor_handle.pb.go | 194 + .../for_core_protos_go_proto/replay_log.pb.go | 522 + .../rewriter_config.pb.go | 763 + .../saved_model.pb.go | 102 + .../saved_object_graph.pb.go | 1014 + .../for_core_protos_go_proto/saver.pb.go | 191 + .../for_core_protos_go_proto/struct.pb.go | 886 + .../tensor_bundle.pb.go | 260 + .../tensorflow_server.pb.go | 160 + .../trackable_object_graph.pb.go | 359 + .../transport_options.pb.go | 84 + .../verifier_config.pb.go | 131 + .../for_core_protos_go_proto/worker.pb.go | 2425 + .../worker_service.pb.go | 60 + tensorflow/go/doc.go | 26 + .../go/example_inception_inference_test.go | 291 + tensorflow/go/genop/.gitignore | 2 + tensorflow/go/genop/generate.sh | 62 + tensorflow/go/genop/internal/api_def_map.go | 128 + tensorflow/go/genop/internal/genop.go | 590 + tensorflow/go/genop/internal/genop_test.go | 820 + tensorflow/go/genop/internal/lib.go | 22 + tensorflow/go/genop/main.go | 72 + tensorflow/go/graph.go | 467 + tensorflow/go/graph_test.go | 340 + tensorflow/go/lib.go | 21 + tensorflow/go/op/generate.go | 20 + tensorflow/go/op/gradients.go | 49 + tensorflow/go/op/gradients_test.go | 246 + tensorflow/go/op/op.go | 51 + tensorflow/go/op/op_test.go | 133 + tensorflow/go/op/scope.go | 185 + tensorflow/go/op/scope_test.go | 201 + tensorflow/go/op/wrappers.go | 49701 ++++++++++++++++ tensorflow/go/operation.go | 216 + tensorflow/go/operation_test.go | 269 + tensorflow/go/saved_model.go | 100 + tensorflow/go/saved_model_test.go | 41 + tensorflow/go/session.go | 408 + tensorflow/go/session_test.go | 319 + tensorflow/go/shape.go | 104 + tensorflow/go/shape_test.go | 85 + tensorflow/go/signature.go | 119 + tensorflow/go/signature_test.go | 207 + tensorflow/go/status.go | 67 + tensorflow/go/stream_executor/dnn.pb.go | 588 + tensorflow/go/tensor.go | 510 + tensorflow/go/tensor_handle.go | 170 + tensorflow/go/tensor_handle_test.go | 127 + tensorflow/go/tensor_test.go | 314 + tensorflow/go/test.sh | 76 + tensorflow/go/util_test.go | 65 + tensorflow/go/version.go | 25 + 106 files changed, 83990 insertions(+) create mode 100644 README.md create mode 100644 go.mod create mode 100644 go.sum create mode 100644 tensorflow/go/BUILD create mode 100644 tensorflow/go/README.md create mode 100644 tensorflow/go/android.go create mode 100644 tensorflow/go/attrs.go create mode 100644 tensorflow/go/attrs_test.go create mode 100644 tensorflow/go/context.go create mode 100644 tensorflow/go/context_test.go create mode 100644 tensorflow/go/core/framework/allocation_description_go_proto/allocation_description.pb.go create mode 100644 tensorflow/go/core/framework/api_def_go_proto/api_def.pb.go create mode 100644 tensorflow/go/core/framework/attr_value_go_proto/attr_value.pb.go create mode 100644 tensorflow/go/core/framework/cost_graph_go_proto/cost_graph.pb.go create mode 100644 tensorflow/go/core/framework/device_attributes_go_proto/device_attributes.pb.go create mode 100644 tensorflow/go/core/framework/function_go_proto/function.pb.go create mode 100644 tensorflow/go/core/framework/graph_go_proto/graph.pb.go create mode 100644 tensorflow/go/core/framework/graph_transfer_info_go_proto/graph_transfer_info.pb.go create mode 100644 tensorflow/go/core/framework/kernel_def_go_proto/kernel_def.pb.go create mode 100644 tensorflow/go/core/framework/log_memory_go_proto/log_memory.pb.go create mode 100644 tensorflow/go/core/framework/node_def_go_proto/node_def.pb.go create mode 100644 tensorflow/go/core/framework/op_def_go_proto/op_def.pb.go create mode 100644 tensorflow/go/core/framework/reader_base_go_proto/reader_base.pb.go create mode 100644 tensorflow/go/core/framework/remote_fused_graph_execute_info_go_proto/remote_fused_graph_execute_info.pb.go create mode 100644 tensorflow/go/core/framework/resource_handle_go_proto/resource_handle.pb.go create mode 100644 tensorflow/go/core/framework/step_stats_go_proto/step_stats.pb.go create mode 100644 tensorflow/go/core/framework/summary_go_proto/summary.pb.go create mode 100644 tensorflow/go/core/framework/tensor_description_go_proto/tensor_description.pb.go create mode 100644 tensorflow/go/core/framework/tensor_go_proto/tensor.pb.go create mode 100644 tensorflow/go/core/framework/tensor_shape_go_proto/tensor_shape.pb.go create mode 100644 tensorflow/go/core/framework/tensor_slice_go_proto/tensor_slice.pb.go create mode 100644 tensorflow/go/core/framework/types_go_proto/types.pb.go create mode 100644 tensorflow/go/core/framework/variable_go_proto/variable.pb.go create mode 100644 tensorflow/go/core/framework/versions_go_proto/versions.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/autotuning.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/bfc_memory_map.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/cluster.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/config.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/control_flow.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/conv_autotuning.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/critical_section.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/debug.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/debug_event.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/device_filters.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/device_properties.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/eager_service.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/error_codes.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/graph_debug_info.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/master.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/master_service.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/meta_graph.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/named_tensor.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/queue_runner.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/remote_tensor_handle.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/replay_log.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/rewriter_config.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_model.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_object_graph.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/saver.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/struct.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/tensor_bundle.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/tensorflow_server.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/trackable_object_graph.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/transport_options.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/verifier_config.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/worker.pb.go create mode 100644 tensorflow/go/core/protobuf/for_core_protos_go_proto/worker_service.pb.go create mode 100644 tensorflow/go/doc.go create mode 100644 tensorflow/go/example_inception_inference_test.go create mode 100644 tensorflow/go/genop/.gitignore create mode 100644 tensorflow/go/genop/generate.sh create mode 100644 tensorflow/go/genop/internal/api_def_map.go create mode 100644 tensorflow/go/genop/internal/genop.go create mode 100644 tensorflow/go/genop/internal/genop_test.go create mode 100644 tensorflow/go/genop/internal/lib.go create mode 100644 tensorflow/go/genop/main.go create mode 100644 tensorflow/go/graph.go create mode 100644 tensorflow/go/graph_test.go create mode 100644 tensorflow/go/lib.go create mode 100644 tensorflow/go/op/generate.go create mode 100644 tensorflow/go/op/gradients.go create mode 100644 tensorflow/go/op/gradients_test.go create mode 100644 tensorflow/go/op/op.go create mode 100644 tensorflow/go/op/op_test.go create mode 100644 tensorflow/go/op/scope.go create mode 100644 tensorflow/go/op/scope_test.go create mode 100644 tensorflow/go/op/wrappers.go create mode 100644 tensorflow/go/operation.go create mode 100644 tensorflow/go/operation_test.go create mode 100644 tensorflow/go/saved_model.go create mode 100644 tensorflow/go/saved_model_test.go create mode 100644 tensorflow/go/session.go create mode 100644 tensorflow/go/session_test.go create mode 100644 tensorflow/go/shape.go create mode 100644 tensorflow/go/shape_test.go create mode 100644 tensorflow/go/signature.go create mode 100644 tensorflow/go/signature_test.go create mode 100644 tensorflow/go/status.go create mode 100644 tensorflow/go/stream_executor/dnn.pb.go create mode 100644 tensorflow/go/tensor.go create mode 100644 tensorflow/go/tensor_handle.go create mode 100644 tensorflow/go/tensor_handle_test.go create mode 100644 tensorflow/go/tensor_test.go create mode 100755 tensorflow/go/test.sh create mode 100644 tensorflow/go/util_test.go create mode 100644 tensorflow/go/version.go diff --git a/README.md b/README.md new file mode 100644 index 0000000..61acd91 --- /dev/null +++ b/README.md @@ -0,0 +1,17 @@ +# Tensorflow Go Bindings +This is a fork of tensorflow's repository (at version 2.3.1) with pre-generated protobuf definitions. + +Specificially, this uses protobufs generated with protoc-gen-go v1.3.5 (before the api refactor) + +# Matching libs +| TensorFlow | C library | URL | +| ---------- | --------- | --- | +| Linux || | +| Linux | CPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.3.1.tar.gz | +| Linux | GPU support | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz | +| macOS || | +| macOS | CPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.3.1.tar.gz | +| Windows || | +| Windows | CPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.3.1.zip | +| Windows | GPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.3.1.zip | + diff --git a/go.mod b/go.mod new file mode 100644 index 0000000..cd98f45 --- /dev/null +++ b/go.mod @@ -0,0 +1,5 @@ +module github.com/tensorflow/tensorflow + +go 1.14 + +require github.com/golang/protobuf v1.3.3 diff --git a/go.sum b/go.sum new file mode 100644 index 0000000..b1efb8b --- /dev/null +++ b/go.sum @@ -0,0 +1,2 @@ +github.com/golang/protobuf v1.3.3 h1:gyjaxf+svBWX08ZjK86iN9geUJF0H6gp2IRKX6Nf6/I= +github.com/golang/protobuf v1.3.3/go.mod h1:vzj43D7+SQXF/4pzW/hwtAqwc6iTitCiVSaWz5lYuqw= diff --git a/tensorflow/go/BUILD b/tensorflow/go/BUILD new file mode 100644 index 0000000..bae3173 --- /dev/null +++ b/tensorflow/go/BUILD @@ -0,0 +1,41 @@ +# Description: +# Go API for TensorFlow. + +package( + default_visibility = ["//visibility:private"], +) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load( + "//tensorflow:tensorflow.bzl", + "tf_shared_library_deps", +) + +sh_test( + name = "test", + size = "small", + srcs = ["test.sh"], + data = [ + ":all_files", # Go sources + "//tensorflow/c:headers", # C library header + "//tensorflow/c/eager:headers", # Eager C library header + "//tensorflow/cc/saved_model:saved_model_half_plus_two", # Testdata for LoadSavedModel + ] + tf_shared_library_deps(), + # TODO: Enable this test again once protos are supported by bazel. + tags = ["manual"], +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md new file mode 100644 index 0000000..21513b9 --- /dev/null +++ b/tensorflow/go/README.md @@ -0,0 +1,103 @@ +# TensorFlow in Go + +Construct and execute TensorFlow graphs in Go. + +[![GoDoc](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go?status.svg)](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go) + +> *WARNING*: The API defined in this package is not stable and can change +> without notice. The same goes for the package path: +> (`github.com/tensorflow/tensorflow/tensorflow/go`). + +## Quickstart + +Refer to [Installing TensorFlow for Go](https://www.tensorflow.org/install/lang_go) + +## Building the TensorFlow C library from source + +If the "Quickstart" instructions above do not work (perhaps the release archives +are not available for your operating system or architecture, or you're using a +different version of CUDA/cuDNN), then the TensorFlow C library must be built +from source. + +### Prerequisites + +- [bazel](https://www.bazel.build/versions/master/docs/install.html) +- Environment to build TensorFlow from source code + ([Linux or macOS](https://www.tensorflow.org/install/source)). If you don't + need GPU support, then try the following: + + ```sh + sudo apt-get install python swig python-numpy # Linux + brew install swig # OS X with homebrew + ``` +- [Protocol buffer compiler (protoc) 3.x](https://github.com/google/protobuf/releases/) + +### Build + +1. Download the source code + + ```sh + go get -d github.com/tensorflow/tensorflow/tensorflow/go + ``` + +2. Build the TensorFlow C library: + + ```sh + cd ${GOPATH}/src/github.com/tensorflow/tensorflow + ./configure + bazel build -c opt //tensorflow:libtensorflow.so + ``` + + This can take a while (tens of minutes, more if also building for GPU). + +3. Make `libtensorflow.so` and `libtensorflow_framework.so` available to the + linker. This can be done by either: + + a. Copying it to a system location, e.g., + + ```sh + sudo cp ${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow/libtensorflow.so /usr/local/lib + sudo cp ${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow/libtensorflow_framework.so /usr/local/lib + ``` + + OR + + b. Setting environment variables: + + ```sh + export LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow + # Linux + export LD_LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow + # OS X + export DYLD_LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow + ``` + +4. Build and test: + + ```sh + go generate github.com/tensorflow/tensorflow/tensorflow/go/op + go test github.com/tensorflow/tensorflow/tensorflow/go + ``` + +### Generate wrapper functions for ops + +Go functions corresponding to TensorFlow operations are generated in `op/wrappers.go`. To regenerate them: + +Prerequisites: +- [Protocol buffer compiler (protoc) 3.x](https://github.com/google/protobuf/releases/) +- The TensorFlow repository under GOPATH + +```sh +go generate github.com/tensorflow/tensorflow/tensorflow/go/op +``` + +## Support + +Use [stackoverflow](http://stackoverflow.com/questions/tagged/tensorflow) and/or +[Github issues](https://github.com/tensorflow/tensorflow/issues). + +## Contributions + +Contributions are welcome. If making any signification changes, probably best to +discuss on a [Github issue](https://github.com/tensorflow/tensorflow/issues) +before investing too much time. Github pull requests are used for contributions. diff --git a/tensorflow/go/android.go b/tensorflow/go/android.go new file mode 100644 index 0000000..3db3ddf --- /dev/null +++ b/tensorflow/go/android.go @@ -0,0 +1,20 @@ +// Copyright 2016 The TensorFlow Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// +build android + +package tensorflow + +// #cgo LDFLAGS: -landroid -llog -lm -lz -ldl +import "C" diff --git a/tensorflow/go/attrs.go b/tensorflow/go/attrs.go new file mode 100644 index 0000000..ed1a1f0 --- /dev/null +++ b/tensorflow/go/attrs.go @@ -0,0 +1,246 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +import "C" +import ( + "fmt" + "unsafe" +) + +// makeCShape converts a shape specified in C.int64_t into a Shape. +func makeCShape(shape []C.int64_t) Shape { + s := Shape{dims: make([]int64, len(shape))} + for i, n := range shape { + s.dims[i] = int64(n) + } + return s +} + +// Attr returns the value of an attribute on op. It returns an error if the +// attribute does not exist. +func (op *Operation) Attr(name string) (interface{}, error) { + cname := C.CString(name) + defer C.free(unsafe.Pointer(cname)) + + status := newStatus() + meta := C.TF_OperationGetAttrMetadata(op.c, cname, status.c) + if err := status.Err(); err != nil { + return nil, err + } + + if meta.is_list == 1 { + return listAttribute(op, cname, meta) + } + return scalarAttribute(op, cname, meta) +} + +func listAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interface{}, error) { + status := newStatus() + + switch meta._type { + case C.TF_ATTR_STRING: + if meta.list_size == 0 { + return []string(nil), nil + } + values := make([]unsafe.Pointer, meta.list_size) + lengths := make([]C.size_t, meta.list_size) + // Add one element in case total_size is zero. + storage := make([]C.char, meta.total_size+1) + C.TF_OperationGetAttrStringList(op.c, cname, &values[0], &lengths[0], C.int(meta.list_size), unsafe.Pointer(&storage[0]), C.size_t(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + list := make([]string, meta.list_size) + for i, val := range values { + length := lengths[i] + list[i] = C.GoStringN((*C.char)(val), C.int(length)) + } + return list, nil + + case C.TF_ATTR_INT: + if meta.list_size == 0 { + return []int64(nil), nil + } + list := make([]C.int64_t, meta.list_size) + C.TF_OperationGetAttrIntList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]int64, meta.list_size) + for i, val := range list { + vals[i] = int64(val) + } + return vals, nil + + case C.TF_ATTR_FLOAT: + if meta.list_size == 0 { + return []float32(nil), nil + } + list := make([]C.float, meta.list_size) + C.TF_OperationGetAttrFloatList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]float32, meta.list_size) + for i, val := range list { + vals[i] = float32(val) + } + return vals, nil + + case C.TF_ATTR_BOOL: + if meta.list_size == 0 { + return []bool(nil), nil + } + list := make([]C.uchar, meta.list_size) + C.TF_OperationGetAttrBoolList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]bool, meta.list_size) + for i, val := range list { + vals[i] = val == 1 + } + return vals, nil + + case C.TF_ATTR_TYPE: + if meta.list_size == 0 { + return []DataType(nil), nil + } + list := make([]C.TF_DataType, meta.list_size) + C.TF_OperationGetAttrTypeList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]DataType, meta.list_size) + for i, val := range list { + vals[i] = DataType(val) + } + return vals, nil + + case C.TF_ATTR_TENSOR: + if meta.list_size == 0 { + return []*Tensor(nil), nil + } + list := make([]*C.TF_Tensor, meta.list_size) + C.TF_OperationGetAttrTensorList(op.c, cname, &list[0], C.int(meta.list_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + vals := make([]*Tensor, meta.list_size) + for i, t := range list { + vals[i] = newTensorFromC(t) + } + return vals, nil + + case C.TF_ATTR_SHAPE: + if meta.list_size == 0 { + return []Shape(nil), nil + } + dims := make([]*C.int64_t, meta.list_size) + numDims := make([]C.int, meta.list_size) + // Add one element in case total_size is zero. + storage := make([]C.int64_t, meta.total_size+1) + C.TF_OperationGetAttrShapeList(op.c, cname, &dims[0], &numDims[0], C.int(meta.list_size), &storage[0], C.int(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + list := make([]Shape, meta.list_size) + for i, dim := range dims { + numDim := numDims[i] + // If the number of dimensions is unknown, default to empty shape. + if numDim < 0 { + continue + } + // A []C.int64_t slice backed by C memory. + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + // Using [1<<27] instead of [1<<30] so it works on 32-bit architecture + slice := (*[1 << 27]C.int64_t)(unsafe.Pointer(dim))[:numDim:numDim] + list[i] = makeCShape(slice) + } + return list, nil + + default: + return nil, fmt.Errorf("list type %v not supported", meta._type) + } +} + +func scalarAttribute(op *Operation, cname *C.char, meta C.TF_AttrMetadata) (interface{}, error) { + status := newStatus() + + switch meta._type { + case C.TF_ATTR_STRING: + if meta.total_size == 0 { + return "", nil + } + v := make([]C.char, meta.total_size) + C.TF_OperationGetAttrString(op.c, cname, unsafe.Pointer(&v[0]), C.size_t(meta.total_size), status.c) + if err := status.Err(); err != nil { + return nil, err + } + return C.GoStringN(&v[0], C.int(meta.total_size)), nil + + case C.TF_ATTR_INT: + var v C.int64_t + C.TF_OperationGetAttrInt(op.c, cname, &v, status.c) + return int64(v), status.Err() + + case C.TF_ATTR_FLOAT: + var v C.float + C.TF_OperationGetAttrFloat(op.c, cname, &v, status.c) + return float32(v), status.Err() + + case C.TF_ATTR_BOOL: + var v C.uchar + C.TF_OperationGetAttrBool(op.c, cname, &v, status.c) + return v == 1, status.Err() + + case C.TF_ATTR_TYPE: + var v C.TF_DataType + C.TF_OperationGetAttrType(op.c, cname, &v, status.c) + return DataType(v), status.Err() + + case C.TF_ATTR_TENSOR: + var v *C.TF_Tensor + C.TF_OperationGetAttrTensor(op.c, cname, &v, status.c) + if err := status.Err(); err != nil { + return nil, err + } + return newTensorFromC(v), nil + + case C.TF_ATTR_SHAPE: + numDims := meta.total_size + // If number of dims is unknown return empty shape to indicate that. + if numDims < 0 { + return Shape{}, nil + } + if numDims == 0 { + return ScalarShape(), nil + } + dims := make([]C.int64_t, numDims) + C.TF_OperationGetAttrShape(op.c, cname, (*C.int64_t)(unsafe.Pointer(&dims[0])), C.int(numDims), status.c) + if err := status.Err(); err != nil { + return nil, err + } + return makeCShape(dims), nil + + default: + return nil, fmt.Errorf("type %v not supported", meta._type) + } +} diff --git a/tensorflow/go/attrs_test.go b/tensorflow/go/attrs_test.go new file mode 100644 index 0000000..ea8af22 --- /dev/null +++ b/tensorflow/go/attrs_test.go @@ -0,0 +1,193 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "reflect" + "testing" +) + +func TestOperationAttrs(t *testing.T) { + g := NewGraph() + + i := 0 + makeConst := func(v interface{}) Output { + op, err := Const(g, fmt.Sprintf("const/%d/%+v", i, v), v) + i++ + if err != nil { + t.Fatal(err) + } + return op + } + + makeTensor := func(v interface{}) *Tensor { + tensor, err := NewTensor(v) + if err != nil { + t.Fatal(err) + } + return tensor + } + + cases := []OpSpec{ + { + Name: "type", + Type: "Placeholder", + Attrs: map[string]interface{}{ + "dtype": Float, + }, + }, + { + Name: "list(float)", + Type: "Bucketize", + Input: []Input{ + makeConst([]float32{1, 2, 3, 4}), + }, + Attrs: map[string]interface{}{ + "boundaries": []float32{0, 1, 2, 3, 4, 5}, + }, + }, + { + Name: "list(float) empty", + Type: "Bucketize", + Input: []Input{ + makeConst([]float32{}), + }, + Attrs: map[string]interface{}{ + "boundaries": []float32(nil), + }, + }, + /* TODO(ashankar): debug this issue and add it back later. + { + Name: "list(type),list(shape)", + Type: "InfeedEnqueueTuple", + Input: []Input{ + OutputList([]Output{ + makeConst(float32(1)), + makeConst([][]int32{{2}}), + }), + }, + Attrs: map[string]interface{}{ + "dtypes": []DataType{Float, Int32}, + "shapes": []Shape{ScalarShape(), MakeShape(1, 1)}, + }, + }, + { + Name: "list(type),list(shape) empty", + Type: "InfeedEnqueueTuple", + Input: []Input{ + OutputList([]Output{ + makeConst([][]int32{{2}}), + }), + }, + Attrs: map[string]interface{}{ + "dtypes": []DataType{Int32}, + "shapes": []Shape(nil), + }, + }, + { + Name: "list(type) empty,string empty,int", + Type: "_XlaSendFromHost", + Input: []Input{ + OutputList([]Output{}), + makeConst(""), + }, + Attrs: map[string]interface{}{ + "Tinputs": []DataType(nil), + "key": "", + "device_ordinal": int64(0), + }, + }, + */ + { + Name: "list(int),int", + Type: "StringToHashBucketStrong", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "num_buckets": int64(2), + "key": []int64{1, 2}, + }, + }, + { + Name: "list(int) empty,int", + Type: "StringToHashBucketStrong", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "num_buckets": int64(2), + "key": ([]int64)(nil), + }, + }, + { + Name: "list(string),type", + Type: "TensorSummary", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "T": String, + "labels": []string{"foo", "bar"}, + }, + }, + { + Name: "list(string) empty,type", + Type: "TensorSummary", + Input: []Input{ + makeConst(""), + }, + Attrs: map[string]interface{}{ + "T": String, + "labels": ([]string)(nil), + }, + }, + { + Name: "tensor", + Type: "Const", + Attrs: map[string]interface{}{ + "dtype": String, + "value": makeTensor("foo"), + }, + }, + } + + for i, spec := range cases { + op, err := g.AddOperation(spec) + if err != nil { + t.Fatal(err) + } + for key, want := range spec.Attrs { + out, err := op.Attr(key) + if err != nil { + t.Fatal(err) + } + if !reflect.DeepEqual(out, want) { + t.Fatalf("%d. %q: Got %#v, wanted %#v", i, key, out, want) + } + wantT, ok := want.(*Tensor) + if ok { + wantVal := wantT.Value() + outVal := out.(*Tensor).Value() + if !reflect.DeepEqual(outVal, wantVal) { + t.Fatalf("%d. %q: Got %#v, wanted %#v", i, key, outVal, wantVal) + } + } + } + } +} diff --git a/tensorflow/go/context.go b/tensorflow/go/context.go new file mode 100644 index 0000000..04f8628 --- /dev/null +++ b/tensorflow/go/context.go @@ -0,0 +1,109 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +// #include "tensorflow/c/eager/c_api.h" +import "C" +import ( + "fmt" + "runtime" +) + +// ContextOptions contains configuration information for a session +type ContextOptions struct { + // Config is a binary-serialized representation of the + // tensorflow.ConfigProto protocol message + // (https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto). + Config []byte + + // Sets the default execution mode + Async bool +} + +// c converts the ContextOptions to the C API's TF_ContextOptions. +// Caller takes ownership of returned object. +func (o *ContextOptions) c() (*C.TFE_ContextOptions, error) { + opt := C.TFE_NewContextOptions() + if o == nil { + return opt, nil + } + + if sz := len(o.Config); sz > 0 { + status := newStatus() + cConfig := C.CBytes(o.Config) + C.TFE_ContextOptionsSetConfig(opt, cConfig, C.size_t(sz), status.c) + C.free(cConfig) + if err := status.Err(); err != nil { + C.TFE_DeleteContextOptions(opt) + return nil, fmt.Errorf("invalid ContextOptions.Config: %v", err) + } + } + + var async uint8 + if o.Async { + async = 1 + } + C.TFE_ContextOptionsSetAsync(opt, C.uchar(async)) + + return opt, nil +} + +// Context for executing operations eagerly. +// +// A Context allows operations to be executed immediately. It encapsulates +// information such as the available devices, resource manager etc. It also +// allows the user to configure execution using a ConfigProto, as they can +// configure a Session when executing a Graph. +type Context struct { + c *C.TFE_Context +} + +// NewContext creates a new context for eager execution. +// options may be nil to use the default options. +func NewContext(options *ContextOptions) (*Context, error) { + status := newStatus() + cOpt, err := options.c() + if err != nil { + return nil, err + } + defer C.TFE_DeleteContextOptions(cOpt) + cContext := C.TFE_NewContext(cOpt, status.c) + if err := status.Err(); err != nil { + return nil, err + } + + c := &Context{c: cContext} + runtime.SetFinalizer(c, (*Context).finalizer) + return c, nil +} + +func (c *Context) finalizer() { + C.TFE_DeleteContext(c.c) +} + +// ListDevices returns the list of devices associated with a Context. +func (c *Context) ListDevices() ([]Device, error) { + status := newStatus() + devicesList := C.TFE_ContextListDevices(c.c, status.c) + if err := status.Err(); err != nil { + return nil, fmt.Errorf("SessionListDevices() failed: %v", err) + } + defer C.TF_DeleteDeviceList(devicesList) + return deviceSliceFromDeviceList(devicesList) +} diff --git a/tensorflow/go/context_test.go b/tensorflow/go/context_test.go new file mode 100644 index 0000000..ce4005d --- /dev/null +++ b/tensorflow/go/context_test.go @@ -0,0 +1,57 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "testing" +) + +func TestContextConfigSetAsync(t *testing.T) { + tests := []bool{false, true} + for _, test := range tests { + t.Run(fmt.Sprint(test), func(t *testing.T) { + opt := &ContextOptions{Async: test} + if _, err := NewContext(opt); err != nil { + t.Fatal(err) + } + }) + } +} + +func TestContextConfigListDevices(t *testing.T) { + c, err := NewContext(nil) + if err != nil { + t.Fatal(err) + } + devs, err := c.ListDevices() + if err != nil { + t.Fatal(err) + } + if len(devs) < 1 { + t.Fatalf("No devices found using ListDevices()") + } + foundCPUDevice := false + for _, d := range devs { + if d.Type == "CPU" { + foundCPUDevice = true + } + } + if !foundCPUDevice { + t.Error("Failed to find CPU device using ListDevices()") + } +} diff --git a/tensorflow/go/core/framework/allocation_description_go_proto/allocation_description.pb.go b/tensorflow/go/core/framework/allocation_description_go_proto/allocation_description.pb.go new file mode 100644 index 0000000..25fa8f7 --- /dev/null +++ b/tensorflow/go/core/framework/allocation_description_go_proto/allocation_description.pb.go @@ -0,0 +1,136 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/allocation_description.proto + +package allocation_description_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type AllocationDescription struct { + // Total number of bytes requested + RequestedBytes int64 `protobuf:"varint,1,opt,name=requested_bytes,json=requestedBytes,proto3" json:"requested_bytes,omitempty"` + // Total number of bytes allocated if known + AllocatedBytes int64 `protobuf:"varint,2,opt,name=allocated_bytes,json=allocatedBytes,proto3" json:"allocated_bytes,omitempty"` + // Name of the allocator used + AllocatorName string `protobuf:"bytes,3,opt,name=allocator_name,json=allocatorName,proto3" json:"allocator_name,omitempty"` + // Identifier of the allocated buffer if known + AllocationId int64 `protobuf:"varint,4,opt,name=allocation_id,json=allocationId,proto3" json:"allocation_id,omitempty"` + // Set if this tensor only has one remaining reference + HasSingleReference bool `protobuf:"varint,5,opt,name=has_single_reference,json=hasSingleReference,proto3" json:"has_single_reference,omitempty"` + // Address of the allocation. + Ptr uint64 `protobuf:"varint,6,opt,name=ptr,proto3" json:"ptr,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AllocationDescription) Reset() { *m = AllocationDescription{} } +func (m *AllocationDescription) String() string { return proto.CompactTextString(m) } +func (*AllocationDescription) ProtoMessage() {} +func (*AllocationDescription) Descriptor() ([]byte, []int) { + return fileDescriptor_1254702e9f0c7d2f, []int{0} +} + +func (m *AllocationDescription) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AllocationDescription.Unmarshal(m, b) +} +func (m *AllocationDescription) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AllocationDescription.Marshal(b, m, deterministic) +} +func (m *AllocationDescription) XXX_Merge(src proto.Message) { + xxx_messageInfo_AllocationDescription.Merge(m, src) +} +func (m *AllocationDescription) XXX_Size() int { + return xxx_messageInfo_AllocationDescription.Size(m) +} +func (m *AllocationDescription) XXX_DiscardUnknown() { + xxx_messageInfo_AllocationDescription.DiscardUnknown(m) +} + +var xxx_messageInfo_AllocationDescription proto.InternalMessageInfo + +func (m *AllocationDescription) GetRequestedBytes() int64 { + if m != nil { + return m.RequestedBytes + } + return 0 +} + +func (m *AllocationDescription) GetAllocatedBytes() int64 { + if m != nil { + return m.AllocatedBytes + } + return 0 +} + +func (m *AllocationDescription) GetAllocatorName() string { + if m != nil { + return m.AllocatorName + } + return "" +} + +func (m *AllocationDescription) GetAllocationId() int64 { + if m != nil { + return m.AllocationId + } + return 0 +} + +func (m *AllocationDescription) GetHasSingleReference() bool { + if m != nil { + return m.HasSingleReference + } + return false +} + +func (m *AllocationDescription) GetPtr() uint64 { + if m != nil { + return m.Ptr + } + return 0 +} + +func init() { + proto.RegisterType((*AllocationDescription)(nil), "tensorflow.AllocationDescription") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/allocation_description.proto", fileDescriptor_1254702e9f0c7d2f) +} + +var fileDescriptor_1254702e9f0c7d2f = []byte{ + // 286 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x8c, 0x91, 0x4d, 0x4b, 0xc3, 0x40, + 0x10, 0x86, 0x59, 0x5b, 0x8b, 0x2e, 0x56, 0x65, 0x51, 0x58, 0xf0, 0x12, 0x14, 0x31, 0xa7, 0x44, + 0x10, 0xbc, 0x1b, 0xbc, 0x78, 0x91, 0x12, 0x6f, 0x82, 0x2c, 0x9b, 0x64, 0xf2, 0x81, 0x49, 0x26, + 0xce, 0x6e, 0x29, 0xfe, 0x16, 0xff, 0xa8, 0xde, 0x24, 0xa9, 0xdd, 0xf4, 0xd0, 0x83, 0xb7, 0x97, + 0x67, 0x9e, 0xfd, 0xe0, 0x1d, 0x7e, 0x6f, 0xa1, 0x35, 0x48, 0x79, 0x8d, 0xab, 0x30, 0x45, 0x82, + 0x30, 0x27, 0xdd, 0xc0, 0x0a, 0xe9, 0x3d, 0xd4, 0x75, 0x8d, 0xa9, 0xb6, 0x15, 0xb6, 0x2a, 0x03, + 0x93, 0x52, 0xd5, 0xf5, 0x39, 0xe8, 0x08, 0x2d, 0x0a, 0x3e, 0x9e, 0xbb, 0xfc, 0x61, 0xfc, 0xfc, + 0xc1, 0xc9, 0x8f, 0xa3, 0x2b, 0x6e, 0xf8, 0x09, 0xc1, 0xc7, 0x12, 0x8c, 0x85, 0x4c, 0x25, 0x9f, + 0x16, 0x8c, 0x64, 0x1e, 0xf3, 0x27, 0xf1, 0xb1, 0xc3, 0x51, 0x4f, 0x7b, 0xf1, 0xef, 0x39, 0x27, + 0xee, 0xad, 0x45, 0x87, 0xd7, 0xe2, 0x35, 0xdf, 0x10, 0x24, 0xd5, 0xea, 0x06, 0xe4, 0xc4, 0x63, + 0xfe, 0x61, 0x3c, 0x77, 0xf4, 0x59, 0x37, 0x20, 0xae, 0xf8, 0x7c, 0xeb, 0xfb, 0x55, 0x26, 0xa7, + 0xc3, 0x6d, 0x47, 0x23, 0x7c, 0xca, 0xc4, 0x2d, 0x3f, 0x2b, 0xb5, 0x51, 0xa6, 0x6a, 0x8b, 0x1a, + 0x14, 0x41, 0x0e, 0x04, 0x6d, 0x0a, 0x72, 0xdf, 0x63, 0xfe, 0x41, 0x2c, 0x4a, 0x6d, 0x5e, 0x86, + 0x51, 0xbc, 0x99, 0x88, 0x53, 0x3e, 0xe9, 0x2c, 0xc9, 0x99, 0xc7, 0xfc, 0x69, 0xdc, 0xc7, 0xe8, + 0x8b, 0x71, 0x89, 0x54, 0x04, 0x63, 0x1d, 0x81, 0x6b, 0x30, 0xba, 0xd8, 0xd9, 0xca, 0xa2, 0x2f, + 0xd0, 0x2c, 0xd8, 0xeb, 0x5b, 0x51, 0xd9, 0x72, 0x99, 0x04, 0x29, 0x36, 0xe1, 0xd6, 0x1a, 0x76, + 0xc7, 0x02, 0xff, 0xb7, 0x1f, 0x55, 0xa0, 0x1a, 0x56, 0xf4, 0xcd, 0x58, 0x32, 0x1b, 0xd2, 0xdd, + 0x6f, 0x00, 0x00, 0x00, 0xff, 0xff, 0x19, 0xc0, 0xbc, 0x20, 0xe6, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/api_def_go_proto/api_def.pb.go b/tensorflow/go/core/framework/api_def_go_proto/api_def.pb.go new file mode 100644 index 0000000..98ed781 --- /dev/null +++ b/tensorflow/go/core/framework/api_def_go_proto/api_def.pb.go @@ -0,0 +1,524 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/api_def.proto + +package api_def_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type ApiDef_Visibility int32 + +const ( + // Normally this is "VISIBLE" unless you are inheriting a + // different value from another ApiDef. + ApiDef_DEFAULT_VISIBILITY ApiDef_Visibility = 0 + // Publicly visible in the API. + ApiDef_VISIBLE ApiDef_Visibility = 1 + // Do not include this op in the generated API. If visibility is + // set to 'SKIP', other fields are ignored for this op. + ApiDef_SKIP ApiDef_Visibility = 2 + // Hide this op by putting it into an internal namespace (or whatever + // is appropriate in the target language). + ApiDef_HIDDEN ApiDef_Visibility = 3 +) + +var ApiDef_Visibility_name = map[int32]string{ + 0: "DEFAULT_VISIBILITY", + 1: "VISIBLE", + 2: "SKIP", + 3: "HIDDEN", +} + +var ApiDef_Visibility_value = map[string]int32{ + "DEFAULT_VISIBILITY": 0, + "VISIBLE": 1, + "SKIP": 2, + "HIDDEN": 3, +} + +func (x ApiDef_Visibility) String() string { + return proto.EnumName(ApiDef_Visibility_name, int32(x)) +} + +func (ApiDef_Visibility) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_00a850add58b816a, []int{0, 0} +} + +// Used to specify and override the default API & behavior in the +// generated code for client languages, from what you would get from +// the OpDef alone. There will be a set of ApiDefs that are common +// to all client languages, and another set per client language. +// The per-client-language ApiDefs will inherit values from the +// common ApiDefs which it can either replace or modify. +// +// We separate the API definition from the OpDef so we can evolve the +// API while remaining backwards compatible when interpretting old +// graphs. Overrides go in an "api_def.pbtxt" file with a text-format +// ApiDefs message. +// +// WARNING: Be *very* careful changing the API for any existing op -- +// you can change the semantics of existing code. These changes may +// need to wait until a major release of TensorFlow to avoid breaking +// our compatibility promises. +type ApiDef struct { + // Name of the op (in the OpDef) to specify the API for. + GraphOpName string `protobuf:"bytes,1,opt,name=graph_op_name,json=graphOpName,proto3" json:"graph_op_name,omitempty"` + // If this op is deprecated, set deprecation message to the message + // that should be logged when this op is used. + // The message should indicate alternative op to use, if any. + DeprecationMessage string `protobuf:"bytes,12,opt,name=deprecation_message,json=deprecationMessage,proto3" json:"deprecation_message,omitempty"` + // Major version when the op will be deleted. For e.g. set this + // value to 2 if op API should be removed in TensorFlow 2.0 and + // deprecated in versions before that. + DeprecationVersion int32 `protobuf:"varint,13,opt,name=deprecation_version,json=deprecationVersion,proto3" json:"deprecation_version,omitempty"` + Visibility ApiDef_Visibility `protobuf:"varint,2,opt,name=visibility,proto3,enum=tensorflow.ApiDef_Visibility" json:"visibility,omitempty"` + Endpoint []*ApiDef_Endpoint `protobuf:"bytes,3,rep,name=endpoint,proto3" json:"endpoint,omitempty"` + InArg []*ApiDef_Arg `protobuf:"bytes,4,rep,name=in_arg,json=inArg,proto3" json:"in_arg,omitempty"` + OutArg []*ApiDef_Arg `protobuf:"bytes,5,rep,name=out_arg,json=outArg,proto3" json:"out_arg,omitempty"` + // List of original in_arg names to specify new argument order. + // Length of arg_order should be either empty to keep current order + // or match size of in_arg. + ArgOrder []string `protobuf:"bytes,11,rep,name=arg_order,json=argOrder,proto3" json:"arg_order,omitempty"` + Attr []*ApiDef_Attr `protobuf:"bytes,6,rep,name=attr,proto3" json:"attr,omitempty"` + // One-line human-readable description of what the Op does. + Summary string `protobuf:"bytes,7,opt,name=summary,proto3" json:"summary,omitempty"` + // Additional, longer human-readable description of what the Op does. + Description string `protobuf:"bytes,8,opt,name=description,proto3" json:"description,omitempty"` + // Modify an existing/inherited description by adding text to the beginning + // or end. + DescriptionPrefix string `protobuf:"bytes,9,opt,name=description_prefix,json=descriptionPrefix,proto3" json:"description_prefix,omitempty"` + DescriptionSuffix string `protobuf:"bytes,10,opt,name=description_suffix,json=descriptionSuffix,proto3" json:"description_suffix,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ApiDef) Reset() { *m = ApiDef{} } +func (m *ApiDef) String() string { return proto.CompactTextString(m) } +func (*ApiDef) ProtoMessage() {} +func (*ApiDef) Descriptor() ([]byte, []int) { + return fileDescriptor_00a850add58b816a, []int{0} +} + +func (m *ApiDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ApiDef.Unmarshal(m, b) +} +func (m *ApiDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ApiDef.Marshal(b, m, deterministic) +} +func (m *ApiDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_ApiDef.Merge(m, src) +} +func (m *ApiDef) XXX_Size() int { + return xxx_messageInfo_ApiDef.Size(m) +} +func (m *ApiDef) XXX_DiscardUnknown() { + xxx_messageInfo_ApiDef.DiscardUnknown(m) +} + +var xxx_messageInfo_ApiDef proto.InternalMessageInfo + +func (m *ApiDef) GetGraphOpName() string { + if m != nil { + return m.GraphOpName + } + return "" +} + +func (m *ApiDef) GetDeprecationMessage() string { + if m != nil { + return m.DeprecationMessage + } + return "" +} + +func (m *ApiDef) GetDeprecationVersion() int32 { + if m != nil { + return m.DeprecationVersion + } + return 0 +} + +func (m *ApiDef) GetVisibility() ApiDef_Visibility { + if m != nil { + return m.Visibility + } + return ApiDef_DEFAULT_VISIBILITY +} + +func (m *ApiDef) GetEndpoint() []*ApiDef_Endpoint { + if m != nil { + return m.Endpoint + } + return nil +} + +func (m *ApiDef) GetInArg() []*ApiDef_Arg { + if m != nil { + return m.InArg + } + return nil +} + +func (m *ApiDef) GetOutArg() []*ApiDef_Arg { + if m != nil { + return m.OutArg + } + return nil +} + +func (m *ApiDef) GetArgOrder() []string { + if m != nil { + return m.ArgOrder + } + return nil +} + +func (m *ApiDef) GetAttr() []*ApiDef_Attr { + if m != nil { + return m.Attr + } + return nil +} + +func (m *ApiDef) GetSummary() string { + if m != nil { + return m.Summary + } + return "" +} + +func (m *ApiDef) GetDescription() string { + if m != nil { + return m.Description + } + return "" +} + +func (m *ApiDef) GetDescriptionPrefix() string { + if m != nil { + return m.DescriptionPrefix + } + return "" +} + +func (m *ApiDef) GetDescriptionSuffix() string { + if m != nil { + return m.DescriptionSuffix + } + return "" +} + +// If you specify any endpoint, this will replace all of the +// inherited endpoints. The first endpoint should be the +// "canonical" endpoint, and should not be deprecated (unless all +// endpoints are deprecated). +type ApiDef_Endpoint struct { + // Name should be either like "CamelCaseName" or + // "Package.CamelCaseName". Client-language-specific ApiDefs may + // use a snake_case convention instead of CamelCase. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Set if this endpoint is deprecated. If set to true, a message suggesting + // to use a non-deprecated endpoint instead will be printed. If all + // endpoints are deprecated, set deprecation_message in ApiDef instead. + Deprecated bool `protobuf:"varint,3,opt,name=deprecated,proto3" json:"deprecated,omitempty"` + // Major version when an endpoint will be deleted. For e.g. set this + // value to 2 if endpoint should be removed in TensorFlow 2.0 and + // deprecated in versions before that. + DeprecationVersion int32 `protobuf:"varint,4,opt,name=deprecation_version,json=deprecationVersion,proto3" json:"deprecation_version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ApiDef_Endpoint) Reset() { *m = ApiDef_Endpoint{} } +func (m *ApiDef_Endpoint) String() string { return proto.CompactTextString(m) } +func (*ApiDef_Endpoint) ProtoMessage() {} +func (*ApiDef_Endpoint) Descriptor() ([]byte, []int) { + return fileDescriptor_00a850add58b816a, []int{0, 0} +} + +func (m *ApiDef_Endpoint) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ApiDef_Endpoint.Unmarshal(m, b) +} +func (m *ApiDef_Endpoint) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ApiDef_Endpoint.Marshal(b, m, deterministic) +} +func (m *ApiDef_Endpoint) XXX_Merge(src proto.Message) { + xxx_messageInfo_ApiDef_Endpoint.Merge(m, src) +} +func (m *ApiDef_Endpoint) XXX_Size() int { + return xxx_messageInfo_ApiDef_Endpoint.Size(m) +} +func (m *ApiDef_Endpoint) XXX_DiscardUnknown() { + xxx_messageInfo_ApiDef_Endpoint.DiscardUnknown(m) +} + +var xxx_messageInfo_ApiDef_Endpoint proto.InternalMessageInfo + +func (m *ApiDef_Endpoint) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *ApiDef_Endpoint) GetDeprecated() bool { + if m != nil { + return m.Deprecated + } + return false +} + +func (m *ApiDef_Endpoint) GetDeprecationVersion() int32 { + if m != nil { + return m.DeprecationVersion + } + return 0 +} + +type ApiDef_Arg struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Change the name used to access this arg in the API from what + // is used in the GraphDef. Note that these names in `backticks` + // will also be replaced in the summary & description fields. + RenameTo string `protobuf:"bytes,2,opt,name=rename_to,json=renameTo,proto3" json:"rename_to,omitempty"` + // Note: this will replace any inherited arg doc. There is no + // current way of modifying arg descriptions (other than replacing + // them entirely) as can be done with op descriptions. + Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ApiDef_Arg) Reset() { *m = ApiDef_Arg{} } +func (m *ApiDef_Arg) String() string { return proto.CompactTextString(m) } +func (*ApiDef_Arg) ProtoMessage() {} +func (*ApiDef_Arg) Descriptor() ([]byte, []int) { + return fileDescriptor_00a850add58b816a, []int{0, 1} +} + +func (m *ApiDef_Arg) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ApiDef_Arg.Unmarshal(m, b) +} +func (m *ApiDef_Arg) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ApiDef_Arg.Marshal(b, m, deterministic) +} +func (m *ApiDef_Arg) XXX_Merge(src proto.Message) { + xxx_messageInfo_ApiDef_Arg.Merge(m, src) +} +func (m *ApiDef_Arg) XXX_Size() int { + return xxx_messageInfo_ApiDef_Arg.Size(m) +} +func (m *ApiDef_Arg) XXX_DiscardUnknown() { + xxx_messageInfo_ApiDef_Arg.DiscardUnknown(m) +} + +var xxx_messageInfo_ApiDef_Arg proto.InternalMessageInfo + +func (m *ApiDef_Arg) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *ApiDef_Arg) GetRenameTo() string { + if m != nil { + return m.RenameTo + } + return "" +} + +func (m *ApiDef_Arg) GetDescription() string { + if m != nil { + return m.Description + } + return "" +} + +// Description of the graph-construction-time configuration of this +// Op. That is to say, this describes the attr fields that will +// be specified in the NodeDef. +type ApiDef_Attr struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Change the name used to access this attr in the API from what + // is used in the GraphDef. Note that these names in `backticks` + // will also be replaced in the summary & description fields. + RenameTo string `protobuf:"bytes,2,opt,name=rename_to,json=renameTo,proto3" json:"rename_to,omitempty"` + // Specify a new default value to use for this attr. This default + // will be used when creating new graphs, as opposed to the + // default in the OpDef, which will be used when interpreting old + // GraphDefs. + DefaultValue *attr_value_go_proto.AttrValue `protobuf:"bytes,3,opt,name=default_value,json=defaultValue,proto3" json:"default_value,omitempty"` + // Note: this will replace any inherited attr doc, there is no current + // way of modifying attr descriptions as can be done with op descriptions. + Description string `protobuf:"bytes,4,opt,name=description,proto3" json:"description,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ApiDef_Attr) Reset() { *m = ApiDef_Attr{} } +func (m *ApiDef_Attr) String() string { return proto.CompactTextString(m) } +func (*ApiDef_Attr) ProtoMessage() {} +func (*ApiDef_Attr) Descriptor() ([]byte, []int) { + return fileDescriptor_00a850add58b816a, []int{0, 2} +} + +func (m *ApiDef_Attr) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ApiDef_Attr.Unmarshal(m, b) +} +func (m *ApiDef_Attr) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ApiDef_Attr.Marshal(b, m, deterministic) +} +func (m *ApiDef_Attr) XXX_Merge(src proto.Message) { + xxx_messageInfo_ApiDef_Attr.Merge(m, src) +} +func (m *ApiDef_Attr) XXX_Size() int { + return xxx_messageInfo_ApiDef_Attr.Size(m) +} +func (m *ApiDef_Attr) XXX_DiscardUnknown() { + xxx_messageInfo_ApiDef_Attr.DiscardUnknown(m) +} + +var xxx_messageInfo_ApiDef_Attr proto.InternalMessageInfo + +func (m *ApiDef_Attr) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *ApiDef_Attr) GetRenameTo() string { + if m != nil { + return m.RenameTo + } + return "" +} + +func (m *ApiDef_Attr) GetDefaultValue() *attr_value_go_proto.AttrValue { + if m != nil { + return m.DefaultValue + } + return nil +} + +func (m *ApiDef_Attr) GetDescription() string { + if m != nil { + return m.Description + } + return "" +} + +type ApiDefs struct { + Op []*ApiDef `protobuf:"bytes,1,rep,name=op,proto3" json:"op,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ApiDefs) Reset() { *m = ApiDefs{} } +func (m *ApiDefs) String() string { return proto.CompactTextString(m) } +func (*ApiDefs) ProtoMessage() {} +func (*ApiDefs) Descriptor() ([]byte, []int) { + return fileDescriptor_00a850add58b816a, []int{1} +} + +func (m *ApiDefs) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ApiDefs.Unmarshal(m, b) +} +func (m *ApiDefs) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ApiDefs.Marshal(b, m, deterministic) +} +func (m *ApiDefs) XXX_Merge(src proto.Message) { + xxx_messageInfo_ApiDefs.Merge(m, src) +} +func (m *ApiDefs) XXX_Size() int { + return xxx_messageInfo_ApiDefs.Size(m) +} +func (m *ApiDefs) XXX_DiscardUnknown() { + xxx_messageInfo_ApiDefs.DiscardUnknown(m) +} + +var xxx_messageInfo_ApiDefs proto.InternalMessageInfo + +func (m *ApiDefs) GetOp() []*ApiDef { + if m != nil { + return m.Op + } + return nil +} + +func init() { + proto.RegisterEnum("tensorflow.ApiDef_Visibility", ApiDef_Visibility_name, ApiDef_Visibility_value) + proto.RegisterType((*ApiDef)(nil), "tensorflow.ApiDef") + proto.RegisterType((*ApiDef_Endpoint)(nil), "tensorflow.ApiDef.Endpoint") + proto.RegisterType((*ApiDef_Arg)(nil), "tensorflow.ApiDef.Arg") + proto.RegisterType((*ApiDef_Attr)(nil), "tensorflow.ApiDef.Attr") + proto.RegisterType((*ApiDefs)(nil), "tensorflow.ApiDefs") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/api_def.proto", fileDescriptor_00a850add58b816a) +} + +var fileDescriptor_00a850add58b816a = []byte{ + // 612 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x54, 0x4d, 0x6f, 0xd3, 0x40, + 0x10, 0xc5, 0xb1, 0x9b, 0x38, 0x93, 0x16, 0x85, 0x45, 0x94, 0x55, 0x2a, 0x90, 0x95, 0x0b, 0x11, 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0xfe, 0xd1, 0x1f, 0x8d, 0xc6, 0x87, 0x6d, + 0xbb, 0xbb, 0x0b, 0x8d, 0xd2, 0x5a, 0x19, 0xea, 0x42, 0x4d, 0x48, 0x6c, 0x19, 0xef, 0xa1, 0x65, + 0xef, 0x05, 0x35, 0x21, 0xf7, 0x7f, 0x01, 0x16, 0x2a, 0xae, 0x82, 0x37, 0x47, 0x65, 0x7f, 0xb3, + 0xe4, 0x4d, 0x8a, 0x83, 0x92, 0x4d, 0xac, 0xef, 0x87, 0x31, 0xd7, 0x67, 0xf9, 0x69, 0x7f, 0x26, + 0x92, 0x41, 0xe5, 0x14, 0xad, 0x0e, 0x63, 0xb1, 0xe6, 0xb0, 0x91, 0x58, 0x10, 0x73, 0xa3, 0xfe, + 0x5a, 0xd6, 0x69, 0xdd, 0x44, 0xaf, 0xff, 0x05, 0x00, 0x00, 0xff, 0xff, 0x4c, 0xda, 0xb1, 0x32, + 0x10, 0x05, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/attr_value_go_proto/attr_value.pb.go b/tensorflow/go/core/framework/attr_value_go_proto/attr_value.pb.go new file mode 100644 index 0000000..5111ce6 --- /dev/null +++ b/tensorflow/go/core/framework/attr_value_go_proto/attr_value.pb.go @@ -0,0 +1,420 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/attr_value.proto + +package attr_value_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing the value for an attr used to configure an Op. +// Comment indicates the corresponding attr type. Only the field matching the +// attr type may be filled. +type AttrValue struct { + // Types that are valid to be assigned to Value: + // *AttrValue_S + // *AttrValue_I + // *AttrValue_F + // *AttrValue_B + // *AttrValue_Type + // *AttrValue_Shape + // *AttrValue_Tensor + // *AttrValue_List + // *AttrValue_Func + // *AttrValue_Placeholder + Value isAttrValue_Value `protobuf_oneof:"value"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AttrValue) Reset() { *m = AttrValue{} } +func (m *AttrValue) String() string { return proto.CompactTextString(m) } +func (*AttrValue) ProtoMessage() {} +func (*AttrValue) Descriptor() ([]byte, []int) { + return fileDescriptor_06e758bf81984406, []int{0} +} + +func (m *AttrValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AttrValue.Unmarshal(m, b) +} +func (m *AttrValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AttrValue.Marshal(b, m, deterministic) +} +func (m *AttrValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_AttrValue.Merge(m, src) +} +func (m *AttrValue) XXX_Size() int { + return xxx_messageInfo_AttrValue.Size(m) +} +func (m *AttrValue) XXX_DiscardUnknown() { + xxx_messageInfo_AttrValue.DiscardUnknown(m) +} + +var xxx_messageInfo_AttrValue proto.InternalMessageInfo + +type isAttrValue_Value interface { + isAttrValue_Value() +} + +type AttrValue_S struct { + S []byte `protobuf:"bytes,2,opt,name=s,proto3,oneof"` +} + +type AttrValue_I struct { + I int64 `protobuf:"varint,3,opt,name=i,proto3,oneof"` +} + +type AttrValue_F struct { + F float32 `protobuf:"fixed32,4,opt,name=f,proto3,oneof"` +} + +type AttrValue_B struct { + B bool `protobuf:"varint,5,opt,name=b,proto3,oneof"` +} + +type AttrValue_Type struct { + Type types_go_proto.DataType `protobuf:"varint,6,opt,name=type,proto3,enum=tensorflow.DataType,oneof"` +} + +type AttrValue_Shape struct { + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,7,opt,name=shape,proto3,oneof"` +} + +type AttrValue_Tensor struct { + Tensor *tensor_go_proto.TensorProto `protobuf:"bytes,8,opt,name=tensor,proto3,oneof"` +} + +type AttrValue_List struct { + List *AttrValue_ListValue `protobuf:"bytes,1,opt,name=list,proto3,oneof"` +} + +type AttrValue_Func struct { + Func *NameAttrList `protobuf:"bytes,10,opt,name=func,proto3,oneof"` +} + +type AttrValue_Placeholder struct { + Placeholder string `protobuf:"bytes,9,opt,name=placeholder,proto3,oneof"` +} + +func (*AttrValue_S) isAttrValue_Value() {} + +func (*AttrValue_I) isAttrValue_Value() {} + +func (*AttrValue_F) isAttrValue_Value() {} + +func (*AttrValue_B) isAttrValue_Value() {} + +func (*AttrValue_Type) isAttrValue_Value() {} + +func (*AttrValue_Shape) isAttrValue_Value() {} + +func (*AttrValue_Tensor) isAttrValue_Value() {} + +func (*AttrValue_List) isAttrValue_Value() {} + +func (*AttrValue_Func) isAttrValue_Value() {} + +func (*AttrValue_Placeholder) isAttrValue_Value() {} + +func (m *AttrValue) GetValue() isAttrValue_Value { + if m != nil { + return m.Value + } + return nil +} + +func (m *AttrValue) GetS() []byte { + if x, ok := m.GetValue().(*AttrValue_S); ok { + return x.S + } + return nil +} + +func (m *AttrValue) GetI() int64 { + if x, ok := m.GetValue().(*AttrValue_I); ok { + return x.I + } + return 0 +} + +func (m *AttrValue) GetF() float32 { + if x, ok := m.GetValue().(*AttrValue_F); ok { + return x.F + } + return 0 +} + +func (m *AttrValue) GetB() bool { + if x, ok := m.GetValue().(*AttrValue_B); ok { + return x.B + } + return false +} + +func (m *AttrValue) GetType() types_go_proto.DataType { + if x, ok := m.GetValue().(*AttrValue_Type); ok { + return x.Type + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *AttrValue) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if x, ok := m.GetValue().(*AttrValue_Shape); ok { + return x.Shape + } + return nil +} + +func (m *AttrValue) GetTensor() *tensor_go_proto.TensorProto { + if x, ok := m.GetValue().(*AttrValue_Tensor); ok { + return x.Tensor + } + return nil +} + +func (m *AttrValue) GetList() *AttrValue_ListValue { + if x, ok := m.GetValue().(*AttrValue_List); ok { + return x.List + } + return nil +} + +func (m *AttrValue) GetFunc() *NameAttrList { + if x, ok := m.GetValue().(*AttrValue_Func); ok { + return x.Func + } + return nil +} + +func (m *AttrValue) GetPlaceholder() string { + if x, ok := m.GetValue().(*AttrValue_Placeholder); ok { + return x.Placeholder + } + return "" +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*AttrValue) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*AttrValue_S)(nil), + (*AttrValue_I)(nil), + (*AttrValue_F)(nil), + (*AttrValue_B)(nil), + (*AttrValue_Type)(nil), + (*AttrValue_Shape)(nil), + (*AttrValue_Tensor)(nil), + (*AttrValue_List)(nil), + (*AttrValue_Func)(nil), + (*AttrValue_Placeholder)(nil), + } +} + +// LINT.IfChange +type AttrValue_ListValue struct { + S [][]byte `protobuf:"bytes,2,rep,name=s,proto3" json:"s,omitempty"` + I []int64 `protobuf:"varint,3,rep,packed,name=i,proto3" json:"i,omitempty"` + F []float32 `protobuf:"fixed32,4,rep,packed,name=f,proto3" json:"f,omitempty"` + B []bool `protobuf:"varint,5,rep,packed,name=b,proto3" json:"b,omitempty"` + Type []types_go_proto.DataType `protobuf:"varint,6,rep,packed,name=type,proto3,enum=tensorflow.DataType" json:"type,omitempty"` + Shape []*tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,7,rep,name=shape,proto3" json:"shape,omitempty"` + Tensor []*tensor_go_proto.TensorProto `protobuf:"bytes,8,rep,name=tensor,proto3" json:"tensor,omitempty"` + Func []*NameAttrList `protobuf:"bytes,9,rep,name=func,proto3" json:"func,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AttrValue_ListValue) Reset() { *m = AttrValue_ListValue{} } +func (m *AttrValue_ListValue) String() string { return proto.CompactTextString(m) } +func (*AttrValue_ListValue) ProtoMessage() {} +func (*AttrValue_ListValue) Descriptor() ([]byte, []int) { + return fileDescriptor_06e758bf81984406, []int{0, 0} +} + +func (m *AttrValue_ListValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AttrValue_ListValue.Unmarshal(m, b) +} +func (m *AttrValue_ListValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AttrValue_ListValue.Marshal(b, m, deterministic) +} +func (m *AttrValue_ListValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_AttrValue_ListValue.Merge(m, src) +} +func (m *AttrValue_ListValue) XXX_Size() int { + return xxx_messageInfo_AttrValue_ListValue.Size(m) +} +func (m *AttrValue_ListValue) XXX_DiscardUnknown() { + xxx_messageInfo_AttrValue_ListValue.DiscardUnknown(m) +} + +var xxx_messageInfo_AttrValue_ListValue proto.InternalMessageInfo + +func (m *AttrValue_ListValue) GetS() [][]byte { + if m != nil { + return m.S + } + return nil +} + +func (m *AttrValue_ListValue) GetI() []int64 { + if m != nil { + return m.I + } + return nil +} + +func (m *AttrValue_ListValue) GetF() []float32 { + if m != nil { + return m.F + } + return nil +} + +func (m *AttrValue_ListValue) GetB() []bool { + if m != nil { + return m.B + } + return nil +} + +func (m *AttrValue_ListValue) GetType() []types_go_proto.DataType { + if m != nil { + return m.Type + } + return nil +} + +func (m *AttrValue_ListValue) GetShape() []*tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *AttrValue_ListValue) GetTensor() []*tensor_go_proto.TensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +func (m *AttrValue_ListValue) GetFunc() []*NameAttrList { + if m != nil { + return m.Func + } + return nil +} + +// A list of attr names and their values. The whole list is attached +// with a string name. E.g., MatMul[T=float]. +type NameAttrList struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Attr map[string]*AttrValue `protobuf:"bytes,2,rep,name=attr,proto3" json:"attr,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NameAttrList) Reset() { *m = NameAttrList{} } +func (m *NameAttrList) String() string { return proto.CompactTextString(m) } +func (*NameAttrList) ProtoMessage() {} +func (*NameAttrList) Descriptor() ([]byte, []int) { + return fileDescriptor_06e758bf81984406, []int{1} +} + +func (m *NameAttrList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NameAttrList.Unmarshal(m, b) +} +func (m *NameAttrList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NameAttrList.Marshal(b, m, deterministic) +} +func (m *NameAttrList) XXX_Merge(src proto.Message) { + xxx_messageInfo_NameAttrList.Merge(m, src) +} +func (m *NameAttrList) XXX_Size() int { + return xxx_messageInfo_NameAttrList.Size(m) +} +func (m *NameAttrList) XXX_DiscardUnknown() { + xxx_messageInfo_NameAttrList.DiscardUnknown(m) +} + +var xxx_messageInfo_NameAttrList proto.InternalMessageInfo + +func (m *NameAttrList) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *NameAttrList) GetAttr() map[string]*AttrValue { + if m != nil { + return m.Attr + } + return nil +} + +func init() { + proto.RegisterType((*AttrValue)(nil), "tensorflow.AttrValue") + proto.RegisterType((*AttrValue_ListValue)(nil), "tensorflow.AttrValue.ListValue") + proto.RegisterType((*NameAttrList)(nil), "tensorflow.NameAttrList") + proto.RegisterMapType((map[string]*AttrValue)(nil), "tensorflow.NameAttrList.AttrEntry") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/attr_value.proto", fileDescriptor_06e758bf81984406) +} + +var fileDescriptor_06e758bf81984406 = []byte{ + // 526 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x94, 0x4f, 0x8b, 0xdb, 0x3c, + 0x10, 0xc6, 0x23, 0xcb, 0xc9, 0xc6, 0x4a, 0xd8, 0x37, 0x88, 0xb7, 0x54, 0x84, 0x42, 0x4d, 0xa0, + 0x45, 0x6c, 0x83, 0x43, 0xd3, 0x3f, 0x94, 0xde, 0x1a, 0x5a, 0xc8, 0xa1, 0x2c, 0x5b, 0x77, 0xe9, + 0xa1, 0x97, 0xe0, 0xa4, 0x72, 0x12, 0x36, 0x89, 0x8c, 0xac, 0x74, 0xc9, 0xb9, 0x87, 0x5e, 0xfb, + 0x39, 0xfa, 0x09, 0x7b, 0x2c, 0x33, 0xf2, 0x3a, 0x86, 0xae, 0x77, 0x6f, 0x33, 0xa3, 0xe7, 0x91, + 0xc6, 0x3f, 0x69, 0xcc, 0xce, 0xac, 0xda, 0xe5, 0xda, 0xa4, 0x1b, 0x7d, 0x3d, 0x5a, 0x68, 0xa3, + 0x46, 0xa9, 0x49, 0xb6, 0xea, 0x5a, 0x9b, 0xab, 0x51, 0x62, 0xad, 0x99, 0x7d, 0x4f, 0x36, 0x7b, + 0x15, 0x65, 0x46, 0x5b, 0xcd, 0xd9, 0x51, 0xdb, 0x7f, 0x5a, 0xef, 0x73, 0x2b, 0xce, 0xd3, 0x1f, + 0xde, 0xa7, 0x9b, 0xe5, 0xab, 0x24, 0x2b, 0x4e, 0xe8, 0x3f, 0xb9, 0x43, 0x7d, 0xc8, 0x54, 0xee, + 0x64, 0x83, 0x9f, 0x4d, 0x16, 0xbc, 0xb3, 0xd6, 0x7c, 0x81, 0xe6, 0xf8, 0x29, 0x23, 0xb9, 0xf0, + 0x42, 0x22, 0xbb, 0xd3, 0x46, 0x4c, 0x72, 0xc8, 0xd7, 0x82, 0x86, 0x44, 0x52, 0xc8, 0xd7, 0x90, + 0xa7, 0xc2, 0x0f, 0x89, 0xf4, 0x20, 0x4f, 0x21, 0x9f, 0x8b, 0x66, 0x48, 0x64, 0x1b, 0xf2, 0x39, + 0x3f, 0x63, 0x3e, 0x6c, 0x2e, 0x5a, 0x21, 0x91, 0xa7, 0xe3, 0xff, 0xa3, 0x63, 0x0f, 0xd1, 0xfb, + 0xc4, 0x26, 0x97, 0x87, 0x4c, 0x4d, 0x1b, 0x31, 0x6a, 0xf8, 0x4b, 0xd6, 0xc4, 0x7e, 0xc5, 0x49, + 0x48, 0x64, 0x67, 0xfc, 0xa8, 0x2a, 0xbe, 0xc4, 0xf0, 0x33, 0x2c, 0x5f, 0x40, 0x9b, 0xd3, 0x46, + 0xec, 0xc4, 0xfc, 0x39, 0x6b, 0x39, 0x9d, 0x68, 0xa3, 0xed, 0xe1, 0xbf, 0xb6, 0x1b, 0x47, 0x21, + 0xe4, 0xaf, 0x98, 0xbf, 0x59, 0xe7, 0x56, 0x10, 0x34, 0x3c, 0xae, 0x1a, 0xca, 0x2f, 0x8f, 0x3e, + 0xae, 0x73, 0x8b, 0x11, 0xf4, 0x07, 0x72, 0x1e, 0x31, 0x3f, 0xdd, 0xef, 0x16, 0x82, 0xa1, 0x4d, + 0x54, 0x6d, 0xe7, 0xc9, 0x56, 0x81, 0x15, 0x4c, 0xa0, 0x07, 0x1d, 0x1f, 0xb0, 0x4e, 0xb6, 0x49, + 0x16, 0x6a, 0xa5, 0x37, 0xdf, 0x94, 0x11, 0x41, 0x48, 0x64, 0x30, 0x6d, 0xc4, 0xd5, 0x62, 0xff, + 0x97, 0xc7, 0x82, 0xf2, 0x24, 0xde, 0x75, 0xb4, 0xa9, 0xec, 0x02, 0xeb, 0x9e, 0x63, 0x4d, 0x25, + 0x9d, 0x78, 0x3d, 0x02, 0xb4, 0x7b, 0x8e, 0x36, 0x95, 0x9e, 0xab, 0xa4, 0x50, 0x01, 0xde, 0x54, + 0xb6, 0x5d, 0x65, 0xce, 0x87, 0x25, 0x71, 0x5a, 0x47, 0x1c, 0xa5, 0x8e, 0xf9, 0xf8, 0xc8, 0x9c, + 0xde, 0xc7, 0xfc, 0x86, 0xf8, 0xa8, 0x42, 0x9c, 0xde, 0x41, 0xbc, 0xe4, 0x3d, 0x2c, 0xc0, 0x05, + 0x28, 0xaf, 0x05, 0xe7, 0xb0, 0x4d, 0x4e, 0x58, 0x13, 0x07, 0x63, 0xf0, 0x9b, 0xb0, 0x6e, 0x75, + 0x9d, 0x73, 0xe6, 0xef, 0x92, 0xad, 0xc2, 0x7b, 0x0b, 0x62, 0x8c, 0xf9, 0x6b, 0xe6, 0xc3, 0x2c, + 0x21, 0xb5, 0xce, 0x78, 0x50, 0xb7, 0x37, 0x5e, 0xec, 0x87, 0x9d, 0x35, 0x87, 0x18, 0xf5, 0xfd, + 0x73, 0xf7, 0xca, 0xb1, 0xc4, 0x7b, 0x8c, 0x5e, 0xa9, 0x43, 0xb1, 0x2f, 0x84, 0xfc, 0x59, 0xd1, + 0x04, 0xbe, 0xfd, 0xce, 0xf8, 0xc1, 0xad, 0x6f, 0x24, 0x76, 0x9a, 0xb7, 0xde, 0x1b, 0x32, 0xf9, + 0x41, 0x98, 0xd0, 0x66, 0x59, 0xd5, 0x95, 0xf3, 0x35, 0xf9, 0xaf, 0xb4, 0x20, 0x98, 0xfc, 0x82, + 0x7c, 0xfd, 0xb4, 0x5c, 0xdb, 0xd5, 0x7e, 0x1e, 0x2d, 0xf4, 0x76, 0x54, 0x19, 0xcc, 0xdb, 0xc3, + 0xa5, 0xae, 0xff, 0x7f, 0xcc, 0x96, 0x7a, 0x86, 0x93, 0xfb, 0x87, 0x90, 0x79, 0x0b, 0xa3, 0x17, + 0x7f, 0x03, 0x00, 0x00, 0xff, 0xff, 0x0b, 0x43, 0xf0, 0xb3, 0x7a, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/cost_graph_go_proto/cost_graph.pb.go b/tensorflow/go/core/framework/cost_graph_go_proto/cost_graph.pb.go new file mode 100644 index 0000000..a42e57b --- /dev/null +++ b/tensorflow/go/core/framework/cost_graph_go_proto/cost_graph.pb.go @@ -0,0 +1,473 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/cost_graph.proto + +package cost_graph_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type CostGraphDef struct { + Node []*CostGraphDef_Node `protobuf:"bytes,1,rep,name=node,proto3" json:"node,omitempty"` + Cost []*CostGraphDef_AggregatedCost `protobuf:"bytes,2,rep,name=cost,proto3" json:"cost,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CostGraphDef) Reset() { *m = CostGraphDef{} } +func (m *CostGraphDef) String() string { return proto.CompactTextString(m) } +func (*CostGraphDef) ProtoMessage() {} +func (*CostGraphDef) Descriptor() ([]byte, []int) { + return fileDescriptor_5f8948141565ace8, []int{0} +} + +func (m *CostGraphDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CostGraphDef.Unmarshal(m, b) +} +func (m *CostGraphDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CostGraphDef.Marshal(b, m, deterministic) +} +func (m *CostGraphDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_CostGraphDef.Merge(m, src) +} +func (m *CostGraphDef) XXX_Size() int { + return xxx_messageInfo_CostGraphDef.Size(m) +} +func (m *CostGraphDef) XXX_DiscardUnknown() { + xxx_messageInfo_CostGraphDef.DiscardUnknown(m) +} + +var xxx_messageInfo_CostGraphDef proto.InternalMessageInfo + +func (m *CostGraphDef) GetNode() []*CostGraphDef_Node { + if m != nil { + return m.Node + } + return nil +} + +func (m *CostGraphDef) GetCost() []*CostGraphDef_AggregatedCost { + if m != nil { + return m.Cost + } + return nil +} + +type CostGraphDef_Node struct { + // The name of the node. Names are globally unique. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // The device of the node. Can be empty if the node is mapped to the + // default partition or partitioning hasn't been run yet. + Device string `protobuf:"bytes,2,opt,name=device,proto3" json:"device,omitempty"` + // The id of the node. Node ids are only unique inside a partition. + Id int32 `protobuf:"varint,3,opt,name=id,proto3" json:"id,omitempty"` + InputInfo []*CostGraphDef_Node_InputInfo `protobuf:"bytes,4,rep,name=input_info,json=inputInfo,proto3" json:"input_info,omitempty"` + OutputInfo []*CostGraphDef_Node_OutputInfo `protobuf:"bytes,5,rep,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"` + // Temporary memory used by this node. + TemporaryMemorySize int64 `protobuf:"varint,6,opt,name=temporary_memory_size,json=temporaryMemorySize,proto3" json:"temporary_memory_size,omitempty"` + // Persistent memory used by this node. + PersistentMemorySize int64 `protobuf:"varint,12,opt,name=persistent_memory_size,json=persistentMemorySize,proto3" json:"persistent_memory_size,omitempty"` + HostTempMemorySize int64 `protobuf:"varint,10,opt,name=host_temp_memory_size,json=hostTempMemorySize,proto3" json:"host_temp_memory_size,omitempty"` // Deprecated: Do not use. + DeviceTempMemorySize int64 `protobuf:"varint,11,opt,name=device_temp_memory_size,json=deviceTempMemorySize,proto3" json:"device_temp_memory_size,omitempty"` // Deprecated: Do not use. + DevicePersistentMemorySize int64 `protobuf:"varint,16,opt,name=device_persistent_memory_size,json=devicePersistentMemorySize,proto3" json:"device_persistent_memory_size,omitempty"` // Deprecated: Do not use. + // Estimate of the computational cost of this node, in microseconds. + ComputeCost int64 `protobuf:"varint,9,opt,name=compute_cost,json=computeCost,proto3" json:"compute_cost,omitempty"` + // Analytical estimate of the computational cost of this node, in + // microseconds. + ComputeTime int64 `protobuf:"varint,14,opt,name=compute_time,json=computeTime,proto3" json:"compute_time,omitempty"` + // Analytical estimate of the memory access cost of this node, in + // microseconds. + MemoryTime int64 `protobuf:"varint,15,opt,name=memory_time,json=memoryTime,proto3" json:"memory_time,omitempty"` + // If true, the output is permanent: it can't be discarded, because this + // node is part of the "final output". Nodes may depend on final nodes. + IsFinal bool `protobuf:"varint,7,opt,name=is_final,json=isFinal,proto3" json:"is_final,omitempty"` + // Ids of the control inputs for this node. + ControlInput []int32 `protobuf:"varint,8,rep,packed,name=control_input,json=controlInput,proto3" json:"control_input,omitempty"` + // Are the costs inaccurate? + Inaccurate bool `protobuf:"varint,17,opt,name=inaccurate,proto3" json:"inaccurate,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CostGraphDef_Node) Reset() { *m = CostGraphDef_Node{} } +func (m *CostGraphDef_Node) String() string { return proto.CompactTextString(m) } +func (*CostGraphDef_Node) ProtoMessage() {} +func (*CostGraphDef_Node) Descriptor() ([]byte, []int) { + return fileDescriptor_5f8948141565ace8, []int{0, 0} +} + +func (m *CostGraphDef_Node) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CostGraphDef_Node.Unmarshal(m, b) +} +func (m *CostGraphDef_Node) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CostGraphDef_Node.Marshal(b, m, deterministic) +} +func (m *CostGraphDef_Node) XXX_Merge(src proto.Message) { + xxx_messageInfo_CostGraphDef_Node.Merge(m, src) +} +func (m *CostGraphDef_Node) XXX_Size() int { + return xxx_messageInfo_CostGraphDef_Node.Size(m) +} +func (m *CostGraphDef_Node) XXX_DiscardUnknown() { + xxx_messageInfo_CostGraphDef_Node.DiscardUnknown(m) +} + +var xxx_messageInfo_CostGraphDef_Node proto.InternalMessageInfo + +func (m *CostGraphDef_Node) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *CostGraphDef_Node) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *CostGraphDef_Node) GetId() int32 { + if m != nil { + return m.Id + } + return 0 +} + +func (m *CostGraphDef_Node) GetInputInfo() []*CostGraphDef_Node_InputInfo { + if m != nil { + return m.InputInfo + } + return nil +} + +func (m *CostGraphDef_Node) GetOutputInfo() []*CostGraphDef_Node_OutputInfo { + if m != nil { + return m.OutputInfo + } + return nil +} + +func (m *CostGraphDef_Node) GetTemporaryMemorySize() int64 { + if m != nil { + return m.TemporaryMemorySize + } + return 0 +} + +func (m *CostGraphDef_Node) GetPersistentMemorySize() int64 { + if m != nil { + return m.PersistentMemorySize + } + return 0 +} + +// Deprecated: Do not use. +func (m *CostGraphDef_Node) GetHostTempMemorySize() int64 { + if m != nil { + return m.HostTempMemorySize + } + return 0 +} + +// Deprecated: Do not use. +func (m *CostGraphDef_Node) GetDeviceTempMemorySize() int64 { + if m != nil { + return m.DeviceTempMemorySize + } + return 0 +} + +// Deprecated: Do not use. +func (m *CostGraphDef_Node) GetDevicePersistentMemorySize() int64 { + if m != nil { + return m.DevicePersistentMemorySize + } + return 0 +} + +func (m *CostGraphDef_Node) GetComputeCost() int64 { + if m != nil { + return m.ComputeCost + } + return 0 +} + +func (m *CostGraphDef_Node) GetComputeTime() int64 { + if m != nil { + return m.ComputeTime + } + return 0 +} + +func (m *CostGraphDef_Node) GetMemoryTime() int64 { + if m != nil { + return m.MemoryTime + } + return 0 +} + +func (m *CostGraphDef_Node) GetIsFinal() bool { + if m != nil { + return m.IsFinal + } + return false +} + +func (m *CostGraphDef_Node) GetControlInput() []int32 { + if m != nil { + return m.ControlInput + } + return nil +} + +func (m *CostGraphDef_Node) GetInaccurate() bool { + if m != nil { + return m.Inaccurate + } + return false +} + +// Inputs of this node. They must be executed before this node can be +// executed. An input is a particular output of another node, specified +// by the node id and the output index. +type CostGraphDef_Node_InputInfo struct { + PrecedingNode int32 `protobuf:"varint,1,opt,name=preceding_node,json=precedingNode,proto3" json:"preceding_node,omitempty"` + PrecedingPort int32 `protobuf:"varint,2,opt,name=preceding_port,json=precedingPort,proto3" json:"preceding_port,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CostGraphDef_Node_InputInfo) Reset() { *m = CostGraphDef_Node_InputInfo{} } +func (m *CostGraphDef_Node_InputInfo) String() string { return proto.CompactTextString(m) } +func (*CostGraphDef_Node_InputInfo) ProtoMessage() {} +func (*CostGraphDef_Node_InputInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_5f8948141565ace8, []int{0, 0, 0} +} + +func (m *CostGraphDef_Node_InputInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CostGraphDef_Node_InputInfo.Unmarshal(m, b) +} +func (m *CostGraphDef_Node_InputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CostGraphDef_Node_InputInfo.Marshal(b, m, deterministic) +} +func (m *CostGraphDef_Node_InputInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_CostGraphDef_Node_InputInfo.Merge(m, src) +} +func (m *CostGraphDef_Node_InputInfo) XXX_Size() int { + return xxx_messageInfo_CostGraphDef_Node_InputInfo.Size(m) +} +func (m *CostGraphDef_Node_InputInfo) XXX_DiscardUnknown() { + xxx_messageInfo_CostGraphDef_Node_InputInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_CostGraphDef_Node_InputInfo proto.InternalMessageInfo + +func (m *CostGraphDef_Node_InputInfo) GetPrecedingNode() int32 { + if m != nil { + return m.PrecedingNode + } + return 0 +} + +func (m *CostGraphDef_Node_InputInfo) GetPrecedingPort() int32 { + if m != nil { + return m.PrecedingPort + } + return 0 +} + +// Outputs of this node. +type CostGraphDef_Node_OutputInfo struct { + Size int64 `protobuf:"varint,1,opt,name=size,proto3" json:"size,omitempty"` + // If >= 0, the output is an alias of an input. Note that an alias input + // may itself be an alias. The algorithm will therefore need to follow + // those pointers. + AliasInputPort int64 `protobuf:"varint,2,opt,name=alias_input_port,json=aliasInputPort,proto3" json:"alias_input_port,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,3,opt,name=shape,proto3" json:"shape,omitempty"` + Dtype types_go_proto.DataType `protobuf:"varint,4,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CostGraphDef_Node_OutputInfo) Reset() { *m = CostGraphDef_Node_OutputInfo{} } +func (m *CostGraphDef_Node_OutputInfo) String() string { return proto.CompactTextString(m) } +func (*CostGraphDef_Node_OutputInfo) ProtoMessage() {} +func (*CostGraphDef_Node_OutputInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_5f8948141565ace8, []int{0, 0, 1} +} + +func (m *CostGraphDef_Node_OutputInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CostGraphDef_Node_OutputInfo.Unmarshal(m, b) +} +func (m *CostGraphDef_Node_OutputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CostGraphDef_Node_OutputInfo.Marshal(b, m, deterministic) +} +func (m *CostGraphDef_Node_OutputInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_CostGraphDef_Node_OutputInfo.Merge(m, src) +} +func (m *CostGraphDef_Node_OutputInfo) XXX_Size() int { + return xxx_messageInfo_CostGraphDef_Node_OutputInfo.Size(m) +} +func (m *CostGraphDef_Node_OutputInfo) XXX_DiscardUnknown() { + xxx_messageInfo_CostGraphDef_Node_OutputInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_CostGraphDef_Node_OutputInfo proto.InternalMessageInfo + +func (m *CostGraphDef_Node_OutputInfo) GetSize() int64 { + if m != nil { + return m.Size + } + return 0 +} + +func (m *CostGraphDef_Node_OutputInfo) GetAliasInputPort() int64 { + if m != nil { + return m.AliasInputPort + } + return 0 +} + +func (m *CostGraphDef_Node_OutputInfo) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *CostGraphDef_Node_OutputInfo) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +// Total cost of this graph, typically used for balancing decisions. +type CostGraphDef_AggregatedCost struct { + // Aggregated cost value. + Cost float32 `protobuf:"fixed32,1,opt,name=cost,proto3" json:"cost,omitempty"` + // Aggregated cost dimension (e.g. 'memory', 'compute', 'network'). + Dimension string `protobuf:"bytes,2,opt,name=dimension,proto3" json:"dimension,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CostGraphDef_AggregatedCost) Reset() { *m = CostGraphDef_AggregatedCost{} } +func (m *CostGraphDef_AggregatedCost) String() string { return proto.CompactTextString(m) } +func (*CostGraphDef_AggregatedCost) ProtoMessage() {} +func (*CostGraphDef_AggregatedCost) Descriptor() ([]byte, []int) { + return fileDescriptor_5f8948141565ace8, []int{0, 1} +} + +func (m *CostGraphDef_AggregatedCost) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CostGraphDef_AggregatedCost.Unmarshal(m, b) +} +func (m *CostGraphDef_AggregatedCost) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CostGraphDef_AggregatedCost.Marshal(b, m, deterministic) +} +func (m *CostGraphDef_AggregatedCost) XXX_Merge(src proto.Message) { + xxx_messageInfo_CostGraphDef_AggregatedCost.Merge(m, src) +} +func (m *CostGraphDef_AggregatedCost) XXX_Size() int { + return xxx_messageInfo_CostGraphDef_AggregatedCost.Size(m) +} +func (m *CostGraphDef_AggregatedCost) XXX_DiscardUnknown() { + xxx_messageInfo_CostGraphDef_AggregatedCost.DiscardUnknown(m) +} + +var xxx_messageInfo_CostGraphDef_AggregatedCost proto.InternalMessageInfo + +func (m *CostGraphDef_AggregatedCost) GetCost() float32 { + if m != nil { + return m.Cost + } + return 0 +} + +func (m *CostGraphDef_AggregatedCost) GetDimension() string { + if m != nil { + return m.Dimension + } + return "" +} + +func init() { + proto.RegisterType((*CostGraphDef)(nil), "tensorflow.CostGraphDef") + proto.RegisterType((*CostGraphDef_Node)(nil), "tensorflow.CostGraphDef.Node") + proto.RegisterType((*CostGraphDef_Node_InputInfo)(nil), "tensorflow.CostGraphDef.Node.InputInfo") + proto.RegisterType((*CostGraphDef_Node_OutputInfo)(nil), "tensorflow.CostGraphDef.Node.OutputInfo") + proto.RegisterType((*CostGraphDef_AggregatedCost)(nil), "tensorflow.CostGraphDef.AggregatedCost") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/cost_graph.proto", fileDescriptor_5f8948141565ace8) +} + +var fileDescriptor_5f8948141565ace8 = []byte{ + // 683 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x54, 0x4f, 0x6f, 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0xbe, 0x02, 0x4b, 0x84, 0x54, 0x64, 0xf0, 0x32, 0x6c, + 0x24, 0xa4, 0xb2, 0xbe, 0x1b, 0x00, 0x4b, 0x7f, 0xe9, 0x11, 0xa1, 0xe7, 0x35, 0xe8, 0x8e, 0x14, + 0x33, 0x1b, 0x76, 0xbd, 0x19, 0xf7, 0xd2, 0xec, 0x02, 0x4b, 0xad, 0xaa, 0xd3, 0xa6, 0x3c, 0xb5, + 0xa6, 0xc5, 0x58, 0x1f, 0xea, 0xb4, 0x20, 0x68, 0x6e, 0x9a, 0xfd, 0xc3, 0xb2, 0xad, 0xc7, 0x14, + 0x9e, 0xeb, 0xe3, 0x91, 0xde, 0x0b, 0x4e, 0x06, 0x65, 0x27, 0x50, 0x0f, 0xf4, 0xba, 0x30, 0x6b, + 0x1d, 0xc3, 0x6e, 0xf7, 0xf7, 0xca, 0x9c, 0x53, 0x4f, 0x79, 0xe3, 0x45, 0x82, 0x4e, 0x06, 0xb1, + 0x06, 0xd0, 0x5e, 0x9d, 0x70, 0xdd, 0x2f, 0xfd, 0x58, 0xdd, 0x6f, 0x25, 0x9b, 0x77, 0x76, 0x08, + 0x8d, 0x80, 0x47, 0x18, 0xa7, 0x5c, 0xc4, 0xf9, 0x54, 0x2f, 0x13, 0x83, 0xaf, 0x06, 0x98, 0x42, + 0x86, 0xe5, 0x32, 0xc5, 0xde, 0x1a, 0xec, 0x14, 0xc3, 0x47, 0x3d, 0xa6, 0x23, 0xe3, 0xd3, 0x87, + 0x90, 0xab, 0xe9, 0xfc, 0xa2, 0xeb, 0x8b, 0xa8, 0x57, 0x5a, 0x78, 0xd7, 0x87, 0xa1, 0x58, 0xbf, + 0x6f, 0xdd, 0x50, 0xb8, 0xb4, 0x11, 0x7f, 0x1a, 0xc6, 0xc5, 0x06, 0x45, 0x4f, 0x7e, 0x05, 0x00, + 0x00, 0xff, 0xff, 0xd7, 0x56, 0x04, 0x65, 0xaa, 0x05, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/device_attributes_go_proto/device_attributes.pb.go b/tensorflow/go/core/framework/device_attributes_go_proto/device_attributes.pb.go new file mode 100644 index 0000000..9a28e08 --- /dev/null +++ b/tensorflow/go/core/framework/device_attributes_go_proto/device_attributes.pb.go @@ -0,0 +1,303 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/device_attributes.proto + +package device_attributes_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type InterconnectLink struct { + DeviceId int32 `protobuf:"varint,1,opt,name=device_id,json=deviceId,proto3" json:"device_id,omitempty"` + Type string `protobuf:"bytes,2,opt,name=type,proto3" json:"type,omitempty"` + Strength int32 `protobuf:"varint,3,opt,name=strength,proto3" json:"strength,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *InterconnectLink) Reset() { *m = InterconnectLink{} } +func (m *InterconnectLink) String() string { return proto.CompactTextString(m) } +func (*InterconnectLink) ProtoMessage() {} +func (*InterconnectLink) Descriptor() ([]byte, []int) { + return fileDescriptor_74908851c78ce22e, []int{0} +} + +func (m *InterconnectLink) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_InterconnectLink.Unmarshal(m, b) +} +func (m *InterconnectLink) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_InterconnectLink.Marshal(b, m, deterministic) +} +func (m *InterconnectLink) XXX_Merge(src proto.Message) { + xxx_messageInfo_InterconnectLink.Merge(m, src) +} +func (m *InterconnectLink) XXX_Size() int { + return xxx_messageInfo_InterconnectLink.Size(m) +} +func (m *InterconnectLink) XXX_DiscardUnknown() { + xxx_messageInfo_InterconnectLink.DiscardUnknown(m) +} + +var xxx_messageInfo_InterconnectLink proto.InternalMessageInfo + +func (m *InterconnectLink) GetDeviceId() int32 { + if m != nil { + return m.DeviceId + } + return 0 +} + +func (m *InterconnectLink) GetType() string { + if m != nil { + return m.Type + } + return "" +} + +func (m *InterconnectLink) GetStrength() int32 { + if m != nil { + return m.Strength + } + return 0 +} + +type LocalLinks struct { + Link []*InterconnectLink `protobuf:"bytes,1,rep,name=link,proto3" json:"link,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *LocalLinks) Reset() { *m = LocalLinks{} } +func (m *LocalLinks) String() string { return proto.CompactTextString(m) } +func (*LocalLinks) ProtoMessage() {} +func (*LocalLinks) Descriptor() ([]byte, []int) { + return fileDescriptor_74908851c78ce22e, []int{1} +} + +func (m *LocalLinks) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_LocalLinks.Unmarshal(m, b) +} +func (m *LocalLinks) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_LocalLinks.Marshal(b, m, deterministic) +} +func (m *LocalLinks) XXX_Merge(src proto.Message) { + xxx_messageInfo_LocalLinks.Merge(m, src) +} +func (m *LocalLinks) XXX_Size() int { + return xxx_messageInfo_LocalLinks.Size(m) +} +func (m *LocalLinks) XXX_DiscardUnknown() { + xxx_messageInfo_LocalLinks.DiscardUnknown(m) +} + +var xxx_messageInfo_LocalLinks proto.InternalMessageInfo + +func (m *LocalLinks) GetLink() []*InterconnectLink { + if m != nil { + return m.Link + } + return nil +} + +type DeviceLocality struct { + // Optional bus locality of device. Default value of 0 means + // no specific locality. Specific localities are indexed from 1. + BusId int32 `protobuf:"varint,1,opt,name=bus_id,json=busId,proto3" json:"bus_id,omitempty"` + // Optional NUMA locality of device. + NumaNode int32 `protobuf:"varint,2,opt,name=numa_node,json=numaNode,proto3" json:"numa_node,omitempty"` + // Optional local interconnect links to other devices. + Links *LocalLinks `protobuf:"bytes,3,opt,name=links,proto3" json:"links,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeviceLocality) Reset() { *m = DeviceLocality{} } +func (m *DeviceLocality) String() string { return proto.CompactTextString(m) } +func (*DeviceLocality) ProtoMessage() {} +func (*DeviceLocality) Descriptor() ([]byte, []int) { + return fileDescriptor_74908851c78ce22e, []int{2} +} + +func (m *DeviceLocality) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeviceLocality.Unmarshal(m, b) +} +func (m *DeviceLocality) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeviceLocality.Marshal(b, m, deterministic) +} +func (m *DeviceLocality) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeviceLocality.Merge(m, src) +} +func (m *DeviceLocality) XXX_Size() int { + return xxx_messageInfo_DeviceLocality.Size(m) +} +func (m *DeviceLocality) XXX_DiscardUnknown() { + xxx_messageInfo_DeviceLocality.DiscardUnknown(m) +} + +var xxx_messageInfo_DeviceLocality proto.InternalMessageInfo + +func (m *DeviceLocality) GetBusId() int32 { + if m != nil { + return m.BusId + } + return 0 +} + +func (m *DeviceLocality) GetNumaNode() int32 { + if m != nil { + return m.NumaNode + } + return 0 +} + +func (m *DeviceLocality) GetLinks() *LocalLinks { + if m != nil { + return m.Links + } + return nil +} + +type DeviceAttributes struct { + // Fully specified name of the device within a cluster. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // String representation of device_type. + DeviceType string `protobuf:"bytes,2,opt,name=device_type,json=deviceType,proto3" json:"device_type,omitempty"` + // Memory capacity of device in bytes. + MemoryLimit int64 `protobuf:"varint,4,opt,name=memory_limit,json=memoryLimit,proto3" json:"memory_limit,omitempty"` + // Platform-specific data about device that may be useful + // for supporting efficient data transfers. + Locality *DeviceLocality `protobuf:"bytes,5,opt,name=locality,proto3" json:"locality,omitempty"` + // A device is assigned a global unique number each time it is + // initialized. "incarnation" should never be 0. + Incarnation uint64 `protobuf:"fixed64,6,opt,name=incarnation,proto3" json:"incarnation,omitempty"` + // String representation of the physical device that this device maps to. + PhysicalDeviceDesc string `protobuf:"bytes,7,opt,name=physical_device_desc,json=physicalDeviceDesc,proto3" json:"physical_device_desc,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeviceAttributes) Reset() { *m = DeviceAttributes{} } +func (m *DeviceAttributes) String() string { return proto.CompactTextString(m) } +func (*DeviceAttributes) ProtoMessage() {} +func (*DeviceAttributes) Descriptor() ([]byte, []int) { + return fileDescriptor_74908851c78ce22e, []int{3} +} + +func (m *DeviceAttributes) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeviceAttributes.Unmarshal(m, b) +} +func (m *DeviceAttributes) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeviceAttributes.Marshal(b, m, deterministic) +} +func (m *DeviceAttributes) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeviceAttributes.Merge(m, src) +} +func (m *DeviceAttributes) XXX_Size() int { + return xxx_messageInfo_DeviceAttributes.Size(m) +} +func (m *DeviceAttributes) XXX_DiscardUnknown() { + xxx_messageInfo_DeviceAttributes.DiscardUnknown(m) +} + +var xxx_messageInfo_DeviceAttributes proto.InternalMessageInfo + +func (m *DeviceAttributes) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *DeviceAttributes) GetDeviceType() string { + if m != nil { + return m.DeviceType + } + return "" +} + +func (m *DeviceAttributes) GetMemoryLimit() int64 { + if m != nil { + return m.MemoryLimit + } + return 0 +} + +func (m *DeviceAttributes) GetLocality() *DeviceLocality { + if m != nil { + return m.Locality + } + return nil +} + +func (m *DeviceAttributes) GetIncarnation() uint64 { + if m != nil { + return m.Incarnation + } + return 0 +} + +func (m *DeviceAttributes) GetPhysicalDeviceDesc() string { + if m != nil { + return m.PhysicalDeviceDesc + } + return "" +} + +func init() { + proto.RegisterType((*InterconnectLink)(nil), "tensorflow.InterconnectLink") + proto.RegisterType((*LocalLinks)(nil), "tensorflow.LocalLinks") + proto.RegisterType((*DeviceLocality)(nil), "tensorflow.DeviceLocality") + proto.RegisterType((*DeviceAttributes)(nil), "tensorflow.DeviceAttributes") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/device_attributes.proto", fileDescriptor_74908851c78ce22e) +} + +var fileDescriptor_74908851c78ce22e = []byte{ + // 425 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x92, 0xc1, 0x8b, 0xd3, 0x40, + 0x14, 0xc6, 0x19, 0xdb, 0xd4, 0xf6, 0x45, 0x64, 0x19, 0x74, 0x19, 0x56, 0xc1, 0xd8, 0x53, 0x0e, + 0xd2, 0xae, 0x2b, 0x78, 0x14, 0x5c, 0xf6, 0x52, 0x28, 0xb2, 0x0c, 0x1e, 0xc4, 0x4b, 0x48, 0x26, + 0xb3, 0xe9, 0xd0, 0x64, 0x5e, 0x99, 0x99, 0xb8, 0xf4, 0xcf, 0xf0, 0x9f, 0x15, 0x8f, 0x32, 0x33, + 0xdd, 0x36, 0x2e, 0x82, 0xb7, 0x97, 0x2f, 0x6f, 0xde, 0xf7, 0x7b, 0x8f, 0x0f, 0xde, 0x3b, 0xa9, + 0x2d, 0x9a, 0xbb, 0x16, 0xef, 0x97, 0x02, 0x8d, 0x5c, 0xde, 0x99, 0xb2, 0x93, 0xf7, 0x68, 0xb6, + 0xcb, 0x5a, 0xfe, 0x50, 0x42, 0x16, 0xa5, 0x73, 0x46, 0x55, 0xbd, 0x93, 0x76, 0xb1, 0x33, 0xe8, + 0x90, 0xc2, 0xe9, 0xc9, 0xbc, 0x80, 0xb3, 0x95, 0x76, 0xd2, 0x08, 0xd4, 0x5a, 0x0a, 0xb7, 0x56, + 0x7a, 0x4b, 0x5f, 0xc1, 0xec, 0xf0, 0x54, 0xd5, 0x8c, 0x64, 0x24, 0x4f, 0xf8, 0x34, 0x0a, 0xab, + 0x9a, 0x52, 0x18, 0xbb, 0xfd, 0x4e, 0xb2, 0x27, 0x19, 0xc9, 0x67, 0x3c, 0xd4, 0xf4, 0x02, 0xa6, + 0xd6, 0x19, 0xa9, 0x1b, 0xb7, 0x61, 0xa3, 0xd8, 0xff, 0xf0, 0x3d, 0xff, 0x04, 0xb0, 0x46, 0x51, + 0xb6, 0x7e, 0xb2, 0xa5, 0x97, 0x30, 0x6e, 0x95, 0xde, 0x32, 0x92, 0x8d, 0xf2, 0xf4, 0xea, 0xf5, + 0xe2, 0x44, 0xb2, 0x78, 0x8c, 0xc1, 0x43, 0xe7, 0xdc, 0xc0, 0xf3, 0x9b, 0xe0, 0x1d, 0xa6, 0x28, + 0xb7, 0xa7, 0x2f, 0x61, 0x52, 0xf5, 0xf6, 0xc4, 0x96, 0x54, 0xbd, 0x5d, 0xd5, 0x9e, 0x5a, 0xf7, + 0x5d, 0x59, 0x68, 0xac, 0x23, 0x5d, 0xc2, 0xa7, 0x5e, 0xf8, 0x82, 0xb5, 0xa4, 0xef, 0x20, 0xf1, + 0xd3, 0x6c, 0xc0, 0x4b, 0xaf, 0xce, 0x87, 0xc6, 0x27, 0x3c, 0x1e, 0x9b, 0xe6, 0xbf, 0x08, 0x9c, + 0x45, 0xd3, 0xcf, 0xc7, 0xdb, 0xf9, 0xc5, 0x75, 0xd9, 0xc9, 0x60, 0x3a, 0xe3, 0xa1, 0xa6, 0x6f, + 0x20, 0x3d, 0x5c, 0x6a, 0x70, 0x13, 0x88, 0xd2, 0x57, 0x7f, 0x99, 0xb7, 0xf0, 0xac, 0x93, 0x1d, + 0x9a, 0x7d, 0xd1, 0xaa, 0x4e, 0x39, 0x36, 0xce, 0x48, 0x3e, 0xe2, 0x69, 0xd4, 0xd6, 0x5e, 0xa2, + 0x1f, 0x61, 0xda, 0x1e, 0x56, 0x63, 0x49, 0xa0, 0xbb, 0x18, 0xd2, 0xfd, 0xbd, 0x3c, 0x3f, 0xf6, + 0xd2, 0x0c, 0x52, 0xa5, 0x45, 0x69, 0x74, 0xe9, 0x14, 0x6a, 0x36, 0xc9, 0x48, 0x3e, 0xe1, 0x43, + 0x89, 0x5e, 0xc2, 0x8b, 0xdd, 0x66, 0x6f, 0x95, 0x28, 0xdb, 0xe2, 0x80, 0x59, 0x4b, 0x2b, 0xd8, + 0xd3, 0x80, 0x49, 0x1f, 0xfe, 0x45, 0x87, 0x1b, 0x69, 0xc5, 0xf5, 0x4f, 0x02, 0x0c, 0x4d, 0x33, + 0xf4, 0x3f, 0xc6, 0xe9, 0xfa, 0xfc, 0xf1, 0x49, 0x6e, 0x7d, 0x9a, 0xec, 0x2d, 0xf9, 0xfe, 0xad, + 0x51, 0x6e, 0xd3, 0x57, 0x0b, 0x81, 0xdd, 0x72, 0x10, 0xc7, 0x7f, 0x97, 0x0d, 0xfe, 0x37, 0xa7, + 0x45, 0x83, 0x45, 0x88, 0xea, 0x6f, 0x42, 0xaa, 0x49, 0xa8, 0x3e, 0xfc, 0x09, 0x00, 0x00, 0xff, + 0xff, 0xd8, 0xfb, 0xe9, 0xc1, 0xe9, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/function_go_proto/function.pb.go b/tensorflow/go/core/framework/function_go_proto/function.pb.go new file mode 100644 index 0000000..48bea38 --- /dev/null +++ b/tensorflow/go/core/framework/function_go_proto/function.pb.go @@ -0,0 +1,347 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/function.proto + +package function_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + node_def_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/node_def_go_proto" + op_def_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// A library is a set of named functions. +type FunctionDefLibrary struct { + Function []*FunctionDef `protobuf:"bytes,1,rep,name=function,proto3" json:"function,omitempty"` + Gradient []*GradientDef `protobuf:"bytes,2,rep,name=gradient,proto3" json:"gradient,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *FunctionDefLibrary) Reset() { *m = FunctionDefLibrary{} } +func (m *FunctionDefLibrary) String() string { return proto.CompactTextString(m) } +func (*FunctionDefLibrary) ProtoMessage() {} +func (*FunctionDefLibrary) Descriptor() ([]byte, []int) { + return fileDescriptor_507748d6812c5f14, []int{0} +} + +func (m *FunctionDefLibrary) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_FunctionDefLibrary.Unmarshal(m, b) +} +func (m *FunctionDefLibrary) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_FunctionDefLibrary.Marshal(b, m, deterministic) +} +func (m *FunctionDefLibrary) XXX_Merge(src proto.Message) { + xxx_messageInfo_FunctionDefLibrary.Merge(m, src) +} +func (m *FunctionDefLibrary) XXX_Size() int { + return xxx_messageInfo_FunctionDefLibrary.Size(m) +} +func (m *FunctionDefLibrary) XXX_DiscardUnknown() { + xxx_messageInfo_FunctionDefLibrary.DiscardUnknown(m) +} + +var xxx_messageInfo_FunctionDefLibrary proto.InternalMessageInfo + +func (m *FunctionDefLibrary) GetFunction() []*FunctionDef { + if m != nil { + return m.Function + } + return nil +} + +func (m *FunctionDefLibrary) GetGradient() []*GradientDef { + if m != nil { + return m.Gradient + } + return nil +} + +// A function can be instantiated when the runtime can bind every attr +// with a value. When a GraphDef has a call to a function, it must +// have binding for every attr defined in the signature. +// +// TODO(zhifengc): +// * device spec, etc. +type FunctionDef struct { + // The definition of the function's name, arguments, return values, + // attrs etc. + Signature *op_def_go_proto.OpDef `protobuf:"bytes,1,opt,name=signature,proto3" json:"signature,omitempty"` + // Attributes specific to this function definition. + Attr map[string]*attr_value_go_proto.AttrValue `protobuf:"bytes,5,rep,name=attr,proto3" json:"attr,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + ArgAttr map[uint32]*FunctionDef_ArgAttrs `protobuf:"bytes,7,rep,name=arg_attr,json=argAttr,proto3" json:"arg_attr,omitempty" protobuf_key:"varint,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // Unique IDs for each resource argument, used to track aliasing resources. If + // Argument A and Argument B alias each other, then + // resource_arg_unique_ids[A.index] == resource_arg_unique_ids[B.index]. + // + // If this field is empty, none of the arguments could alias; otherwise, every + // resource argument should have an entry in this field. + // + // When instantiated, the unique IDs will be attached to the _Arg nodes' + // "_resource_arg_unique_id" attribute. + ResourceArgUniqueId map[uint32]uint32 `protobuf:"bytes,8,rep,name=resource_arg_unique_id,json=resourceArgUniqueId,proto3" json:"resource_arg_unique_id,omitempty" protobuf_key:"varint,1,opt,name=key,proto3" protobuf_val:"varint,2,opt,name=value,proto3"` + // By convention, "op" in node_def is resolved by consulting with a + // user-defined library first. If not resolved, "func" is assumed to + // be a builtin op. + NodeDef []*node_def_go_proto.NodeDef `protobuf:"bytes,3,rep,name=node_def,json=nodeDef,proto3" json:"node_def,omitempty"` + // A mapping from the output arg names from `signature` to the + // outputs from `node_def` that should be returned by the function. + Ret map[string]string `protobuf:"bytes,4,rep,name=ret,proto3" json:"ret,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // A mapping from control output names from `signature` to node names in + // `node_def` which should be control outputs of this function. + ControlRet map[string]string `protobuf:"bytes,6,rep,name=control_ret,json=controlRet,proto3" json:"control_ret,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *FunctionDef) Reset() { *m = FunctionDef{} } +func (m *FunctionDef) String() string { return proto.CompactTextString(m) } +func (*FunctionDef) ProtoMessage() {} +func (*FunctionDef) Descriptor() ([]byte, []int) { + return fileDescriptor_507748d6812c5f14, []int{1} +} + +func (m *FunctionDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_FunctionDef.Unmarshal(m, b) +} +func (m *FunctionDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_FunctionDef.Marshal(b, m, deterministic) +} +func (m *FunctionDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_FunctionDef.Merge(m, src) +} +func (m *FunctionDef) XXX_Size() int { + return xxx_messageInfo_FunctionDef.Size(m) +} +func (m *FunctionDef) XXX_DiscardUnknown() { + xxx_messageInfo_FunctionDef.DiscardUnknown(m) +} + +var xxx_messageInfo_FunctionDef proto.InternalMessageInfo + +func (m *FunctionDef) GetSignature() *op_def_go_proto.OpDef { + if m != nil { + return m.Signature + } + return nil +} + +func (m *FunctionDef) GetAttr() map[string]*attr_value_go_proto.AttrValue { + if m != nil { + return m.Attr + } + return nil +} + +func (m *FunctionDef) GetArgAttr() map[uint32]*FunctionDef_ArgAttrs { + if m != nil { + return m.ArgAttr + } + return nil +} + +func (m *FunctionDef) GetResourceArgUniqueId() map[uint32]uint32 { + if m != nil { + return m.ResourceArgUniqueId + } + return nil +} + +func (m *FunctionDef) GetNodeDef() []*node_def_go_proto.NodeDef { + if m != nil { + return m.NodeDef + } + return nil +} + +func (m *FunctionDef) GetRet() map[string]string { + if m != nil { + return m.Ret + } + return nil +} + +func (m *FunctionDef) GetControlRet() map[string]string { + if m != nil { + return m.ControlRet + } + return nil +} + +// Attributes for function arguments. These attributes are the same set of +// valid attributes as to _Arg nodes. +type FunctionDef_ArgAttrs struct { + Attr map[string]*attr_value_go_proto.AttrValue `protobuf:"bytes,1,rep,name=attr,proto3" json:"attr,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *FunctionDef_ArgAttrs) Reset() { *m = FunctionDef_ArgAttrs{} } +func (m *FunctionDef_ArgAttrs) String() string { return proto.CompactTextString(m) } +func (*FunctionDef_ArgAttrs) ProtoMessage() {} +func (*FunctionDef_ArgAttrs) Descriptor() ([]byte, []int) { + return fileDescriptor_507748d6812c5f14, []int{1, 1} +} + +func (m *FunctionDef_ArgAttrs) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_FunctionDef_ArgAttrs.Unmarshal(m, b) +} +func (m *FunctionDef_ArgAttrs) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_FunctionDef_ArgAttrs.Marshal(b, m, deterministic) +} +func (m *FunctionDef_ArgAttrs) XXX_Merge(src proto.Message) { + xxx_messageInfo_FunctionDef_ArgAttrs.Merge(m, src) +} +func (m *FunctionDef_ArgAttrs) XXX_Size() int { + return xxx_messageInfo_FunctionDef_ArgAttrs.Size(m) +} +func (m *FunctionDef_ArgAttrs) XXX_DiscardUnknown() { + xxx_messageInfo_FunctionDef_ArgAttrs.DiscardUnknown(m) +} + +var xxx_messageInfo_FunctionDef_ArgAttrs proto.InternalMessageInfo + +func (m *FunctionDef_ArgAttrs) GetAttr() map[string]*attr_value_go_proto.AttrValue { + if m != nil { + return m.Attr + } + return nil +} + +// GradientDef defines the gradient function of a function defined in +// a function library. +// +// A gradient function g (specified by gradient_func) for a function f +// (specified by function_name) must follow the following: +// +// The function 'f' must be a numerical function which takes N inputs +// and produces M outputs. Its gradient function 'g', which is a +// function taking N + M inputs and produces N outputs. +// +// I.e. if we have +// (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), +// then, g is +// (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, +// dL/dy1, dL/dy2, ..., dL/dy_M), +// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the +// loss function). dL/dx_i is the partial derivative of L with respect +// to x_i. +type GradientDef struct { + FunctionName string `protobuf:"bytes,1,opt,name=function_name,json=functionName,proto3" json:"function_name,omitempty"` + GradientFunc string `protobuf:"bytes,2,opt,name=gradient_func,json=gradientFunc,proto3" json:"gradient_func,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GradientDef) Reset() { *m = GradientDef{} } +func (m *GradientDef) String() string { return proto.CompactTextString(m) } +func (*GradientDef) ProtoMessage() {} +func (*GradientDef) Descriptor() ([]byte, []int) { + return fileDescriptor_507748d6812c5f14, []int{2} +} + +func (m *GradientDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GradientDef.Unmarshal(m, b) +} +func (m *GradientDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GradientDef.Marshal(b, m, deterministic) +} +func (m *GradientDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_GradientDef.Merge(m, src) +} +func (m *GradientDef) XXX_Size() int { + return xxx_messageInfo_GradientDef.Size(m) +} +func (m *GradientDef) XXX_DiscardUnknown() { + xxx_messageInfo_GradientDef.DiscardUnknown(m) +} + +var xxx_messageInfo_GradientDef proto.InternalMessageInfo + +func (m *GradientDef) GetFunctionName() string { + if m != nil { + return m.FunctionName + } + return "" +} + +func (m *GradientDef) GetGradientFunc() string { + if m != nil { + return m.GradientFunc + } + return "" +} + +func init() { + proto.RegisterType((*FunctionDefLibrary)(nil), "tensorflow.FunctionDefLibrary") + proto.RegisterType((*FunctionDef)(nil), "tensorflow.FunctionDef") + proto.RegisterMapType((map[uint32]*FunctionDef_ArgAttrs)(nil), "tensorflow.FunctionDef.ArgAttrEntry") + proto.RegisterMapType((map[string]*attr_value_go_proto.AttrValue)(nil), "tensorflow.FunctionDef.AttrEntry") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.FunctionDef.ControlRetEntry") + proto.RegisterMapType((map[uint32]uint32)(nil), "tensorflow.FunctionDef.ResourceArgUniqueIdEntry") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.FunctionDef.RetEntry") + proto.RegisterType((*FunctionDef_ArgAttrs)(nil), "tensorflow.FunctionDef.ArgAttrs") + proto.RegisterMapType((map[string]*attr_value_go_proto.AttrValue)(nil), "tensorflow.FunctionDef.ArgAttrs.AttrEntry") + proto.RegisterType((*GradientDef)(nil), "tensorflow.GradientDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/function.proto", fileDescriptor_507748d6812c5f14) +} + +var fileDescriptor_507748d6812c5f14 = []byte{ + // 576 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xac, 0x94, 0x6f, 0x6b, 0x13, 0x4f, + 0x10, 0xc7, 0xb9, 0x24, 0x6d, 0x2e, 0x93, 0xe4, 0xf7, 0xab, 0x5b, 0xff, 0x1c, 0x79, 0x14, 0xa3, + 0x68, 0xa8, 0x70, 0x91, 0x14, 0x8b, 0x08, 0x2a, 0xad, 0x5a, 0xff, 0x20, 0x69, 0x09, 0xa8, 0x20, + 0xc2, 0xb1, 0xb9, 0xdb, 0x9c, 0x47, 0x93, 0xdd, 0x38, 0xb7, 0x67, 0xc9, 0x13, 0xf1, 0x75, 0xf8, + 0xd6, 0x7c, 0x23, 0x3e, 0x94, 0xdd, 0xbb, 0xcb, 0x6d, 0xdb, 0x9c, 0x45, 0xf0, 0xd9, 0x66, 0xf7, + 0xfb, 0xf9, 0xce, 0x64, 0x66, 0x6e, 0xa0, 0x2f, 0x19, 0x8f, 0x05, 0x4e, 0x67, 0xe2, 0x74, 0xe0, + 0x0b, 0x64, 0x83, 0x29, 0xd2, 0x39, 0x3b, 0x15, 0x78, 0x32, 0x98, 0x26, 0xdc, 0x97, 0x91, 0xe0, + 0xee, 0x02, 0x85, 0x14, 0x04, 0x0a, 0x65, 0x67, 0xa7, 0x9c, 0xa2, 0x52, 0xa2, 0xf7, 0x95, 0xce, + 0x12, 0x96, 0x72, 0x9d, 0x3f, 0x44, 0xe0, 0x22, 0x60, 0x5e, 0xc0, 0xa6, 0x99, 0xf2, 0x4e, 0xb9, + 0x52, 0x2c, 0x0a, 0x5d, 0xef, 0x1b, 0x90, 0xc3, 0x2c, 0xb7, 0xe7, 0x6c, 0xfa, 0x36, 0x9a, 0x20, + 0xc5, 0x25, 0xd9, 0x05, 0x3b, 0xcf, 0xd8, 0xb1, 0xba, 0xd5, 0x7e, 0x73, 0x78, 0xc3, 0x2d, 0x0c, + 0x5d, 0x83, 0x18, 0xaf, 0x84, 0x0a, 0x0a, 0x91, 0x06, 0x11, 0xe3, 0xd2, 0xa9, 0x5c, 0x84, 0x5e, + 0x66, 0x6f, 0x1a, 0xca, 0x85, 0xbd, 0x9f, 0x75, 0x68, 0x1a, 0x76, 0x64, 0x00, 0x8d, 0x38, 0x0a, + 0x39, 0x95, 0x09, 0x32, 0xc7, 0xea, 0x5a, 0xfd, 0xe6, 0xf0, 0x8a, 0xe9, 0x72, 0xb4, 0x50, 0x7c, + 0xa1, 0x21, 0x0f, 0xa0, 0xa6, 0xca, 0xe4, 0x6c, 0xe8, 0x88, 0x37, 0x4b, 0xd2, 0x74, 0xf7, 0xa5, + 0xc4, 0x17, 0x5c, 0xe2, 0x72, 0xac, 0xe5, 0xe4, 0x29, 0xd8, 0x14, 0x43, 0x4f, 0xa3, 0x75, 0x8d, + 0xde, 0x2e, 0x45, 0x31, 0x2c, 0xe8, 0x3a, 0x4d, 0x7f, 0x11, 0x06, 0xd7, 0x91, 0xc5, 0x22, 0x41, + 0x9f, 0x79, 0xca, 0x29, 0xe1, 0xd1, 0x97, 0x84, 0x79, 0x51, 0xe0, 0xd8, 0xda, 0xee, 0x7e, 0x99, + 0xdd, 0x38, 0xa3, 0xf6, 0x31, 0x7c, 0xa7, 0x99, 0xd7, 0x41, 0x6a, 0xbd, 0x8d, 0x17, 0x5f, 0x88, + 0x0b, 0x76, 0xde, 0x59, 0xa7, 0xaa, 0x8d, 0xb7, 0x4d, 0xe3, 0x91, 0x08, 0x98, 0x2a, 0x48, 0x9d, + 0xa7, 0x07, 0x32, 0x84, 0x2a, 0x32, 0xe9, 0xd4, 0xb4, 0xb4, 0x5b, 0x9e, 0x83, 0x4c, 0x63, 0x2a, + 0x31, 0x79, 0x05, 0x4d, 0x5f, 0x70, 0x89, 0x62, 0xe6, 0x29, 0x76, 0x53, 0xb3, 0x77, 0xcb, 0xd8, + 0x67, 0xa9, 0x74, 0x65, 0x01, 0xfe, 0xea, 0xa2, 0x33, 0x82, 0xc6, 0xaa, 0x54, 0x64, 0x0b, 0xaa, + 0x27, 0x6c, 0xa9, 0x9b, 0xd8, 0x18, 0xab, 0x23, 0xb9, 0x07, 0x1b, 0x7a, 0x9a, 0x9d, 0x8a, 0x6e, + 0xec, 0x35, 0x33, 0x84, 0xe2, 0xde, 0xab, 0xc7, 0x71, 0xaa, 0x79, 0x54, 0x79, 0x68, 0x75, 0x7e, + 0x58, 0x60, 0x67, 0xe5, 0x8f, 0xc9, 0x93, 0xac, 0xd3, 0xe9, 0x40, 0xee, 0x5c, 0xd2, 0xae, 0xf8, + 0x7c, 0xcb, 0xff, 0x79, 0x72, 0x9f, 0xa0, 0x65, 0x8e, 0x86, 0x69, 0xd9, 0x4e, 0x2d, 0xf7, 0xce, + 0x5a, 0x76, 0x2f, 0x4b, 0xd9, 0x74, 0x3f, 0x04, 0xa7, 0x6c, 0x52, 0xd6, 0x44, 0xba, 0x6a, 0x46, + 0x6a, 0x9b, 0x3e, 0x7b, 0x60, 0xe7, 0xad, 0x5a, 0xf3, 0xa7, 0xcf, 0x70, 0x0d, 0x93, 0x7b, 0x0c, + 0xff, 0x9f, 0xeb, 0xf4, 0xdf, 0xe0, 0x6f, 0x6a, 0x76, 0x65, 0xab, 0xda, 0xfb, 0x00, 0x4d, 0xe3, + 0xb3, 0x27, 0xb7, 0xa0, 0x9d, 0x6f, 0x0b, 0x8f, 0xd3, 0x39, 0xcb, 0xac, 0x5a, 0xf9, 0xe5, 0x88, + 0xce, 0x99, 0x12, 0xe5, 0xdb, 0xc1, 0x53, 0x0f, 0x99, 0x77, 0x2b, 0xbf, 0x54, 0x85, 0x3b, 0xf8, + 0x6e, 0x81, 0x23, 0x30, 0x34, 0x0b, 0xba, 0x5a, 0x70, 0x07, 0xff, 0xe5, 0xb5, 0x3d, 0x56, 0x2b, + 0x2e, 0x3e, 0xb6, 0x3e, 0x1e, 0x85, 0x91, 0xfc, 0x9c, 0x4c, 0x5c, 0x5f, 0xcc, 0x07, 0xc6, 0x62, + 0x5c, 0x7f, 0x0c, 0x45, 0xd9, 0xf6, 0xf6, 0x42, 0xe1, 0xe9, 0xb5, 0xf9, 0xcb, 0xb2, 0x26, 0x9b, + 0xfa, 0xb4, 0xfb, 0x3b, 0x00, 0x00, 0xff, 0xff, 0xc4, 0x54, 0xcc, 0x7a, 0xf6, 0x05, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/graph_go_proto/graph.pb.go b/tensorflow/go/core/framework/graph_go_proto/graph.pb.go new file mode 100644 index 0000000..a8f824d --- /dev/null +++ b/tensorflow/go/core/framework/graph_go_proto/graph.pb.go @@ -0,0 +1,153 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/graph.proto + +package graph_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + function_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/function_go_proto" + node_def_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/node_def_go_proto" + versions_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Represents the graph of operations +type GraphDef struct { + Node []*node_def_go_proto.NodeDef `protobuf:"bytes,1,rep,name=node,proto3" json:"node,omitempty"` + // Compatibility versions of the graph. See core/public/version.h for version + // history. The GraphDef version is distinct from the TensorFlow version, and + // each release of TensorFlow will support a range of GraphDef versions. + Versions *versions_go_proto.VersionDef `protobuf:"bytes,4,opt,name=versions,proto3" json:"versions,omitempty"` + // Deprecated single version field; use versions above instead. Since all + // GraphDef changes before "versions" was introduced were forward + // compatible, this field is entirely ignored. + Version int32 `protobuf:"varint,3,opt,name=version,proto3" json:"version,omitempty"` // Deprecated: Do not use. + // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET. + // + // "library" provides user-defined functions. + // + // Naming: + // * library.function.name are in a flat namespace. + // NOTE: We may need to change it to be hierarchical to support + // different orgs. E.g., + // { "/google/nn", { ... }}, + // { "/google/vision", { ... }} + // { "/org_foo/module_bar", { ... }} + // map named_lib; + // * If node[i].op is the name of one function in "library", + // node[i] is deemed as a function call. Otherwise, node[i].op + // must be a primitive operation supported by the runtime. + // + // + // Function call semantics: + // + // * The callee may start execution as soon as some of its inputs + // are ready. The caller may want to use Tuple() mechanism to + // ensure all inputs are ready in the same time. + // + // * The consumer of return values may start executing as soon as + // the return values the consumer depends on are ready. The + // consumer may want to use Tuple() mechanism to ensure the + // consumer does not start until all return values of the callee + // function are ready. + Library *function_go_proto.FunctionDefLibrary `protobuf:"bytes,2,opt,name=library,proto3" json:"library,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphDef) Reset() { *m = GraphDef{} } +func (m *GraphDef) String() string { return proto.CompactTextString(m) } +func (*GraphDef) ProtoMessage() {} +func (*GraphDef) Descriptor() ([]byte, []int) { + return fileDescriptor_c7b29295d3bc875a, []int{0} +} + +func (m *GraphDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphDef.Unmarshal(m, b) +} +func (m *GraphDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphDef.Marshal(b, m, deterministic) +} +func (m *GraphDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphDef.Merge(m, src) +} +func (m *GraphDef) XXX_Size() int { + return xxx_messageInfo_GraphDef.Size(m) +} +func (m *GraphDef) XXX_DiscardUnknown() { + xxx_messageInfo_GraphDef.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphDef proto.InternalMessageInfo + +func (m *GraphDef) GetNode() []*node_def_go_proto.NodeDef { + if m != nil { + return m.Node + } + return nil +} + +func (m *GraphDef) GetVersions() *versions_go_proto.VersionDef { + if m != nil { + return m.Versions + } + return nil +} + +// Deprecated: Do not use. +func (m *GraphDef) GetVersion() int32 { + if m != nil { + return m.Version + } + return 0 +} + +func (m *GraphDef) GetLibrary() *function_go_proto.FunctionDefLibrary { + if m != nil { + return m.Library + } + return nil +} + +func init() { + proto.RegisterType((*GraphDef)(nil), "tensorflow.GraphDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/graph.proto", fileDescriptor_c7b29295d3bc875a) +} + +var fileDescriptor_c7b29295d3bc875a = []byte{ + // 274 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0xd1, 0x4d, 0x4b, 0xf3, 0x40, + 0x10, 0x07, 0x70, 0xb6, 0xed, 0xf3, 0xb4, 0x6c, 0x6e, 0x2b, 0xc8, 0x22, 0x22, 0x41, 0x10, 0x73, + 0x4a, 0x20, 0x5e, 0x3c, 0x87, 0xa0, 0x97, 0x22, 0x25, 0x07, 0x0f, 0x5e, 0x42, 0x5e, 0x66, 0xb7, + 0xc1, 0x76, 0xa7, 0x4c, 0x52, 0x8b, 0x7e, 0x39, 0xbf, 0x96, 0x47, 0xc9, 0xa6, 0x69, 0x73, 0xa8, + 0x7a, 0x1b, 0x76, 0x7f, 0xf3, 0x67, 0x67, 0x96, 0xdf, 0x34, 0x60, 0x6a, 0x24, 0xb5, 0xc2, 0x5d, + 0x50, 0x20, 0x41, 0xa0, 0x28, 0x5b, 0xc3, 0x0e, 0xe9, 0x35, 0xd0, 0x94, 0x6d, 0x96, 0xfe, 0x86, + 0xb0, 0x41, 0xc1, 0x8f, 0xec, 0xc2, 0xfb, 0xb9, 0x45, 0x6d, 0x4d, 0xd1, 0x54, 0x68, 0xba, 0xae, + 0xdf, 0xa4, 0xc1, 0x12, 0xd2, 0x12, 0xd4, 0xdf, 0xf2, 0x0d, 0xa8, 0xae, 0xd0, 0xd4, 0x9d, 0xbc, + 0xfe, 0x64, 0x7c, 0xf6, 0xd8, 0xbe, 0x2c, 0x06, 0x25, 0x6e, 0xf9, 0xa4, 0x0d, 0x92, 0xcc, 0x1d, + 0x7b, 0x4e, 0x78, 0xe6, 0x1f, 0x53, 0xfc, 0x27, 0x2c, 0x21, 0x06, 0x95, 0x58, 0x20, 0x42, 0x3e, + 0xeb, 0x73, 0xe4, 0xc4, 0x65, 0x9e, 0x13, 0x9e, 0x0f, 0xf1, 0x73, 0x77, 0xd7, 0xfa, 0x83, 0x13, + 0x97, 0x7c, 0xba, 0xaf, 0xe5, 0xd8, 0x65, 0xde, 0xbf, 0x68, 0x24, 0x59, 0xd2, 0x1f, 0x89, 0x7b, + 0x3e, 0x5d, 0x55, 0x39, 0x65, 0xf4, 0x2e, 0x47, 0x36, 0xf0, 0x6a, 0x18, 0xf8, 0xb0, 0x5f, 0x44, + 0x0c, 0x6a, 0xde, 0xa9, 0xa4, 0xe7, 0xd1, 0x07, 0x97, 0x48, 0x7a, 0xa8, 0x0f, 0xc3, 0x46, 0x8e, + 0x1d, 0x6d, 0xd1, 0x4e, 0x5a, 0x2f, 0xd8, 0xcb, 0x5c, 0x57, 0xcd, 0x72, 0x9b, 0xfb, 0x05, 0xae, + 0x83, 0xc1, 0x86, 0x4e, 0x97, 0x1a, 0x4f, 0xfe, 0x60, 0xaa, 0x31, 0xb5, 0xab, 0xfb, 0x62, 0x2c, + 0xff, 0x6f, 0xab, 0xbb, 0xef, 0x00, 0x00, 0x00, 0xff, 0xff, 0x03, 0x37, 0xd9, 0xce, 0xf7, 0x01, + 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/graph_transfer_info_go_proto/graph_transfer_info.pb.go b/tensorflow/go/core/framework/graph_transfer_info_go_proto/graph_transfer_info.pb.go new file mode 100644 index 0000000..a92db17 --- /dev/null +++ b/tensorflow/go/core/framework/graph_transfer_info_go_proto/graph_transfer_info.pb.go @@ -0,0 +1,611 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/graph_transfer_info.proto + +package graph_transfer_info_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type GraphTransferInfo_Destination int32 + +const ( + GraphTransferInfo_NOP GraphTransferInfo_Destination = 0 + GraphTransferInfo_HEXAGON GraphTransferInfo_Destination = 1 +) + +var GraphTransferInfo_Destination_name = map[int32]string{ + 0: "NOP", + 1: "HEXAGON", +} + +var GraphTransferInfo_Destination_value = map[string]int32{ + "NOP": 0, + "HEXAGON": 1, +} + +func (x GraphTransferInfo_Destination) String() string { + return proto.EnumName(GraphTransferInfo_Destination_name, int32(x)) +} + +func (GraphTransferInfo_Destination) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{7, 0} +} + +type GraphTransferNodeInput struct { + NodeId int32 `protobuf:"varint,1,opt,name=node_id,json=nodeId,proto3" json:"node_id,omitempty"` + OutputPort int32 `protobuf:"varint,2,opt,name=output_port,json=outputPort,proto3" json:"output_port,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferNodeInput) Reset() { *m = GraphTransferNodeInput{} } +func (m *GraphTransferNodeInput) String() string { return proto.CompactTextString(m) } +func (*GraphTransferNodeInput) ProtoMessage() {} +func (*GraphTransferNodeInput) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{0} +} + +func (m *GraphTransferNodeInput) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferNodeInput.Unmarshal(m, b) +} +func (m *GraphTransferNodeInput) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferNodeInput.Marshal(b, m, deterministic) +} +func (m *GraphTransferNodeInput) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferNodeInput.Merge(m, src) +} +func (m *GraphTransferNodeInput) XXX_Size() int { + return xxx_messageInfo_GraphTransferNodeInput.Size(m) +} +func (m *GraphTransferNodeInput) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferNodeInput.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferNodeInput proto.InternalMessageInfo + +func (m *GraphTransferNodeInput) GetNodeId() int32 { + if m != nil { + return m.NodeId + } + return 0 +} + +func (m *GraphTransferNodeInput) GetOutputPort() int32 { + if m != nil { + return m.OutputPort + } + return 0 +} + +type GraphTransferNodeInfo struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + NodeId int32 `protobuf:"varint,2,opt,name=node_id,json=nodeId,proto3" json:"node_id,omitempty"` + TypeName string `protobuf:"bytes,3,opt,name=type_name,json=typeName,proto3" json:"type_name,omitempty"` + SocOpId int32 `protobuf:"varint,4,opt,name=soc_op_id,json=socOpId,proto3" json:"soc_op_id,omitempty"` + PaddingId int32 `protobuf:"varint,5,opt,name=padding_id,json=paddingId,proto3" json:"padding_id,omitempty"` + InputCount int32 `protobuf:"varint,6,opt,name=input_count,json=inputCount,proto3" json:"input_count,omitempty"` + OutputCount int32 `protobuf:"varint,7,opt,name=output_count,json=outputCount,proto3" json:"output_count,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferNodeInfo) Reset() { *m = GraphTransferNodeInfo{} } +func (m *GraphTransferNodeInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferNodeInfo) ProtoMessage() {} +func (*GraphTransferNodeInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{1} +} + +func (m *GraphTransferNodeInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferNodeInfo.Unmarshal(m, b) +} +func (m *GraphTransferNodeInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferNodeInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferNodeInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferNodeInfo.Merge(m, src) +} +func (m *GraphTransferNodeInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferNodeInfo.Size(m) +} +func (m *GraphTransferNodeInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferNodeInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferNodeInfo proto.InternalMessageInfo + +func (m *GraphTransferNodeInfo) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *GraphTransferNodeInfo) GetNodeId() int32 { + if m != nil { + return m.NodeId + } + return 0 +} + +func (m *GraphTransferNodeInfo) GetTypeName() string { + if m != nil { + return m.TypeName + } + return "" +} + +func (m *GraphTransferNodeInfo) GetSocOpId() int32 { + if m != nil { + return m.SocOpId + } + return 0 +} + +func (m *GraphTransferNodeInfo) GetPaddingId() int32 { + if m != nil { + return m.PaddingId + } + return 0 +} + +func (m *GraphTransferNodeInfo) GetInputCount() int32 { + if m != nil { + return m.InputCount + } + return 0 +} + +func (m *GraphTransferNodeInfo) GetOutputCount() int32 { + if m != nil { + return m.OutputCount + } + return 0 +} + +type GraphTransferConstNodeInfo struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + NodeId int32 `protobuf:"varint,2,opt,name=node_id,json=nodeId,proto3" json:"node_id,omitempty"` + Shape []int64 `protobuf:"varint,3,rep,packed,name=shape,proto3" json:"shape,omitempty"` + Data []byte `protobuf:"bytes,4,opt,name=data,proto3" json:"data,omitempty"` + Dtype types_go_proto.DataType `protobuf:"varint,5,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferConstNodeInfo) Reset() { *m = GraphTransferConstNodeInfo{} } +func (m *GraphTransferConstNodeInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferConstNodeInfo) ProtoMessage() {} +func (*GraphTransferConstNodeInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{2} +} + +func (m *GraphTransferConstNodeInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferConstNodeInfo.Unmarshal(m, b) +} +func (m *GraphTransferConstNodeInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferConstNodeInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferConstNodeInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferConstNodeInfo.Merge(m, src) +} +func (m *GraphTransferConstNodeInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferConstNodeInfo.Size(m) +} +func (m *GraphTransferConstNodeInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferConstNodeInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferConstNodeInfo proto.InternalMessageInfo + +func (m *GraphTransferConstNodeInfo) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *GraphTransferConstNodeInfo) GetNodeId() int32 { + if m != nil { + return m.NodeId + } + return 0 +} + +func (m *GraphTransferConstNodeInfo) GetShape() []int64 { + if m != nil { + return m.Shape + } + return nil +} + +func (m *GraphTransferConstNodeInfo) GetData() []byte { + if m != nil { + return m.Data + } + return nil +} + +func (m *GraphTransferConstNodeInfo) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +type GraphTransferNodeInputInfo struct { + NodeId int32 `protobuf:"varint,1,opt,name=node_id,json=nodeId,proto3" json:"node_id,omitempty"` + NodeInput []*GraphTransferNodeInput `protobuf:"bytes,2,rep,name=node_input,json=nodeInput,proto3" json:"node_input,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferNodeInputInfo) Reset() { *m = GraphTransferNodeInputInfo{} } +func (m *GraphTransferNodeInputInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferNodeInputInfo) ProtoMessage() {} +func (*GraphTransferNodeInputInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{3} +} + +func (m *GraphTransferNodeInputInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferNodeInputInfo.Unmarshal(m, b) +} +func (m *GraphTransferNodeInputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferNodeInputInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferNodeInputInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferNodeInputInfo.Merge(m, src) +} +func (m *GraphTransferNodeInputInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferNodeInputInfo.Size(m) +} +func (m *GraphTransferNodeInputInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferNodeInputInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferNodeInputInfo proto.InternalMessageInfo + +func (m *GraphTransferNodeInputInfo) GetNodeId() int32 { + if m != nil { + return m.NodeId + } + return 0 +} + +func (m *GraphTransferNodeInputInfo) GetNodeInput() []*GraphTransferNodeInput { + if m != nil { + return m.NodeInput + } + return nil +} + +type GraphTransferNodeOutputInfo struct { + NodeId int32 `protobuf:"varint,1,opt,name=node_id,json=nodeId,proto3" json:"node_id,omitempty"` + MaxByteSize []int32 `protobuf:"varint,2,rep,packed,name=max_byte_size,json=maxByteSize,proto3" json:"max_byte_size,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferNodeOutputInfo) Reset() { *m = GraphTransferNodeOutputInfo{} } +func (m *GraphTransferNodeOutputInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferNodeOutputInfo) ProtoMessage() {} +func (*GraphTransferNodeOutputInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{4} +} + +func (m *GraphTransferNodeOutputInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferNodeOutputInfo.Unmarshal(m, b) +} +func (m *GraphTransferNodeOutputInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferNodeOutputInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferNodeOutputInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferNodeOutputInfo.Merge(m, src) +} +func (m *GraphTransferNodeOutputInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferNodeOutputInfo.Size(m) +} +func (m *GraphTransferNodeOutputInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferNodeOutputInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferNodeOutputInfo proto.InternalMessageInfo + +func (m *GraphTransferNodeOutputInfo) GetNodeId() int32 { + if m != nil { + return m.NodeId + } + return 0 +} + +func (m *GraphTransferNodeOutputInfo) GetMaxByteSize() []int32 { + if m != nil { + return m.MaxByteSize + } + return nil +} + +type GraphTransferGraphInputNodeInfo struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Shape []int64 `protobuf:"varint,2,rep,packed,name=shape,proto3" json:"shape,omitempty"` + Dtype types_go_proto.DataType `protobuf:"varint,3,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferGraphInputNodeInfo) Reset() { *m = GraphTransferGraphInputNodeInfo{} } +func (m *GraphTransferGraphInputNodeInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferGraphInputNodeInfo) ProtoMessage() {} +func (*GraphTransferGraphInputNodeInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{5} +} + +func (m *GraphTransferGraphInputNodeInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferGraphInputNodeInfo.Unmarshal(m, b) +} +func (m *GraphTransferGraphInputNodeInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferGraphInputNodeInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferGraphInputNodeInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferGraphInputNodeInfo.Merge(m, src) +} +func (m *GraphTransferGraphInputNodeInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferGraphInputNodeInfo.Size(m) +} +func (m *GraphTransferGraphInputNodeInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferGraphInputNodeInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferGraphInputNodeInfo proto.InternalMessageInfo + +func (m *GraphTransferGraphInputNodeInfo) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *GraphTransferGraphInputNodeInfo) GetShape() []int64 { + if m != nil { + return m.Shape + } + return nil +} + +func (m *GraphTransferGraphInputNodeInfo) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +type GraphTransferGraphOutputNodeInfo struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Shape []int64 `protobuf:"varint,2,rep,packed,name=shape,proto3" json:"shape,omitempty"` + Dtype types_go_proto.DataType `protobuf:"varint,3,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferGraphOutputNodeInfo) Reset() { *m = GraphTransferGraphOutputNodeInfo{} } +func (m *GraphTransferGraphOutputNodeInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferGraphOutputNodeInfo) ProtoMessage() {} +func (*GraphTransferGraphOutputNodeInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{6} +} + +func (m *GraphTransferGraphOutputNodeInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferGraphOutputNodeInfo.Unmarshal(m, b) +} +func (m *GraphTransferGraphOutputNodeInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferGraphOutputNodeInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferGraphOutputNodeInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferGraphOutputNodeInfo.Merge(m, src) +} +func (m *GraphTransferGraphOutputNodeInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferGraphOutputNodeInfo.Size(m) +} +func (m *GraphTransferGraphOutputNodeInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferGraphOutputNodeInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferGraphOutputNodeInfo proto.InternalMessageInfo + +func (m *GraphTransferGraphOutputNodeInfo) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *GraphTransferGraphOutputNodeInfo) GetShape() []int64 { + if m != nil { + return m.Shape + } + return nil +} + +func (m *GraphTransferGraphOutputNodeInfo) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +// Protocol buffer representing a handle to a tensorflow resource. Handles are +// not valid across executions, but can be serialized back and forth from within +// a single run. +type GraphTransferInfo struct { + NodeInfo []*GraphTransferNodeInfo `protobuf:"bytes,1,rep,name=node_info,json=nodeInfo,proto3" json:"node_info,omitempty"` + ConstNodeInfo []*GraphTransferConstNodeInfo `protobuf:"bytes,2,rep,name=const_node_info,json=constNodeInfo,proto3" json:"const_node_info,omitempty"` + NodeInputInfo []*GraphTransferNodeInputInfo `protobuf:"bytes,3,rep,name=node_input_info,json=nodeInputInfo,proto3" json:"node_input_info,omitempty"` + NodeOutputInfo []*GraphTransferNodeOutputInfo `protobuf:"bytes,4,rep,name=node_output_info,json=nodeOutputInfo,proto3" json:"node_output_info,omitempty"` + // Input Node parameters of transferred graph + GraphInputNodeInfo []*GraphTransferGraphInputNodeInfo `protobuf:"bytes,5,rep,name=graph_input_node_info,json=graphInputNodeInfo,proto3" json:"graph_input_node_info,omitempty"` + GraphOutputNodeInfo []*GraphTransferGraphOutputNodeInfo `protobuf:"bytes,6,rep,name=graph_output_node_info,json=graphOutputNodeInfo,proto3" json:"graph_output_node_info,omitempty"` + // Destination of graph transfer + Destination GraphTransferInfo_Destination `protobuf:"varint,7,opt,name=destination,proto3,enum=tensorflow.GraphTransferInfo_Destination" json:"destination,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphTransferInfo) Reset() { *m = GraphTransferInfo{} } +func (m *GraphTransferInfo) String() string { return proto.CompactTextString(m) } +func (*GraphTransferInfo) ProtoMessage() {} +func (*GraphTransferInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c3a1e773f26c9475, []int{7} +} + +func (m *GraphTransferInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphTransferInfo.Unmarshal(m, b) +} +func (m *GraphTransferInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphTransferInfo.Marshal(b, m, deterministic) +} +func (m *GraphTransferInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphTransferInfo.Merge(m, src) +} +func (m *GraphTransferInfo) XXX_Size() int { + return xxx_messageInfo_GraphTransferInfo.Size(m) +} +func (m *GraphTransferInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphTransferInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphTransferInfo proto.InternalMessageInfo + +func (m *GraphTransferInfo) GetNodeInfo() []*GraphTransferNodeInfo { + if m != nil { + return m.NodeInfo + } + return nil +} + +func (m *GraphTransferInfo) GetConstNodeInfo() []*GraphTransferConstNodeInfo { + if m != nil { + return m.ConstNodeInfo + } + return nil +} + +func (m *GraphTransferInfo) GetNodeInputInfo() []*GraphTransferNodeInputInfo { + if m != nil { + return m.NodeInputInfo + } + return nil +} + +func (m *GraphTransferInfo) GetNodeOutputInfo() []*GraphTransferNodeOutputInfo { + if m != nil { + return m.NodeOutputInfo + } + return nil +} + +func (m *GraphTransferInfo) GetGraphInputNodeInfo() []*GraphTransferGraphInputNodeInfo { + if m != nil { + return m.GraphInputNodeInfo + } + return nil +} + +func (m *GraphTransferInfo) GetGraphOutputNodeInfo() []*GraphTransferGraphOutputNodeInfo { + if m != nil { + return m.GraphOutputNodeInfo + } + return nil +} + +func (m *GraphTransferInfo) GetDestination() GraphTransferInfo_Destination { + if m != nil { + return m.Destination + } + return GraphTransferInfo_NOP +} + +func init() { + proto.RegisterEnum("tensorflow.GraphTransferInfo_Destination", GraphTransferInfo_Destination_name, GraphTransferInfo_Destination_value) + proto.RegisterType((*GraphTransferNodeInput)(nil), "tensorflow.GraphTransferNodeInput") + proto.RegisterType((*GraphTransferNodeInfo)(nil), "tensorflow.GraphTransferNodeInfo") + proto.RegisterType((*GraphTransferConstNodeInfo)(nil), "tensorflow.GraphTransferConstNodeInfo") + proto.RegisterType((*GraphTransferNodeInputInfo)(nil), "tensorflow.GraphTransferNodeInputInfo") + proto.RegisterType((*GraphTransferNodeOutputInfo)(nil), "tensorflow.GraphTransferNodeOutputInfo") + proto.RegisterType((*GraphTransferGraphInputNodeInfo)(nil), "tensorflow.GraphTransferGraphInputNodeInfo") + proto.RegisterType((*GraphTransferGraphOutputNodeInfo)(nil), "tensorflow.GraphTransferGraphOutputNodeInfo") + proto.RegisterType((*GraphTransferInfo)(nil), "tensorflow.GraphTransferInfo") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/graph_transfer_info.proto", fileDescriptor_c3a1e773f26c9475) +} + +var fileDescriptor_c3a1e773f26c9475 = []byte{ + // 675 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xb4, 0x55, 0xdb, 0x6e, 0xd3, 0x40, + 0x10, 0xc5, 0x71, 0x2e, 0xcd, 0xa4, 0x37, 0x4c, 0x5b, 0xa2, 0x56, 0xa8, 0xad, 0x11, 0x50, 0x2e, + 0x4a, 0xa4, 0xf6, 0x1d, 0xa9, 0x17, 0x54, 0x2a, 0xa4, 0x24, 0x2c, 0x7d, 0x40, 0x7d, 0xc0, 0xda, + 0xda, 0x6b, 0xd7, 0x82, 0xec, 0x5a, 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0000000..24a0116 --- /dev/null +++ b/tensorflow/go/core/framework/kernel_def_go_proto/kernel_def.pb.go @@ -0,0 +1,238 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/kernel_def.proto + +package kernel_def_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type KernelDef struct { + // Must match the name of an Op. + Op string `protobuf:"bytes,1,opt,name=op,proto3" json:"op,omitempty"` + // Type of device this kernel runs on. + DeviceType string `protobuf:"bytes,2,opt,name=device_type,json=deviceType,proto3" json:"device_type,omitempty"` + Constraint []*KernelDef_AttrConstraint `protobuf:"bytes,3,rep,name=constraint,proto3" json:"constraint,omitempty"` + // Names of the Op's input_/output_args that reside in host memory + // instead of device memory. + HostMemoryArg []string `protobuf:"bytes,4,rep,name=host_memory_arg,json=hostMemoryArg,proto3" json:"host_memory_arg,omitempty"` + // This allows experimental kernels to be registered for an op that + // won't be used unless the user specifies a "_kernel" attr with + // value matching this. + Label string `protobuf:"bytes,5,opt,name=label,proto3" json:"label,omitempty"` + // Prioritization of kernel amongst different devices. By default we assume + // priority is 0. The higher the priority the better. By default (i.e. if + // this is not set), we prefer GPU kernels over CPU. + Priority int32 `protobuf:"varint,6,opt,name=priority,proto3" json:"priority,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *KernelDef) Reset() { *m = KernelDef{} } +func (m *KernelDef) String() string { return proto.CompactTextString(m) } +func (*KernelDef) ProtoMessage() {} +func (*KernelDef) Descriptor() ([]byte, []int) { + return fileDescriptor_18794e085ea7671a, []int{0} +} + +func (m *KernelDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_KernelDef.Unmarshal(m, b) +} +func (m *KernelDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_KernelDef.Marshal(b, m, deterministic) +} +func (m *KernelDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_KernelDef.Merge(m, src) +} +func (m *KernelDef) XXX_Size() int { + return xxx_messageInfo_KernelDef.Size(m) +} +func (m *KernelDef) XXX_DiscardUnknown() { + xxx_messageInfo_KernelDef.DiscardUnknown(m) +} + +var xxx_messageInfo_KernelDef proto.InternalMessageInfo + +func (m *KernelDef) GetOp() string { + if m != nil { + return m.Op + } + return "" +} + +func (m *KernelDef) GetDeviceType() string { + if m != nil { + return m.DeviceType + } + return "" +} + +func (m *KernelDef) GetConstraint() []*KernelDef_AttrConstraint { + if m != nil { + return m.Constraint + } + return nil +} + +func (m *KernelDef) GetHostMemoryArg() []string { + if m != nil { + return m.HostMemoryArg + } + return nil +} + +func (m *KernelDef) GetLabel() string { + if m != nil { + return m.Label + } + return "" +} + +func (m *KernelDef) GetPriority() int32 { + if m != nil { + return m.Priority + } + return 0 +} + +type KernelDef_AttrConstraint struct { + // Name of an attr from the Op. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // A list of values that this kernel supports for this attr. + // Like OpDef.AttrDef.allowed_values, except for kernels instead of Ops. + AllowedValues *attr_value_go_proto.AttrValue `protobuf:"bytes,2,opt,name=allowed_values,json=allowedValues,proto3" json:"allowed_values,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *KernelDef_AttrConstraint) Reset() { *m = KernelDef_AttrConstraint{} } +func (m *KernelDef_AttrConstraint) String() string { return proto.CompactTextString(m) } +func (*KernelDef_AttrConstraint) ProtoMessage() {} +func (*KernelDef_AttrConstraint) Descriptor() ([]byte, []int) { + return fileDescriptor_18794e085ea7671a, []int{0, 0} +} + +func (m *KernelDef_AttrConstraint) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_KernelDef_AttrConstraint.Unmarshal(m, b) +} +func (m *KernelDef_AttrConstraint) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_KernelDef_AttrConstraint.Marshal(b, m, deterministic) +} +func (m *KernelDef_AttrConstraint) XXX_Merge(src proto.Message) { + xxx_messageInfo_KernelDef_AttrConstraint.Merge(m, src) +} +func (m *KernelDef_AttrConstraint) XXX_Size() int { + return xxx_messageInfo_KernelDef_AttrConstraint.Size(m) +} +func (m *KernelDef_AttrConstraint) XXX_DiscardUnknown() { + xxx_messageInfo_KernelDef_AttrConstraint.DiscardUnknown(m) +} + +var xxx_messageInfo_KernelDef_AttrConstraint proto.InternalMessageInfo + +func (m *KernelDef_AttrConstraint) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *KernelDef_AttrConstraint) GetAllowedValues() *attr_value_go_proto.AttrValue { + if m != nil { + return m.AllowedValues + } + return nil +} + +// A collection of KernelDefs +type KernelList struct { + Kernel []*KernelDef `protobuf:"bytes,1,rep,name=kernel,proto3" json:"kernel,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *KernelList) Reset() { *m = KernelList{} } +func (m *KernelList) String() string { return proto.CompactTextString(m) } +func (*KernelList) ProtoMessage() {} +func (*KernelList) Descriptor() ([]byte, []int) { + return fileDescriptor_18794e085ea7671a, []int{1} +} + +func (m *KernelList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_KernelList.Unmarshal(m, b) +} +func (m *KernelList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_KernelList.Marshal(b, m, deterministic) +} +func (m *KernelList) XXX_Merge(src proto.Message) { + xxx_messageInfo_KernelList.Merge(m, src) +} +func (m *KernelList) XXX_Size() int { + return xxx_messageInfo_KernelList.Size(m) +} +func (m *KernelList) XXX_DiscardUnknown() { + xxx_messageInfo_KernelList.DiscardUnknown(m) +} + +var xxx_messageInfo_KernelList proto.InternalMessageInfo + +func (m *KernelList) GetKernel() []*KernelDef { + if m != nil { + return m.Kernel + } + return nil +} + +func init() { + proto.RegisterType((*KernelDef)(nil), "tensorflow.KernelDef") + proto.RegisterType((*KernelDef_AttrConstraint)(nil), "tensorflow.KernelDef.AttrConstraint") + proto.RegisterType((*KernelList)(nil), "tensorflow.KernelList") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/kernel_def.proto", fileDescriptor_18794e085ea7671a) +} + +var fileDescriptor_18794e085ea7671a = []byte{ + // 365 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x52, 0x41, 0xcb, 0xda, 0x40, + 0x10, 0x65, 0xe3, 0xa7, 0xd4, 0x11, 0x15, 0x96, 0x0a, 0x8b, 0x97, 0x06, 0x29, 0x25, 0x14, 0x9a, + 0x80, 0x3d, 0xb6, 0x17, 0xad, 0xb7, 0xb6, 0x60, 0x43, 0xe9, 0xa1, 0x97, 0xb0, 0x89, 0x93, 0x18, + 0x4c, 0x32, 0x61, 0xb3, 0x2a, 0x39, 0xf7, 0x8f, 0xf5, 0x67, 0xf5, 0x58, 0xb2, 0x91, 0x98, 0x82, + 0xfd, 0x6e, 0x33, 0xb3, 0x6f, 0x66, 0xf6, 0xbd, 0x37, 0xf0, 0x56, 0x63, 0x51, 0x91, 0x8a, 0x33, + 0xba, 0x7a, 0x11, 0x29, 0xf4, 0x62, 0x25, 0x73, 0xbc, 0x92, 0x3a, 0x79, 0x27, 0x54, 0x05, 0x66, + 0xc1, 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0xc5, 0x43, 0xab, 0x4d, 0x65, 0x98, 0x4e, 0xd6, 0x8b, 0x3e, 0x93, 0x66, + 0xce, 0x8f, 0xe6, 0xd5, 0x9f, 0xde, 0xc0, 0x26, 0xab, 0x56, 0x1f, 0x00, 0x5a, 0x96, 0x5f, 0xd2, + 0x4a, 0xf3, 0x77, 0x30, 0x6a, 0xcd, 0x11, 0xcc, 0xa8, 0xb1, 0x78, 0xa8, 0x86, 0x7f, 0x03, 0x6d, + 0x7f, 0x31, 0x10, 0xa4, 0x92, 0x3e, 0xa8, 0x73, 0x6b, 0x3b, 0xef, 0xf0, 0xfb, 0xc6, 0xac, 0x6a, + 0xcf, 0x7e, 0x7e, 0x4b, 0x52, 0x7d, 0x3c, 0x87, 0x6e, 0x44, 0xb9, 0xd7, 0xb3, 0xf9, 0x71, 0x98, + 0xd0, 0xff, 0xef, 0x26, 0x48, 0x28, 0x30, 0x27, 0xf0, 0x87, 0xb1, 0x70, 0x64, 0xa2, 0xf7, 0x7f, + 0x03, 0x00, 0x00, 0xff, 0xff, 0xf3, 0x5b, 0xc5, 0x79, 0x72, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/log_memory_go_proto/log_memory.pb.go b/tensorflow/go/core/framework/log_memory_go_proto/log_memory.pb.go new file mode 100644 index 0000000..9bc7d46 --- /dev/null +++ b/tensorflow/go/core/framework/log_memory_go_proto/log_memory.pb.go @@ -0,0 +1,457 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/log_memory.proto + +package log_memory_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_description_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_description_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type MemoryLogStep struct { + // Process-unique step id. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Handle describing the feeds and fetches of the step. + Handle string `protobuf:"bytes,2,opt,name=handle,proto3" json:"handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryLogStep) Reset() { *m = MemoryLogStep{} } +func (m *MemoryLogStep) String() string { return proto.CompactTextString(m) } +func (*MemoryLogStep) ProtoMessage() {} +func (*MemoryLogStep) Descriptor() ([]byte, []int) { + return fileDescriptor_4f52e83a3ef81427, []int{0} +} + +func (m *MemoryLogStep) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryLogStep.Unmarshal(m, b) +} +func (m *MemoryLogStep) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryLogStep.Marshal(b, m, deterministic) +} +func (m *MemoryLogStep) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryLogStep.Merge(m, src) +} +func (m *MemoryLogStep) XXX_Size() int { + return xxx_messageInfo_MemoryLogStep.Size(m) +} +func (m *MemoryLogStep) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryLogStep.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryLogStep proto.InternalMessageInfo + +func (m *MemoryLogStep) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *MemoryLogStep) GetHandle() string { + if m != nil { + return m.Handle + } + return "" +} + +type MemoryLogTensorAllocation struct { + // Process-unique step id. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Name of the kernel making the allocation as set in GraphDef, + // e.g., "affine2/weights/Assign". + KernelName string `protobuf:"bytes,2,opt,name=kernel_name,json=kernelName,proto3" json:"kernel_name,omitempty"` + // Allocated tensor details. + Tensor *tensor_description_go_proto.TensorDescription `protobuf:"bytes,3,opt,name=tensor,proto3" json:"tensor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryLogTensorAllocation) Reset() { *m = MemoryLogTensorAllocation{} } +func (m *MemoryLogTensorAllocation) String() string { return proto.CompactTextString(m) } +func (*MemoryLogTensorAllocation) ProtoMessage() {} +func (*MemoryLogTensorAllocation) Descriptor() ([]byte, []int) { + return fileDescriptor_4f52e83a3ef81427, []int{1} +} + +func (m *MemoryLogTensorAllocation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryLogTensorAllocation.Unmarshal(m, b) +} +func (m *MemoryLogTensorAllocation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryLogTensorAllocation.Marshal(b, m, deterministic) +} +func (m *MemoryLogTensorAllocation) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryLogTensorAllocation.Merge(m, src) +} +func (m *MemoryLogTensorAllocation) XXX_Size() int { + return xxx_messageInfo_MemoryLogTensorAllocation.Size(m) +} +func (m *MemoryLogTensorAllocation) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryLogTensorAllocation.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryLogTensorAllocation proto.InternalMessageInfo + +func (m *MemoryLogTensorAllocation) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *MemoryLogTensorAllocation) GetKernelName() string { + if m != nil { + return m.KernelName + } + return "" +} + +func (m *MemoryLogTensorAllocation) GetTensor() *tensor_description_go_proto.TensorDescription { + if m != nil { + return m.Tensor + } + return nil +} + +type MemoryLogTensorDeallocation struct { + // Id of the tensor buffer being deallocated, used to match to a + // corresponding allocation. + AllocationId int64 `protobuf:"varint,1,opt,name=allocation_id,json=allocationId,proto3" json:"allocation_id,omitempty"` + // Name of the allocator used. + AllocatorName string `protobuf:"bytes,2,opt,name=allocator_name,json=allocatorName,proto3" json:"allocator_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryLogTensorDeallocation) Reset() { *m = MemoryLogTensorDeallocation{} } +func (m *MemoryLogTensorDeallocation) String() string { return proto.CompactTextString(m) } +func (*MemoryLogTensorDeallocation) ProtoMessage() {} +func (*MemoryLogTensorDeallocation) Descriptor() ([]byte, []int) { + return fileDescriptor_4f52e83a3ef81427, []int{2} +} + +func (m *MemoryLogTensorDeallocation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryLogTensorDeallocation.Unmarshal(m, b) +} +func (m *MemoryLogTensorDeallocation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryLogTensorDeallocation.Marshal(b, m, deterministic) +} +func (m *MemoryLogTensorDeallocation) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryLogTensorDeallocation.Merge(m, src) +} +func (m *MemoryLogTensorDeallocation) XXX_Size() int { + return xxx_messageInfo_MemoryLogTensorDeallocation.Size(m) +} +func (m *MemoryLogTensorDeallocation) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryLogTensorDeallocation.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryLogTensorDeallocation proto.InternalMessageInfo + +func (m *MemoryLogTensorDeallocation) GetAllocationId() int64 { + if m != nil { + return m.AllocationId + } + return 0 +} + +func (m *MemoryLogTensorDeallocation) GetAllocatorName() string { + if m != nil { + return m.AllocatorName + } + return "" +} + +type MemoryLogTensorOutput struct { + // Process-unique step id. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Name of the kernel producing an output as set in GraphDef, e.g., + // "affine2/weights/Assign". + KernelName string `protobuf:"bytes,2,opt,name=kernel_name,json=kernelName,proto3" json:"kernel_name,omitempty"` + // Index of the output being set. + Index int32 `protobuf:"varint,3,opt,name=index,proto3" json:"index,omitempty"` + // Output tensor details. + Tensor *tensor_description_go_proto.TensorDescription `protobuf:"bytes,4,opt,name=tensor,proto3" json:"tensor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryLogTensorOutput) Reset() { *m = MemoryLogTensorOutput{} } +func (m *MemoryLogTensorOutput) String() string { return proto.CompactTextString(m) } +func (*MemoryLogTensorOutput) ProtoMessage() {} +func (*MemoryLogTensorOutput) Descriptor() ([]byte, []int) { + return fileDescriptor_4f52e83a3ef81427, []int{3} +} + +func (m *MemoryLogTensorOutput) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryLogTensorOutput.Unmarshal(m, b) +} +func (m *MemoryLogTensorOutput) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryLogTensorOutput.Marshal(b, m, deterministic) +} +func (m *MemoryLogTensorOutput) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryLogTensorOutput.Merge(m, src) +} +func (m *MemoryLogTensorOutput) XXX_Size() int { + return xxx_messageInfo_MemoryLogTensorOutput.Size(m) +} +func (m *MemoryLogTensorOutput) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryLogTensorOutput.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryLogTensorOutput proto.InternalMessageInfo + +func (m *MemoryLogTensorOutput) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *MemoryLogTensorOutput) GetKernelName() string { + if m != nil { + return m.KernelName + } + return "" +} + +func (m *MemoryLogTensorOutput) GetIndex() int32 { + if m != nil { + return m.Index + } + return 0 +} + +func (m *MemoryLogTensorOutput) GetTensor() *tensor_description_go_proto.TensorDescription { + if m != nil { + return m.Tensor + } + return nil +} + +type MemoryLogRawAllocation struct { + // Process-unique step id. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Name of the operation making the allocation. + Operation string `protobuf:"bytes,2,opt,name=operation,proto3" json:"operation,omitempty"` + // Number of bytes in the allocation. + NumBytes int64 `protobuf:"varint,3,opt,name=num_bytes,json=numBytes,proto3" json:"num_bytes,omitempty"` + // Address of the allocation. + Ptr uint64 `protobuf:"varint,4,opt,name=ptr,proto3" json:"ptr,omitempty"` + // Id of the tensor buffer being allocated, used to match to a + // corresponding deallocation. + AllocationId int64 `protobuf:"varint,5,opt,name=allocation_id,json=allocationId,proto3" json:"allocation_id,omitempty"` + // Name of the allocator used. + AllocatorName string `protobuf:"bytes,6,opt,name=allocator_name,json=allocatorName,proto3" json:"allocator_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryLogRawAllocation) Reset() { *m = MemoryLogRawAllocation{} } +func (m *MemoryLogRawAllocation) String() string { return proto.CompactTextString(m) } +func (*MemoryLogRawAllocation) ProtoMessage() {} +func (*MemoryLogRawAllocation) Descriptor() ([]byte, []int) { + return fileDescriptor_4f52e83a3ef81427, []int{4} +} + +func (m *MemoryLogRawAllocation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryLogRawAllocation.Unmarshal(m, b) +} +func (m *MemoryLogRawAllocation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryLogRawAllocation.Marshal(b, m, deterministic) +} +func (m *MemoryLogRawAllocation) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryLogRawAllocation.Merge(m, src) +} +func (m *MemoryLogRawAllocation) XXX_Size() int { + return xxx_messageInfo_MemoryLogRawAllocation.Size(m) +} +func (m *MemoryLogRawAllocation) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryLogRawAllocation.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryLogRawAllocation proto.InternalMessageInfo + +func (m *MemoryLogRawAllocation) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *MemoryLogRawAllocation) GetOperation() string { + if m != nil { + return m.Operation + } + return "" +} + +func (m *MemoryLogRawAllocation) GetNumBytes() int64 { + if m != nil { + return m.NumBytes + } + return 0 +} + +func (m *MemoryLogRawAllocation) GetPtr() uint64 { + if m != nil { + return m.Ptr + } + return 0 +} + +func (m *MemoryLogRawAllocation) GetAllocationId() int64 { + if m != nil { + return m.AllocationId + } + return 0 +} + +func (m *MemoryLogRawAllocation) GetAllocatorName() string { + if m != nil { + return m.AllocatorName + } + return "" +} + +type MemoryLogRawDeallocation struct { + // Process-unique step id. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Name of the operation making the deallocation. + Operation string `protobuf:"bytes,2,opt,name=operation,proto3" json:"operation,omitempty"` + // Id of the tensor buffer being deallocated, used to match to a + // corresponding allocation. + AllocationId int64 `protobuf:"varint,3,opt,name=allocation_id,json=allocationId,proto3" json:"allocation_id,omitempty"` + // Name of the allocator used. + AllocatorName string `protobuf:"bytes,4,opt,name=allocator_name,json=allocatorName,proto3" json:"allocator_name,omitempty"` + // True if the deallocation is queued and will be performed later, + // e.g. for GPU lazy freeing of buffers. + Deferred bool `protobuf:"varint,5,opt,name=deferred,proto3" json:"deferred,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryLogRawDeallocation) Reset() { *m = MemoryLogRawDeallocation{} } +func (m *MemoryLogRawDeallocation) String() string { return proto.CompactTextString(m) } +func (*MemoryLogRawDeallocation) ProtoMessage() {} +func (*MemoryLogRawDeallocation) Descriptor() ([]byte, []int) { + return fileDescriptor_4f52e83a3ef81427, []int{5} +} + +func (m *MemoryLogRawDeallocation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryLogRawDeallocation.Unmarshal(m, b) +} +func (m *MemoryLogRawDeallocation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryLogRawDeallocation.Marshal(b, m, deterministic) +} +func (m *MemoryLogRawDeallocation) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryLogRawDeallocation.Merge(m, src) +} +func (m *MemoryLogRawDeallocation) XXX_Size() int { + return xxx_messageInfo_MemoryLogRawDeallocation.Size(m) +} +func (m *MemoryLogRawDeallocation) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryLogRawDeallocation.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryLogRawDeallocation proto.InternalMessageInfo + +func (m *MemoryLogRawDeallocation) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *MemoryLogRawDeallocation) GetOperation() string { + if m != nil { + return m.Operation + } + return "" +} + +func (m *MemoryLogRawDeallocation) GetAllocationId() int64 { + if m != nil { + return m.AllocationId + } + return 0 +} + +func (m *MemoryLogRawDeallocation) GetAllocatorName() string { + if m != nil { + return m.AllocatorName + } + return "" +} + +func (m *MemoryLogRawDeallocation) GetDeferred() bool { + if m != nil { + return m.Deferred + } + return false +} + +func init() { + proto.RegisterType((*MemoryLogStep)(nil), "tensorflow.MemoryLogStep") + proto.RegisterType((*MemoryLogTensorAllocation)(nil), "tensorflow.MemoryLogTensorAllocation") + proto.RegisterType((*MemoryLogTensorDeallocation)(nil), "tensorflow.MemoryLogTensorDeallocation") + proto.RegisterType((*MemoryLogTensorOutput)(nil), "tensorflow.MemoryLogTensorOutput") + proto.RegisterType((*MemoryLogRawAllocation)(nil), "tensorflow.MemoryLogRawAllocation") + proto.RegisterType((*MemoryLogRawDeallocation)(nil), "tensorflow.MemoryLogRawDeallocation") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/log_memory.proto", fileDescriptor_4f52e83a3ef81427) +} + +var fileDescriptor_4f52e83a3ef81427 = []byte{ + // 447 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xa4, 0x94, 0xc1, 0x6e, 0xd3, 0x40, + 0x10, 0x86, 0xb5, 0x38, 0x31, 0xc9, 0x94, 0x00, 0x5a, 0x41, 0x31, 0x2d, 0x88, 0x28, 0x08, 0x29, + 0xe2, 0x90, 0x48, 0x45, 0xdc, 0x21, 0xea, 0xa5, 0x52, 0x81, 0xb2, 0x70, 0xe2, 0x62, 0x39, 0xf1, + 0xc4, 0xb5, 0xea, 0xdd, 0xb1, 0xd6, 0x6b, 0x85, 0x9e, 0xb9, 0xf2, 0x0c, 0xbc, 0x07, 0xaf, 0xc0, + 0x13, 0x71, 0x44, 0xf6, 0x06, 0xaf, 0x69, 0x13, 0x29, 0xc0, 0x6d, 0xff, 0xf1, 0xee, 0xcc, 0xf7, + 0xcf, 0x68, 0x0c, 0xcf, 0x0d, 0xaa, 0x82, 0xf4, 0x32, 0xa3, 0xd5, 0x74, 0x41, 0x1a, 0xa7, 0x4b, + 0x1d, 0x49, 0x5c, 0x91, 0xbe, 0x98, 0x66, 0x94, 0x84, 0x12, 0x25, 0xe9, 0xcb, 0x49, 0xae, 0xc9, + 0x10, 0x07, 0x77, 0xf7, 0xe0, 0x68, 0xfb, 0x3b, 0xfb, 0x25, 0x8c, 0xb1, 0x58, 0xe8, 0x34, 0x37, + 0x29, 0x29, 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0xf1, 0x83, 0xc1, 0x7e, 0x03, 0x28, 0xa2, 0xd5, 0x2e, 0x73, 0x79, + 0x04, 0x7d, 0xca, 0x51, 0xd7, 0xb7, 0xd6, 0x7c, 0x2e, 0xc0, 0x0f, 0xa1, 0xaf, 0x4a, 0x19, 0xce, + 0x2f, 0x0d, 0x16, 0x35, 0xa2, 0x27, 0x7a, 0xaa, 0x94, 0xb3, 0x4a, 0xf3, 0xbb, 0xe0, 0xe5, 0xc6, + 0x22, 0x76, 0x44, 0x75, 0xbc, 0xde, 0xed, 0xee, 0x4e, 0xdd, 0xf6, 0x37, 0x75, 0xfb, 0x3b, 0x83, + 0xa0, 0x6d, 0xe6, 0x8f, 0xb1, 0xfe, 0xa3, 0x9d, 0x6b, 0x7c, 0xde, 0x4e, 0x7c, 0x9d, 0x0d, 0x7c, + 0xfc, 0x00, 0x7a, 0x31, 0x2e, 0x51, 0x6b, 0xb4, 0x36, 0x7b, 0xa2, 0xd1, 0xb3, 0x2f, 0x0c, 0x02, + 0xd2, 0x49, 0x7b, 0x6a, 0xcd, 0x6e, 0xce, 0xee, 0x9c, 0x52, 0x62, 0x8d, 0x9d, 0x55, 0x2b, 0x59, + 0x9c, 0xb1, 0x4f, 0xef, 0x93, 0xd4, 0x9c, 0x97, 0xf3, 0xc9, 0x82, 0xe4, 0xb4, 0xb5, 0xd4, 0x9b, + 0x8f, 0x09, 0x6d, 0xff, 0x4b, 0x84, 0x09, 0x85, 0xf5, 0xa2, 0xff, 0x64, 0x6c, 0xee, 0xd7, 0xa7, + 0x17, 0xbf, 0x02, 0x00, 0x00, 0xff, 0xff, 0x9b, 0xac, 0xfa, 0xb2, 0x60, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/node_def_go_proto/node_def.pb.go b/tensorflow/go/core/framework/node_def_go_proto/node_def.pb.go new file mode 100644 index 0000000..1288e22 --- /dev/null +++ b/tensorflow/go/core/framework/node_def_go_proto/node_def.pb.go @@ -0,0 +1,243 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/node_def.proto + +package node_def_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type NodeDef struct { + // The name given to this operator. Used for naming inputs, + // logging, visualization, etc. Unique within a single GraphDef. + // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_>./]*". + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // The operation name. There may be custom parameters in attrs. + // Op names starting with an underscore are reserved for internal use. + Op string `protobuf:"bytes,2,opt,name=op,proto3" json:"op,omitempty"` + // Each input is "node:src_output" with "node" being a string name and + // "src_output" indicating which output tensor to use from "node". If + // "src_output" is 0 the ":0" suffix can be omitted. Regular inputs + // may optionally be followed by control inputs that have the format + // "^node". + Input []string `protobuf:"bytes,3,rep,name=input,proto3" json:"input,omitempty"` + // A (possibly partial) specification for the device on which this + // node should be placed. + // The expected syntax for this string is as follows: + // + // DEVICE_SPEC ::= PARTIAL_SPEC + // + // PARTIAL_SPEC ::= ("/" CONSTRAINT) * + // CONSTRAINT ::= ("job:" JOB_NAME) + // | ("replica:" [1-9][0-9]*) + // | ("task:" [1-9][0-9]*) + // | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") ) + // + // Valid values for this string include: + // * "/job:worker/replica:0/task:1/device:GPU:3" (full specification) + // * "/job:worker/device:GPU:3" (partial specification) + // * "" (no specification) + // + // If the constraints do not resolve to a single device (or if this + // field is empty or not present), the runtime will attempt to + // choose a device automatically. + Device string `protobuf:"bytes,4,opt,name=device,proto3" json:"device,omitempty"` + // Operation-specific graph-construction-time configuration. + // Note that this should include all attrs defined in the + // corresponding OpDef, including those with a value matching + // the default -- this allows the default to change and makes + // NodeDefs easier to interpret on their own. However, if + // an attr with a default is not specified in this list, the + // default will be used. + // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and + // one of the names from the corresponding OpDef's attr field). + // The values must have a type matching the corresponding OpDef + // attr's type field. + // TODO(josh11b): Add some examples here showing best practices. + Attr map[string]*attr_value_go_proto.AttrValue `protobuf:"bytes,5,rep,name=attr,proto3" json:"attr,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // This stores debug information associated with the node. + ExperimentalDebugInfo *NodeDef_ExperimentalDebugInfo `protobuf:"bytes,6,opt,name=experimental_debug_info,json=experimentalDebugInfo,proto3" json:"experimental_debug_info,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NodeDef) Reset() { *m = NodeDef{} } +func (m *NodeDef) String() string { return proto.CompactTextString(m) } +func (*NodeDef) ProtoMessage() {} +func (*NodeDef) Descriptor() ([]byte, []int) { + return fileDescriptor_b34b3b836a96140b, []int{0} +} + +func (m *NodeDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NodeDef.Unmarshal(m, b) +} +func (m *NodeDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NodeDef.Marshal(b, m, deterministic) +} +func (m *NodeDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_NodeDef.Merge(m, src) +} +func (m *NodeDef) XXX_Size() int { + return xxx_messageInfo_NodeDef.Size(m) +} +func (m *NodeDef) XXX_DiscardUnknown() { + xxx_messageInfo_NodeDef.DiscardUnknown(m) +} + +var xxx_messageInfo_NodeDef proto.InternalMessageInfo + +func (m *NodeDef) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *NodeDef) GetOp() string { + if m != nil { + return m.Op + } + return "" +} + +func (m *NodeDef) GetInput() []string { + if m != nil { + return m.Input + } + return nil +} + +func (m *NodeDef) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *NodeDef) GetAttr() map[string]*attr_value_go_proto.AttrValue { + if m != nil { + return m.Attr + } + return nil +} + +func (m *NodeDef) GetExperimentalDebugInfo() *NodeDef_ExperimentalDebugInfo { + if m != nil { + return m.ExperimentalDebugInfo + } + return nil +} + +type NodeDef_ExperimentalDebugInfo struct { + // Opaque string inserted into error messages created by the runtime. + // + // This is intended to store the list of names of the nodes from the + // original graph that this node was derived. For example if this node, say + // C, was result of a fusion of 2 nodes A and B, then 'original_node' would + // be {A, B}. This information can be used to map errors originating at the + // current node to some top level source code. + OriginalNodeNames []string `protobuf:"bytes,1,rep,name=original_node_names,json=originalNodeNames,proto3" json:"original_node_names,omitempty"` + // This is intended to store the list of names of the functions from the + // original graph that this node was derived. For example if this node, say + // C, was result of a fusion of node A in function FA and node B in function + // FB, then `original_funcs` would be {FA, FB}. If the node is in the top + // level graph, the `original_func` is empty. This information, with the + // `original_node_names` can be used to map errors originating at the + // current ndoe to some top level source code. + OriginalFuncNames []string `protobuf:"bytes,2,rep,name=original_func_names,json=originalFuncNames,proto3" json:"original_func_names,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NodeDef_ExperimentalDebugInfo) Reset() { *m = NodeDef_ExperimentalDebugInfo{} } +func (m *NodeDef_ExperimentalDebugInfo) String() string { return proto.CompactTextString(m) } +func (*NodeDef_ExperimentalDebugInfo) ProtoMessage() {} +func (*NodeDef_ExperimentalDebugInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_b34b3b836a96140b, []int{0, 1} +} + +func (m *NodeDef_ExperimentalDebugInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NodeDef_ExperimentalDebugInfo.Unmarshal(m, b) +} +func (m *NodeDef_ExperimentalDebugInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NodeDef_ExperimentalDebugInfo.Marshal(b, m, deterministic) +} +func (m *NodeDef_ExperimentalDebugInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_NodeDef_ExperimentalDebugInfo.Merge(m, src) +} +func (m *NodeDef_ExperimentalDebugInfo) XXX_Size() int { + return xxx_messageInfo_NodeDef_ExperimentalDebugInfo.Size(m) +} +func (m *NodeDef_ExperimentalDebugInfo) XXX_DiscardUnknown() { + xxx_messageInfo_NodeDef_ExperimentalDebugInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_NodeDef_ExperimentalDebugInfo proto.InternalMessageInfo + +func (m *NodeDef_ExperimentalDebugInfo) GetOriginalNodeNames() []string { + if m != nil { + return m.OriginalNodeNames + } + return nil +} + +func (m *NodeDef_ExperimentalDebugInfo) GetOriginalFuncNames() []string { + if m != nil { + return m.OriginalFuncNames + } + return nil +} + +func init() { + proto.RegisterType((*NodeDef)(nil), "tensorflow.NodeDef") + proto.RegisterMapType((map[string]*attr_value_go_proto.AttrValue)(nil), "tensorflow.NodeDef.AttrEntry") + proto.RegisterType((*NodeDef_ExperimentalDebugInfo)(nil), "tensorflow.NodeDef.ExperimentalDebugInfo") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/node_def.proto", fileDescriptor_b34b3b836a96140b) +} + +var fileDescriptor_b34b3b836a96140b = []byte{ + // 381 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x74, 0x92, 0xcf, 0x8b, 0xd3, 0x40, + 0x14, 0xc7, 0x99, 0xa4, 0xad, 0x64, 0x0a, 0xa2, 0xa3, 0xd5, 0xa1, 0x20, 0x04, 0x4f, 0x51, 0x21, + 0xc1, 0x7a, 0x11, 0x6f, 0x96, 0x56, 0xf0, 0x52, 0x4b, 0x0e, 0x1e, 0xbc, 0x84, 0x34, 0x79, 0x89, + 0xa1, 0xc9, 0xbc, 0x30, 0x9d, 0xb4, 0x5b, 0xf6, 0x3f, 0xdb, 0xbf, 0x6c, 0x8f, 0xcb, 0x4c, 0xb3, + 0x6d, 0x0a, 0xe9, 0xed, 0xcd, 0xbc, 0xcf, 0x77, 0xbe, 0xf3, 0x7e, 0x50, 0x4f, 0x81, 0xd8, 0xa1, + 0xcc, 0x4a, 0x3c, 0x04, 0x09, 0x4a, 0x08, 0x32, 0x19, 0x57, 0x70, 0x40, 0xb9, 0x0d, 0x04, 0xa6, + 0x10, 0xa5, 0x90, 0xf9, 0xb5, 0x44, 0x85, 0x8c, 0x5e, 0xc8, 0xe9, 0xe7, 0xdb, 0xaa, 0x58, 0x29, + 0x19, 0xed, 0xe3, 0xb2, 0x81, 0x93, 0xee, 0xe3, 0x83, 0x4d, 0x5f, 0xac, 0x30, 0x85, 0x05, 0x64, + 0x8c, 0xd1, 0x81, 0x88, 0x2b, 0xe0, 0xc4, 0x25, 0x9e, 0x13, 0x9a, 0x98, 0xbd, 0xa4, 0x16, 0xd6, + 0xdc, 0x32, 0x37, 0x16, 0xd6, 0xec, 0x2d, 0x1d, 0x16, 0xa2, 0x6e, 0x14, 0xb7, 0x5d, 0xdb, 0x73, + 0xc2, 0xd3, 0x81, 0xbd, 0xa3, 0xa3, 0x14, 0xf6, 0x45, 0x02, 0x7c, 0x60, 0xc8, 0xf6, 0xc4, 0xbe, + 0xd2, 0x81, 0x76, 0xe4, 0x43, 0xd7, 0xf6, 0xc6, 0xb3, 0x0f, 0xfe, 0xe5, 0x63, 0x7e, 0x6b, 0xea, + 0xff, 0x54, 0x4a, 0x2e, 0x85, 0x92, 0xc7, 0xd0, 0xa0, 0x2c, 0xa6, 0xef, 0xe1, 0xae, 0x06, 0x59, + 0x54, 0x20, 0x54, 0x5c, 0x46, 0x29, 0x6c, 0x9a, 0x3c, 0x2a, 0x44, 0x86, 0x7c, 0xe4, 0x12, 0x6f, + 0x3c, 0xfb, 0xd4, 0xf7, 0xca, 0xb2, 0x23, 0x59, 0x68, 0xc5, 0x6f, 0x91, 0x61, 0x38, 0x81, 0xbe, + 0xeb, 0xe9, 0x8a, 0x3a, 0x67, 0x57, 0xf6, 0x8a, 0xda, 0x5b, 0x38, 0xb6, 0x35, 0xeb, 0x90, 0x7d, + 0xa1, 0x43, 0xd3, 0x21, 0x53, 0xf5, 0x78, 0x36, 0xe9, 0xfa, 0x69, 0xdd, 0x5f, 0x9d, 0x0c, 0x4f, + 0xcc, 0x0f, 0xeb, 0x3b, 0x99, 0x1e, 0xe8, 0xa4, 0xd7, 0x9f, 0xf9, 0xf4, 0x0d, 0xca, 0x22, 0x2f, + 0x44, 0x5c, 0x46, 0x66, 0x5e, 0xba, 0xa5, 0x3b, 0x4e, 0x4c, 0xeb, 0x5e, 0x3f, 0xa7, 0x74, 0x0d, + 0x2b, 0x9d, 0xb8, 0xe2, 0xb3, 0x46, 0x24, 0x2d, 0x6f, 0x5d, 0xf3, 0xbf, 0x1a, 0x91, 0x18, 0x7e, + 0x7e, 0x4f, 0x39, 0xca, 0xbc, 0xfb, 0xbf, 0xf3, 0xa4, 0xe7, 0x8e, 0x7e, 0x76, 0xad, 0x67, 0xbc, + 0x26, 0xff, 0xfe, 0xe4, 0x85, 0xfa, 0xdf, 0x6c, 0xfc, 0x04, 0xab, 0xa0, 0xb3, 0x1c, 0xfd, 0x61, + 0x8e, 0xb7, 0x76, 0x2d, 0xca, 0x31, 0x32, 0x6b, 0xf3, 0x48, 0xc8, 0x66, 0x64, 0xa2, 0x6f, 0x4f, + 0x01, 0x00, 0x00, 0xff, 0xff, 0x1f, 0xdb, 0xe5, 0xd2, 0xa4, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/op_def_go_proto/op_def.pb.go b/tensorflow/go/core/framework/op_def_go_proto/op_def.pb.go new file mode 100644 index 0000000..8a72e2f --- /dev/null +++ b/tensorflow/go/core/framework/op_def_go_proto/op_def.pb.go @@ -0,0 +1,546 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/op_def.proto + +package op_def_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Defines an operation. A NodeDef in a GraphDef specifies an Op by +// using the "op" field which should match the name of a OpDef. +// LINT.IfChange +type OpDef struct { + // Op names starting with an underscore are reserved for internal use. + // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9>_]*". + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Description of the input(s). + InputArg []*OpDef_ArgDef `protobuf:"bytes,2,rep,name=input_arg,json=inputArg,proto3" json:"input_arg,omitempty"` + // Description of the output(s). + OutputArg []*OpDef_ArgDef `protobuf:"bytes,3,rep,name=output_arg,json=outputArg,proto3" json:"output_arg,omitempty"` + // Named control outputs for this operation. Useful only for composite + // operations (i.e. functions) which want to name different control outputs. + ControlOutput []string `protobuf:"bytes,20,rep,name=control_output,json=controlOutput,proto3" json:"control_output,omitempty"` + Attr []*OpDef_AttrDef `protobuf:"bytes,4,rep,name=attr,proto3" json:"attr,omitempty"` + // Optional deprecation based on GraphDef versions. + Deprecation *OpDeprecation `protobuf:"bytes,8,opt,name=deprecation,proto3" json:"deprecation,omitempty"` + // One-line human-readable description of what the Op does. + Summary string `protobuf:"bytes,5,opt,name=summary,proto3" json:"summary,omitempty"` + // Additional, longer human-readable description of what the Op does. + Description string `protobuf:"bytes,6,opt,name=description,proto3" json:"description,omitempty"` + // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs) + IsCommutative bool `protobuf:"varint,18,opt,name=is_commutative,json=isCommutative,proto3" json:"is_commutative,omitempty"` + // If is_aggregate is true, then this operation accepts N >= 2 + // inputs and produces 1 output all of the same type. Should be + // associative and commutative, and produce output with the same + // shape as the input. The optimizer may replace an aggregate op + // taking input from multiple devices with a tree of aggregate ops + // that aggregate locally within each device (and possibly within + // groups of nearby devices) before communicating. + // TODO(josh11b): Implement that optimization. + IsAggregate bool `protobuf:"varint,16,opt,name=is_aggregate,json=isAggregate,proto3" json:"is_aggregate,omitempty"` + // Ops are marked as stateful if their behavior depends on some state beyond + // their input tensors (e.g. variable reading op) or if they have + // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops + // must always produce the same output for the same input and have + // no side-effects. + // + // By default Ops may be moved between devices. Stateful ops should + // either not be moved, or should only be moved if that state can also + // be moved (e.g. via some sort of save / restore). + // Stateful ops are guaranteed to never be optimized away by Common + // Subexpression Elimination (CSE). + IsStateful bool `protobuf:"varint,17,opt,name=is_stateful,json=isStateful,proto3" json:"is_stateful,omitempty"` + // By default, all inputs to an Op must be initialized Tensors. Ops + // that may initialize tensors for the first time should set this + // field to true, to allow the Op to take an uninitialized Tensor as + // input. + AllowsUninitializedInput bool `protobuf:"varint,19,opt,name=allows_uninitialized_input,json=allowsUninitializedInput,proto3" json:"allows_uninitialized_input,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *OpDef) Reset() { *m = OpDef{} } +func (m *OpDef) String() string { return proto.CompactTextString(m) } +func (*OpDef) ProtoMessage() {} +func (*OpDef) Descriptor() ([]byte, []int) { + return fileDescriptor_0a0e27face061c12, []int{0} +} + +func (m *OpDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_OpDef.Unmarshal(m, b) +} +func (m *OpDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_OpDef.Marshal(b, m, deterministic) +} +func (m *OpDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_OpDef.Merge(m, src) +} +func (m *OpDef) XXX_Size() int { + return xxx_messageInfo_OpDef.Size(m) +} +func (m *OpDef) XXX_DiscardUnknown() { + xxx_messageInfo_OpDef.DiscardUnknown(m) +} + +var xxx_messageInfo_OpDef proto.InternalMessageInfo + +func (m *OpDef) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *OpDef) GetInputArg() []*OpDef_ArgDef { + if m != nil { + return m.InputArg + } + return nil +} + +func (m *OpDef) GetOutputArg() []*OpDef_ArgDef { + if m != nil { + return m.OutputArg + } + return nil +} + +func (m *OpDef) GetControlOutput() []string { + if m != nil { + return m.ControlOutput + } + return nil +} + +func (m *OpDef) GetAttr() []*OpDef_AttrDef { + if m != nil { + return m.Attr + } + return nil +} + +func (m *OpDef) GetDeprecation() *OpDeprecation { + if m != nil { + return m.Deprecation + } + return nil +} + +func (m *OpDef) GetSummary() string { + if m != nil { + return m.Summary + } + return "" +} + +func (m *OpDef) GetDescription() string { + if m != nil { + return m.Description + } + return "" +} + +func (m *OpDef) GetIsCommutative() bool { + if m != nil { + return m.IsCommutative + } + return false +} + +func (m *OpDef) GetIsAggregate() bool { + if m != nil { + return m.IsAggregate + } + return false +} + +func (m *OpDef) GetIsStateful() bool { + if m != nil { + return m.IsStateful + } + return false +} + +func (m *OpDef) GetAllowsUninitializedInput() bool { + if m != nil { + return m.AllowsUninitializedInput + } + return false +} + +// For describing inputs and outputs. +type OpDef_ArgDef struct { + // Name for the input/output. Should match the regexp "[a-z][a-z0-9_]*". + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Human readable description. + Description string `protobuf:"bytes,2,opt,name=description,proto3" json:"description,omitempty"` + // Describes the type of one or more tensors that are accepted/produced + // by this input/output arg. The only legal combinations are: + // * For a single tensor: either the "type" field is set or the + // "type_attr" field is set to the name of an attr with type "type". + // * For a sequence of tensors with the same type: the "number_attr" + // field will be set to the name of an attr with type "int", and + // either the "type" or "type_attr" field will be set as for + // single tensors. + // * For a sequence of tensors, the "type_list_attr" field will be set + // to the name of an attr with type "list(type)". + Type types_go_proto.DataType `protobuf:"varint,3,opt,name=type,proto3,enum=tensorflow.DataType" json:"type,omitempty"` + TypeAttr string `protobuf:"bytes,4,opt,name=type_attr,json=typeAttr,proto3" json:"type_attr,omitempty"` + NumberAttr string `protobuf:"bytes,5,opt,name=number_attr,json=numberAttr,proto3" json:"number_attr,omitempty"` + // If specified, attr must have type "list(type)", and none of + // type, type_attr, and number_attr may be specified. + TypeListAttr string `protobuf:"bytes,6,opt,name=type_list_attr,json=typeListAttr,proto3" json:"type_list_attr,omitempty"` + // For inputs: if true, the inputs are required to be refs. + // By default, inputs can be either refs or non-refs. + // For outputs: if true, outputs are refs, otherwise they are not. + IsRef bool `protobuf:"varint,16,opt,name=is_ref,json=isRef,proto3" json:"is_ref,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *OpDef_ArgDef) Reset() { *m = OpDef_ArgDef{} } +func (m *OpDef_ArgDef) String() string { return proto.CompactTextString(m) } +func (*OpDef_ArgDef) ProtoMessage() {} +func (*OpDef_ArgDef) Descriptor() ([]byte, []int) { + return fileDescriptor_0a0e27face061c12, []int{0, 0} +} + +func (m *OpDef_ArgDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_OpDef_ArgDef.Unmarshal(m, b) +} +func (m *OpDef_ArgDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_OpDef_ArgDef.Marshal(b, m, deterministic) +} +func (m *OpDef_ArgDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_OpDef_ArgDef.Merge(m, src) +} +func (m *OpDef_ArgDef) XXX_Size() int { + return xxx_messageInfo_OpDef_ArgDef.Size(m) +} +func (m *OpDef_ArgDef) XXX_DiscardUnknown() { + xxx_messageInfo_OpDef_ArgDef.DiscardUnknown(m) +} + +var xxx_messageInfo_OpDef_ArgDef proto.InternalMessageInfo + +func (m *OpDef_ArgDef) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *OpDef_ArgDef) GetDescription() string { + if m != nil { + return m.Description + } + return "" +} + +func (m *OpDef_ArgDef) GetType() types_go_proto.DataType { + if m != nil { + return m.Type + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *OpDef_ArgDef) GetTypeAttr() string { + if m != nil { + return m.TypeAttr + } + return "" +} + +func (m *OpDef_ArgDef) GetNumberAttr() string { + if m != nil { + return m.NumberAttr + } + return "" +} + +func (m *OpDef_ArgDef) GetTypeListAttr() string { + if m != nil { + return m.TypeListAttr + } + return "" +} + +func (m *OpDef_ArgDef) GetIsRef() bool { + if m != nil { + return m.IsRef + } + return false +} + +// Description of the graph-construction-time configuration of this +// Op. That is to say, this describes the attr fields that will +// be specified in the NodeDef. +type OpDef_AttrDef struct { + // A descriptive name for the argument. May be used, e.g. by the + // Python client, as a keyword argument name, and so should match + // the regexp "[a-z][a-z0-9_]+". + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // One of the type names from attr_value.proto ("string", "list(string)", + // "int", etc.). + Type string `protobuf:"bytes,2,opt,name=type,proto3" json:"type,omitempty"` + // A reasonable default for this attribute if the user does not supply + // a value. If not specified, the user must supply a value. + DefaultValue *attr_value_go_proto.AttrValue `protobuf:"bytes,3,opt,name=default_value,json=defaultValue,proto3" json:"default_value,omitempty"` + // Human-readable description. + Description string `protobuf:"bytes,4,opt,name=description,proto3" json:"description,omitempty"` + // For type == "int", this is a minimum value. For "list(___)" + // types, this is the minimum length. + HasMinimum bool `protobuf:"varint,5,opt,name=has_minimum,json=hasMinimum,proto3" json:"has_minimum,omitempty"` + Minimum int64 `protobuf:"varint,6,opt,name=minimum,proto3" json:"minimum,omitempty"` + // The set of allowed values. Has type that is the "list" version + // of the "type" field above (uses the "list" field of AttrValue). + // If type == "type" or "list(type)" above, then the "type" field + // of "allowed_values.list" has the set of allowed DataTypes. + // If type == "string" or "list(string)", then the "s" field of + // "allowed_values.list" has the set of allowed strings. + AllowedValues *attr_value_go_proto.AttrValue `protobuf:"bytes,7,opt,name=allowed_values,json=allowedValues,proto3" json:"allowed_values,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *OpDef_AttrDef) Reset() { *m = OpDef_AttrDef{} } +func (m *OpDef_AttrDef) String() string { return proto.CompactTextString(m) } +func (*OpDef_AttrDef) ProtoMessage() {} +func (*OpDef_AttrDef) Descriptor() ([]byte, []int) { + return fileDescriptor_0a0e27face061c12, []int{0, 1} +} + +func (m *OpDef_AttrDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_OpDef_AttrDef.Unmarshal(m, b) +} +func (m *OpDef_AttrDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_OpDef_AttrDef.Marshal(b, m, deterministic) +} +func (m *OpDef_AttrDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_OpDef_AttrDef.Merge(m, src) +} +func (m *OpDef_AttrDef) XXX_Size() int { + return xxx_messageInfo_OpDef_AttrDef.Size(m) +} +func (m *OpDef_AttrDef) XXX_DiscardUnknown() { + xxx_messageInfo_OpDef_AttrDef.DiscardUnknown(m) +} + +var xxx_messageInfo_OpDef_AttrDef proto.InternalMessageInfo + +func (m *OpDef_AttrDef) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *OpDef_AttrDef) GetType() string { + if m != nil { + return m.Type + } + return "" +} + +func (m *OpDef_AttrDef) GetDefaultValue() *attr_value_go_proto.AttrValue { + if m != nil { + return m.DefaultValue + } + return nil +} + +func (m *OpDef_AttrDef) GetDescription() string { + if m != nil { + return m.Description + } + return "" +} + +func (m *OpDef_AttrDef) GetHasMinimum() bool { + if m != nil { + return m.HasMinimum + } + return false +} + +func (m *OpDef_AttrDef) GetMinimum() int64 { + if m != nil { + return m.Minimum + } + return 0 +} + +func (m *OpDef_AttrDef) GetAllowedValues() *attr_value_go_proto.AttrValue { + if m != nil { + return m.AllowedValues + } + return nil +} + +// Information about version-dependent deprecation of an op +type OpDeprecation struct { + // First GraphDef version at which the op is disallowed. + Version int32 `protobuf:"varint,1,opt,name=version,proto3" json:"version,omitempty"` + // Explanation of why it was deprecated and what to use instead. + Explanation string `protobuf:"bytes,2,opt,name=explanation,proto3" json:"explanation,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *OpDeprecation) Reset() { *m = OpDeprecation{} } +func (m *OpDeprecation) String() string { return proto.CompactTextString(m) } +func (*OpDeprecation) ProtoMessage() {} +func (*OpDeprecation) Descriptor() ([]byte, []int) { + return fileDescriptor_0a0e27face061c12, []int{1} +} + +func (m *OpDeprecation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_OpDeprecation.Unmarshal(m, b) +} +func (m *OpDeprecation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_OpDeprecation.Marshal(b, m, deterministic) +} +func (m *OpDeprecation) XXX_Merge(src proto.Message) { + xxx_messageInfo_OpDeprecation.Merge(m, src) +} +func (m *OpDeprecation) XXX_Size() int { + return xxx_messageInfo_OpDeprecation.Size(m) +} +func (m *OpDeprecation) XXX_DiscardUnknown() { + xxx_messageInfo_OpDeprecation.DiscardUnknown(m) +} + +var xxx_messageInfo_OpDeprecation proto.InternalMessageInfo + +func (m *OpDeprecation) GetVersion() int32 { + if m != nil { + return m.Version + } + return 0 +} + +func (m *OpDeprecation) GetExplanation() string { + if m != nil { + return m.Explanation + } + return "" +} + +// A collection of OpDefs +type OpList struct { + Op []*OpDef `protobuf:"bytes,1,rep,name=op,proto3" json:"op,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *OpList) Reset() { *m = OpList{} } +func (m *OpList) String() string { return proto.CompactTextString(m) } +func (*OpList) ProtoMessage() {} +func (*OpList) Descriptor() ([]byte, []int) { + return fileDescriptor_0a0e27face061c12, []int{2} +} + +func (m *OpList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_OpList.Unmarshal(m, b) +} +func (m *OpList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_OpList.Marshal(b, m, deterministic) +} +func (m *OpList) XXX_Merge(src proto.Message) { + xxx_messageInfo_OpList.Merge(m, src) +} +func (m *OpList) XXX_Size() int { + return xxx_messageInfo_OpList.Size(m) +} +func (m *OpList) XXX_DiscardUnknown() { + xxx_messageInfo_OpList.DiscardUnknown(m) +} + +var xxx_messageInfo_OpList proto.InternalMessageInfo + +func (m *OpList) GetOp() []*OpDef { + if m != nil { + return m.Op + } + return nil +} + +func init() { + proto.RegisterType((*OpDef)(nil), "tensorflow.OpDef") + proto.RegisterType((*OpDef_ArgDef)(nil), "tensorflow.OpDef.ArgDef") + proto.RegisterType((*OpDef_AttrDef)(nil), "tensorflow.OpDef.AttrDef") + proto.RegisterType((*OpDeprecation)(nil), "tensorflow.OpDeprecation") + proto.RegisterType((*OpList)(nil), "tensorflow.OpList") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/op_def.proto", fileDescriptor_0a0e27face061c12) +} + +var fileDescriptor_0a0e27face061c12 = []byte{ + // 674 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x94, 0xcd, 0x6e, 0x13, 0x3b, + 0x14, 0xc7, 0x35, 0xf9, 0xce, 0x49, 0x13, 0xdd, 0xfa, 0xb6, 0x92, 0x6f, 0xee, 0xa2, 0xd3, 0xea, + 0xf6, 0x2a, 0x02, 0x91, 0x48, 0x45, 0x08, 0x09, 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b/tensorflow/go/core/framework/reader_base_go_proto/reader_base.pb.go new file mode 100644 index 0000000..f6aac9e --- /dev/null +++ b/tensorflow/go/core/framework/reader_base_go_proto/reader_base.pb.go @@ -0,0 +1,114 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/reader_base.proto + +package reader_base_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// For serializing and restoring the state of ReaderBase, see +// reader_base.h for details. +type ReaderBaseState struct { + WorkStarted int64 `protobuf:"varint,1,opt,name=work_started,json=workStarted,proto3" json:"work_started,omitempty"` + WorkFinished int64 `protobuf:"varint,2,opt,name=work_finished,json=workFinished,proto3" json:"work_finished,omitempty"` + NumRecordsProduced int64 `protobuf:"varint,3,opt,name=num_records_produced,json=numRecordsProduced,proto3" json:"num_records_produced,omitempty"` + CurrentWork []byte `protobuf:"bytes,4,opt,name=current_work,json=currentWork,proto3" json:"current_work,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ReaderBaseState) Reset() { *m = ReaderBaseState{} } +func (m *ReaderBaseState) String() string { return proto.CompactTextString(m) } +func (*ReaderBaseState) ProtoMessage() {} +func (*ReaderBaseState) Descriptor() ([]byte, []int) { + return fileDescriptor_9d8282e7620a01b6, []int{0} +} + +func (m *ReaderBaseState) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ReaderBaseState.Unmarshal(m, b) +} +func (m *ReaderBaseState) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ReaderBaseState.Marshal(b, m, deterministic) +} +func (m *ReaderBaseState) XXX_Merge(src proto.Message) { + xxx_messageInfo_ReaderBaseState.Merge(m, src) +} +func (m *ReaderBaseState) XXX_Size() int { + return xxx_messageInfo_ReaderBaseState.Size(m) +} +func (m *ReaderBaseState) XXX_DiscardUnknown() { + xxx_messageInfo_ReaderBaseState.DiscardUnknown(m) +} + +var xxx_messageInfo_ReaderBaseState proto.InternalMessageInfo + +func (m *ReaderBaseState) GetWorkStarted() int64 { + if m != nil { + return m.WorkStarted + } + return 0 +} + +func (m *ReaderBaseState) GetWorkFinished() int64 { + if m != nil { + return m.WorkFinished + } + return 0 +} + +func (m *ReaderBaseState) GetNumRecordsProduced() int64 { + if m != nil { + return m.NumRecordsProduced + } + return 0 +} + +func (m *ReaderBaseState) GetCurrentWork() []byte { + if m != nil { + return m.CurrentWork + } + return nil +} + +func init() { + proto.RegisterType((*ReaderBaseState)(nil), "tensorflow.ReaderBaseState") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/reader_base.proto", fileDescriptor_9d8282e7620a01b6) +} + +var fileDescriptor_9d8282e7620a01b6 = []byte{ + // 254 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x90, 0xc1, 0x4a, 0x03, 0x31, + 0x10, 0x86, 0x89, 0x15, 0x0f, 0x69, 0x45, 0x59, 0x3c, 0xe4, 0x58, 0xf5, 0x52, 0x10, 0x76, 0x05, + 0xdf, 0x60, 0x0f, 0x9e, 0x97, 0xf4, 0x20, 0x78, 0x09, 0xd9, 0x64, 0x76, 0xbb, 0xd4, 0xcd, 0x94, + 0x49, 0x42, 0x9f, 0xc0, 0xd7, 0xf1, 0xf9, 0x3c, 0x4a, 0xd2, 0xc5, 0xf5, 0x20, 0xbd, 0x0d, 0xff, + 0x7c, 0x93, 0xf0, 0xfd, 0xfc, 0x29, 0x80, 0xf3, 0x48, 0xdd, 0x07, 0x1e, 0x2b, 0x83, 0x04, 0x55, + 0x47, 0x7a, 0x84, 0x23, 0xd2, 0xbe, 0x22, 0xd0, 0x16, 0x48, 0xb5, 0xda, 0x43, 0x79, 0x20, 0x0c, + 0x58, 0xf0, 0x19, 0x7e, 0xf8, 0x62, 0xfc, 0x46, 0x66, 0xa2, 0xd6, 0x1e, 0xb6, 0x41, 0x07, 0x28, + 0xee, 0xf9, 0x2a, 0x5d, 0x2a, 0x1f, 0x34, 0x05, 0xb0, 0x82, 0xad, 0xd9, 0x66, 0x21, 0x97, 0x29, + 0xdb, 0x9e, 0xa2, 0xe2, 0x91, 0x5f, 0x67, 0xa4, 0x1b, 0xdc, 0xe0, 0x77, 0x60, 0xc5, 0x45, 0x66, + 0xf2, 0xdd, 0xeb, 0x94, 0x15, 0xcf, 0xfc, 0xce, 0xc5, 0x51, 0x11, 0x18, 0x24, 0xeb, 0xd5, 0x81, + 0xd0, 0x46, 0x03, 0x56, 0x2c, 0x32, 0x5b, 0xb8, 0x38, 0xca, 0xd3, 0xaa, 0x99, 0x36, 0xe9, 0x67, + 0x13, 0x89, 0xc0, 0x05, 0x95, 0x5e, 0x12, 0x97, 0x6b, 0xb6, 0x59, 0xc9, 0xe5, 0x94, 0xbd, 0x21, + 0xed, 0xeb, 0x4f, 0xc6, 0x05, 0x52, 0x5f, 0xce, 0x0e, 0xe5, 0xaf, 0x6b, 0x7d, 0x3b, 0xab, 0x34, + 0x49, 0xd5, 0x37, 0xec, 0x5d, 0xf6, 0x43, 0xd8, 0xc5, 0xb6, 0x34, 0x38, 0x56, 0x7f, 0x5a, 0xfa, + 0x7f, 0xec, 0xf1, 0x4c, 0x7d, 0xaa, 0x47, 0x95, 0x1b, 0xfc, 0x66, 0xac, 0xbd, 0xca, 0xd3, 0xcb, + 0x4f, 0x00, 0x00, 0x00, 0xff, 0xff, 0x0a, 0x29, 0x98, 0x2d, 0x7a, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/remote_fused_graph_execute_info_go_proto/remote_fused_graph_execute_info.pb.go b/tensorflow/go/core/framework/remote_fused_graph_execute_info_go_proto/remote_fused_graph_execute_info.pb.go new file mode 100644 index 0000000..37a1d18 --- /dev/null +++ b/tensorflow/go/core/framework/remote_fused_graph_execute_info_go_proto/remote_fused_graph_execute_info.pb.go @@ -0,0 +1,218 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/remote_fused_graph_execute_info.proto + +package remote_fused_graph_execute_info_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing a handle to a tensorflow resource. Handles are +// not valid across executions, but can be serialized back and forth from within +// a single run. +type RemoteFusedGraphExecuteInfo struct { + // Definition of remote graph + RemoteGraph *graph_go_proto.GraphDef `protobuf:"bytes,1,opt,name=remote_graph,json=remoteGraph,proto3" json:"remote_graph,omitempty"` + // Remote fused graph input node name + GraphInputNodeName []string `protobuf:"bytes,2,rep,name=graph_input_node_name,json=graphInputNodeName,proto3" json:"graph_input_node_name,omitempty"` + // Remote fused graph output node name + GraphOutputNodeName []string `protobuf:"bytes,3,rep,name=graph_output_node_name,json=graphOutputNodeName,proto3" json:"graph_output_node_name,omitempty"` + // Executor's name + ExecutorName string `protobuf:"bytes,4,opt,name=executor_name,json=executorName,proto3" json:"executor_name,omitempty"` + // Optional: Parameters given to the executor + SerializedExecutorParameters []byte `protobuf:"bytes,5,opt,name=serialized_executor_parameters,json=serializedExecutorParameters,proto3" json:"serialized_executor_parameters,omitempty"` + // Optional: Default graph input tensor shape used to allocate memory + // before executing op + DefaultGraphInputTensorShape []*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto `protobuf:"bytes,6,rep,name=default_graph_input_tensor_shape,json=defaultGraphInputTensorShape,proto3" json:"default_graph_input_tensor_shape,omitempty"` + // Optional: Default graph input tensor shape used to allocate memory + // before executing op + // TODO(satok): Remote output tensor shape once shape information is stored + // in NodeDef + DefaultGraphOutputTensorShape []*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto `protobuf:"bytes,7,rep,name=default_graph_output_tensor_shape,json=defaultGraphOutputTensorShape,proto3" json:"default_graph_output_tensor_shape,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RemoteFusedGraphExecuteInfo) Reset() { *m = RemoteFusedGraphExecuteInfo{} } +func (m *RemoteFusedGraphExecuteInfo) String() string { return proto.CompactTextString(m) } +func (*RemoteFusedGraphExecuteInfo) ProtoMessage() {} +func (*RemoteFusedGraphExecuteInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_c15f13da5b37f691, []int{0} +} + +func (m *RemoteFusedGraphExecuteInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RemoteFusedGraphExecuteInfo.Unmarshal(m, b) +} +func (m *RemoteFusedGraphExecuteInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RemoteFusedGraphExecuteInfo.Marshal(b, m, deterministic) +} +func (m *RemoteFusedGraphExecuteInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_RemoteFusedGraphExecuteInfo.Merge(m, src) +} +func (m *RemoteFusedGraphExecuteInfo) XXX_Size() int { + return xxx_messageInfo_RemoteFusedGraphExecuteInfo.Size(m) +} +func (m *RemoteFusedGraphExecuteInfo) XXX_DiscardUnknown() { + xxx_messageInfo_RemoteFusedGraphExecuteInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_RemoteFusedGraphExecuteInfo proto.InternalMessageInfo + +func (m *RemoteFusedGraphExecuteInfo) GetRemoteGraph() *graph_go_proto.GraphDef { + if m != nil { + return m.RemoteGraph + } + return nil +} + +func (m *RemoteFusedGraphExecuteInfo) GetGraphInputNodeName() []string { + if m != nil { + return m.GraphInputNodeName + } + return nil +} + +func (m *RemoteFusedGraphExecuteInfo) GetGraphOutputNodeName() []string { + if m != nil { + return m.GraphOutputNodeName + } + return nil +} + +func (m *RemoteFusedGraphExecuteInfo) GetExecutorName() string { + if m != nil { + return m.ExecutorName + } + return "" +} + +func (m *RemoteFusedGraphExecuteInfo) GetSerializedExecutorParameters() []byte { + if m != nil { + return m.SerializedExecutorParameters + } + return nil +} + +func (m *RemoteFusedGraphExecuteInfo) GetDefaultGraphInputTensorShape() []*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto { + if m != nil { + return m.DefaultGraphInputTensorShape + } + return nil +} + +func (m *RemoteFusedGraphExecuteInfo) GetDefaultGraphOutputTensorShape() []*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto { + if m != nil { + return m.DefaultGraphOutputTensorShape + } + return nil +} + +type RemoteFusedGraphExecuteInfo_TensorShapeTypeProto struct { + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) Reset() { + *m = RemoteFusedGraphExecuteInfo_TensorShapeTypeProto{} +} +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) String() string { + return proto.CompactTextString(m) +} +func (*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) ProtoMessage() {} +func (*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) Descriptor() ([]byte, []int) { + return fileDescriptor_c15f13da5b37f691, []int{0, 0} +} + +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RemoteFusedGraphExecuteInfo_TensorShapeTypeProto.Unmarshal(m, b) +} +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RemoteFusedGraphExecuteInfo_TensorShapeTypeProto.Marshal(b, m, deterministic) +} +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_RemoteFusedGraphExecuteInfo_TensorShapeTypeProto.Merge(m, src) +} +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) XXX_Size() int { + return xxx_messageInfo_RemoteFusedGraphExecuteInfo_TensorShapeTypeProto.Size(m) +} +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) XXX_DiscardUnknown() { + xxx_messageInfo_RemoteFusedGraphExecuteInfo_TensorShapeTypeProto.DiscardUnknown(m) +} + +var xxx_messageInfo_RemoteFusedGraphExecuteInfo_TensorShapeTypeProto proto.InternalMessageInfo + +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *RemoteFusedGraphExecuteInfo_TensorShapeTypeProto) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func init() { + proto.RegisterType((*RemoteFusedGraphExecuteInfo)(nil), "tensorflow.RemoteFusedGraphExecuteInfo") + proto.RegisterType((*RemoteFusedGraphExecuteInfo_TensorShapeTypeProto)(nil), "tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProto") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/remote_fused_graph_execute_info.proto", fileDescriptor_c15f13da5b37f691) +} + +var fileDescriptor_c15f13da5b37f691 = []byte{ + // 462 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xac, 0x93, 0xc1, 0x8b, 0xd3, 0x40, + 0x14, 0xc6, 0x99, 0xad, 0x5d, 0xd9, 0x69, 0xf5, 0x30, 0xae, 0x12, 0x6a, 0x95, 0xa8, 0x08, 0x41, + 0x24, 0xc5, 0xee, 0xc1, 0x8b, 0x20, 0x2c, 0x5d, 0x97, 0xbd, 0xac, 0x25, 0xee, 0xc9, 0xcb, 0x30, + 0xdb, 0xbc, 0xa4, 0xc1, 0x26, 0x2f, 0x4c, 0x26, 0xae, 0xeb, 0x59, 0xfc, 0x5f, 0x3c, 0xfa, 0xdf, + 0x79, 0x94, 0xbc, 0x89, 0xe9, 0x14, 0xb6, 0x05, 0x61, 0x6f, 0xd3, 0xbe, 0xdf, 0xf7, 0xe6, 0x7b, + 0x5f, 0xde, 0xf0, 0xf7, 0x06, 0x8a, 0x0a, 0x75, 0xb2, 0xc2, 0xab, 0xc9, 0x02, 0x35, 0x4c, 0x12, + 0xad, 0x72, 0xb8, 0x42, 0xfd, 0x65, 0xa2, 0x21, 0x47, 0x03, 0x32, 0xa9, 0x2b, 0x88, 0x65, 0xaa, + 0x55, 0xb9, 0x94, 0xf0, 0x0d, 0x16, 0xb5, 0x01, 0x99, 0x15, 0x09, 0x86, 0xa5, 0x46, 0x83, 0x82, + 0xaf, 0x1b, 0x8c, 0x5e, 0x6e, 0x6f, 0x46, 0x7a, 0x2b, 0x19, 0xbd, 0xde, 0x8e, 0xd9, 0x8a, 0xac, + 0x96, 0xaa, 0x84, 0x96, 0xde, 0xd1, 0xd4, 0x5c, 0x97, 0x50, 0x59, 0xec, 0xf9, 0xef, 0x3e, 0x7f, + 0x1c, 0x91, 0xe3, 0x0f, 0x8d, 0xe1, 0xd3, 0xe6, 0xbe, 0x13, 0x6b, 0xf7, 0xac, 0x48, 0x50, 0xbc, + 0xe5, 0xc3, 0x76, 0x20, 0xb2, 0xe2, 0x31, 0x9f, 0x05, 0x83, 0xe9, 0x61, 0xb8, 0xee, 0x1e, 0x92, + 0x66, 0x06, 0x49, 0x34, 0xb0, 0x24, 0xfd, 0x16, 0x6f, 0xf8, 0x43, 0x3b, 0x7c, 0x56, 0x94, 0xb5, + 0x91, 0x05, 0xc6, 0x20, 0x0b, 0x95, 0x83, 0xb7, 0xe7, 0xf7, 0x82, 0x83, 0x48, 0x50, 0xf1, 0xac, + 0xa9, 0x9d, 0x63, 0x0c, 0xe7, 0x2a, 0x07, 0x71, 0xc4, 0x1f, 0x59, 0x09, 0xd6, 0x66, 0x53, 0xd3, + 0x23, 0xcd, 0x03, 0xaa, 0x7e, 0xa4, 0x62, 0x27, 0x7a, 0xc1, 0xef, 0xd9, 0x78, 0x51, 0x5b, 0xf6, + 0x8e, 0xcf, 0x82, 0x83, 0x68, 0xf8, 0xef, 0x4f, 0x82, 0x66, 0xfc, 0x69, 0x05, 0x3a, 0x53, 0xab, + 0xec, 0x3b, 0xc4, 0xb2, 0xe3, 0x4b, 0xd5, 0x64, 0x62, 0x40, 0x57, 0x5e, 0xdf, 0x67, 0xc1, 0x30, + 0x1a, 0xaf, 0xa9, 0x93, 0x16, 0x9a, 0x77, 0x8c, 0xf8, 0xc1, 0xb8, 0x1f, 0x43, 0xa2, 0xea, 0x95, + 0x91, 0xee, 0x6c, 0x6e, 0xfa, 0xde, 0xbe, 0xdf, 0x0b, 0x06, 0xd3, 0x77, 0x6e, 0x40, 0x3b, 0xf2, + 0x0d, 0x2f, 0x08, 0xfb, 0xd4, 0x48, 0x2f, 0xae, 0x4b, 0x98, 0x37, 0x1f, 0x25, 0x1a, 0xb7, 0xb7, + 0x9c, 0x76, 0x19, 0x39, 0x98, 0xf8, 0xc9, 0xf8, 0xb3, 0x4d, 0x1b, 0x6d, 0x5e, 0x1b, 0x3e, 0xee, + 0xde, 0x82, 0x8f, 0x27, 0xae, 0x0f, 0x9b, 0xbb, 0xc3, 0x8d, 0xbe, 0xf2, 0xc3, 0x9b, 0x64, 0xe2, + 0x15, 0xef, 0xc7, 0xcd, 0x8e, 0xd1, 0xb2, 0xdc, 0xdf, 0x5c, 0x96, 0x99, 0x32, 0xaa, 0x21, 0x23, + 0x8b, 0x88, 0x29, 0xef, 0x5b, 0xbf, 0x7b, 0xb4, 0x58, 0x63, 0x97, 0x75, 0x9a, 0x5b, 0x3f, 0x16, + 0x3d, 0xfe, 0xc5, 0xb8, 0x87, 0x3a, 0x75, 0xd1, 0x6e, 0xb9, 0x8f, 0xfd, 0x1d, 0x53, 0x52, 0x97, + 0x39, 0xfb, 0x9c, 0xa4, 0x99, 0x59, 0xd6, 0x97, 0xe1, 0x02, 0xf3, 0x89, 0xf3, 0x4c, 0x6e, 0x3e, + 0xa6, 0xf8, 0x9f, 0x2f, 0x5c, 0xa6, 0x28, 0xe9, 0x71, 0xfd, 0x61, 0xec, 0x72, 0x9f, 0x4e, 0x47, + 0x7f, 0x03, 0x00, 0x00, 0xff, 0xff, 0x53, 0x61, 0xb0, 0x68, 0x31, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/resource_handle_go_proto/resource_handle.pb.go b/tensorflow/go/core/framework/resource_handle_go_proto/resource_handle.pb.go new file mode 100644 index 0000000..15eadaf --- /dev/null +++ b/tensorflow/go/core/framework/resource_handle_go_proto/resource_handle.pb.go @@ -0,0 +1,197 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/resource_handle.proto + +package resource_handle_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing a handle to a tensorflow resource. Handles are +// not valid across executions, but can be serialized back and forth from within +// a single run. +type ResourceHandleProto struct { + // Unique name for the device containing the resource. + Device string `protobuf:"bytes,1,opt,name=device,proto3" json:"device,omitempty"` + // Container in which this resource is placed. + Container string `protobuf:"bytes,2,opt,name=container,proto3" json:"container,omitempty"` + // Unique name of this resource. + Name string `protobuf:"bytes,3,opt,name=name,proto3" json:"name,omitempty"` + // Hash code for the type of the resource. Is only valid in the same device + // and in the same execution. + HashCode uint64 `protobuf:"varint,4,opt,name=hash_code,json=hashCode,proto3" json:"hash_code,omitempty"` + // For debug-only, the name of the type pointed to by this handle, if + // available. + MaybeTypeName string `protobuf:"bytes,5,opt,name=maybe_type_name,json=maybeTypeName,proto3" json:"maybe_type_name,omitempty"` + // Data types and shapes for the underlying resource. + DtypesAndShapes []*ResourceHandleProto_DtypeAndShape `protobuf:"bytes,6,rep,name=dtypes_and_shapes,json=dtypesAndShapes,proto3" json:"dtypes_and_shapes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ResourceHandleProto) Reset() { *m = ResourceHandleProto{} } +func (m *ResourceHandleProto) String() string { return proto.CompactTextString(m) } +func (*ResourceHandleProto) ProtoMessage() {} +func (*ResourceHandleProto) Descriptor() ([]byte, []int) { + return fileDescriptor_a36024d2bd9a2afd, []int{0} +} + +func (m *ResourceHandleProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ResourceHandleProto.Unmarshal(m, b) +} +func (m *ResourceHandleProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ResourceHandleProto.Marshal(b, m, deterministic) +} +func (m *ResourceHandleProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_ResourceHandleProto.Merge(m, src) +} +func (m *ResourceHandleProto) XXX_Size() int { + return xxx_messageInfo_ResourceHandleProto.Size(m) +} +func (m *ResourceHandleProto) XXX_DiscardUnknown() { + xxx_messageInfo_ResourceHandleProto.DiscardUnknown(m) +} + +var xxx_messageInfo_ResourceHandleProto proto.InternalMessageInfo + +func (m *ResourceHandleProto) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *ResourceHandleProto) GetContainer() string { + if m != nil { + return m.Container + } + return "" +} + +func (m *ResourceHandleProto) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *ResourceHandleProto) GetHashCode() uint64 { + if m != nil { + return m.HashCode + } + return 0 +} + +func (m *ResourceHandleProto) GetMaybeTypeName() string { + if m != nil { + return m.MaybeTypeName + } + return "" +} + +func (m *ResourceHandleProto) GetDtypesAndShapes() []*ResourceHandleProto_DtypeAndShape { + if m != nil { + return m.DtypesAndShapes + } + return nil +} + +// Protocol buffer representing a pair of (data type, tensor shape). +type ResourceHandleProto_DtypeAndShape struct { + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ResourceHandleProto_DtypeAndShape) Reset() { *m = ResourceHandleProto_DtypeAndShape{} } +func (m *ResourceHandleProto_DtypeAndShape) String() string { return proto.CompactTextString(m) } +func (*ResourceHandleProto_DtypeAndShape) ProtoMessage() {} +func (*ResourceHandleProto_DtypeAndShape) Descriptor() ([]byte, []int) { + return fileDescriptor_a36024d2bd9a2afd, []int{0, 0} +} + +func (m *ResourceHandleProto_DtypeAndShape) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ResourceHandleProto_DtypeAndShape.Unmarshal(m, b) +} +func (m *ResourceHandleProto_DtypeAndShape) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ResourceHandleProto_DtypeAndShape.Marshal(b, m, deterministic) +} +func (m *ResourceHandleProto_DtypeAndShape) XXX_Merge(src proto.Message) { + xxx_messageInfo_ResourceHandleProto_DtypeAndShape.Merge(m, src) +} +func (m *ResourceHandleProto_DtypeAndShape) XXX_Size() int { + return xxx_messageInfo_ResourceHandleProto_DtypeAndShape.Size(m) +} +func (m *ResourceHandleProto_DtypeAndShape) XXX_DiscardUnknown() { + xxx_messageInfo_ResourceHandleProto_DtypeAndShape.DiscardUnknown(m) +} + +var xxx_messageInfo_ResourceHandleProto_DtypeAndShape proto.InternalMessageInfo + +func (m *ResourceHandleProto_DtypeAndShape) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *ResourceHandleProto_DtypeAndShape) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func init() { + proto.RegisterType((*ResourceHandleProto)(nil), "tensorflow.ResourceHandleProto") + proto.RegisterType((*ResourceHandleProto_DtypeAndShape)(nil), "tensorflow.ResourceHandleProto.DtypeAndShape") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/resource_handle.proto", fileDescriptor_a36024d2bd9a2afd) +} + +var fileDescriptor_a36024d2bd9a2afd = []byte{ + // 363 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 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0x68, 0xb4, 0x6d, 0x70, 0xce, 0xcc, 0x44, 0xc6, 0x60, + 0x55, 0x69, 0x40, 0x35, 0xdf, 0x63, 0xcd, 0x48, 0xea, 0x48, 0xf8, 0x18, 0x80, 0x65, 0x0e, 0x8c, + 0xa1, 0xe9, 0x35, 0xf2, 0xc6, 0x39, 0x06, 0xc0, 0x0f, 0x59, 0x37, 0x96, 0xeb, 0x29, 0x88, 0xdc, + 0x93, 0x20, 0x6e, 0x8d, 0xb8, 0x6d, 0x6a, 0x4f, 0xd6, 0x29, 0x5c, 0xe5, 0x22, 0x77, 0xec, 0x4f, + 0x40, 0xb6, 0x85, 0x4c, 0x82, 0x22, 0xa7, 0xb6, 0xea, 0x83, 0xea, 0xb0, 0xe5, 0x9e, 0xd8, 0xdb, + 0xa4, 0xf6, 0x8e, 0x28, 0xf6, 0x38, 0x27, 0x9e, 0x25, 0xc1, 0x4d, 0xce, 0xf2, 0xba, 0x85, 0xce, + 0xdb, 0x59, 0xf7, 0x90, 0xb5, 0x3f, 0x20, 0xf8, 0x11, 0xab, 0x11, 0x86, 0x92, 0x77, 0xdc, 0x7f, + 0x65, 0xfd, 0xb1, 0xcc, 0x64, 0x6e, 0xca, 0x2b, 0x20, 0xdc, 0x65, 0x35, 0x32, 0x43, 0x57, 0xd1, + 0x72, 0xfb, 0x65, 0xec, 0x84, 0x4a, 0xd2, 0x24, 0x23, 0x5e, 0x01, 0xbd, 0x34, 0x1b, 0xbf, 0x7e, + 0x37, 0x46, 0x4f, 0x06, 0xb3, 0x50, 0x85, 0x65, 0xc2, 0xfb, 0x0b, 0x8d, 0x3a, 0x9f, 0x72, 0x18, + 0xf7, 0xb7, 0xe1, 0x3c, 0x8b, 0x96, 0x53, 0xdb, 0xc7, 0xb8, 0xfc, 0x89, 0x76, 0x97, 0x21, 0xfe, + 0xf0, 0xbb, 0x44, 0x88, 0x82, 0xde, 0xff, 0xc5, 0x30, 0xa6, 0x75, 0xaa, 0x4e, 0x5f, 0x03, 0x00, + 0x00, 0xff, 0xff, 0x67, 0xfd, 0xc6, 0xa7, 0x9d, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/step_stats_go_proto/step_stats.pb.go b/tensorflow/go/core/framework/step_stats_go_proto/step_stats.pb.go new file mode 100644 index 0000000..af2f58f --- /dev/null +++ b/tensorflow/go/core/framework/step_stats_go_proto/step_stats.pb.go @@ -0,0 +1,635 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/step_stats.proto + +package step_stats_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + allocation_description_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/allocation_description_go_proto" + tensor_description_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_description_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// An allocation/de-allocation operation performed by the allocator. +type AllocationRecord struct { + // The timestamp of the operation. + AllocMicros int64 `protobuf:"varint,1,opt,name=alloc_micros,json=allocMicros,proto3" json:"alloc_micros,omitempty"` + // Number of bytes allocated, or de-allocated if negative. + AllocBytes int64 `protobuf:"varint,2,opt,name=alloc_bytes,json=allocBytes,proto3" json:"alloc_bytes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AllocationRecord) Reset() { *m = AllocationRecord{} } +func (m *AllocationRecord) String() string { return proto.CompactTextString(m) } +func (*AllocationRecord) ProtoMessage() {} +func (*AllocationRecord) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{0} +} + +func (m *AllocationRecord) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AllocationRecord.Unmarshal(m, b) +} +func (m *AllocationRecord) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AllocationRecord.Marshal(b, m, deterministic) +} +func (m *AllocationRecord) XXX_Merge(src proto.Message) { + xxx_messageInfo_AllocationRecord.Merge(m, src) +} +func (m *AllocationRecord) XXX_Size() int { + return xxx_messageInfo_AllocationRecord.Size(m) +} +func (m *AllocationRecord) XXX_DiscardUnknown() { + xxx_messageInfo_AllocationRecord.DiscardUnknown(m) +} + +var xxx_messageInfo_AllocationRecord proto.InternalMessageInfo + +func (m *AllocationRecord) GetAllocMicros() int64 { + if m != nil { + return m.AllocMicros + } + return 0 +} + +func (m *AllocationRecord) GetAllocBytes() int64 { + if m != nil { + return m.AllocBytes + } + return 0 +} + +type AllocatorMemoryUsed struct { + AllocatorName string `protobuf:"bytes,1,opt,name=allocator_name,json=allocatorName,proto3" json:"allocator_name,omitempty"` + // These are per-node allocator memory stats. + TotalBytes int64 `protobuf:"varint,2,opt,name=total_bytes,json=totalBytes,proto3" json:"total_bytes,omitempty"` + PeakBytes int64 `protobuf:"varint,3,opt,name=peak_bytes,json=peakBytes,proto3" json:"peak_bytes,omitempty"` + // The bytes that are not deallocated. + LiveBytes int64 `protobuf:"varint,4,opt,name=live_bytes,json=liveBytes,proto3" json:"live_bytes,omitempty"` + // The allocation and deallocation timeline. + AllocationRecords []*AllocationRecord `protobuf:"bytes,6,rep,name=allocation_records,json=allocationRecords,proto3" json:"allocation_records,omitempty"` + // These are snapshots of the overall allocator memory stats. + // The number of live bytes currently allocated by the allocator. + AllocatorBytesInUse int64 `protobuf:"varint,5,opt,name=allocator_bytes_in_use,json=allocatorBytesInUse,proto3" json:"allocator_bytes_in_use,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AllocatorMemoryUsed) Reset() { *m = AllocatorMemoryUsed{} } +func (m *AllocatorMemoryUsed) String() string { return proto.CompactTextString(m) } +func (*AllocatorMemoryUsed) ProtoMessage() {} +func (*AllocatorMemoryUsed) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{1} +} + +func (m *AllocatorMemoryUsed) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AllocatorMemoryUsed.Unmarshal(m, b) +} +func (m *AllocatorMemoryUsed) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AllocatorMemoryUsed.Marshal(b, m, deterministic) +} +func (m *AllocatorMemoryUsed) XXX_Merge(src proto.Message) { + xxx_messageInfo_AllocatorMemoryUsed.Merge(m, src) +} +func (m *AllocatorMemoryUsed) XXX_Size() int { + return xxx_messageInfo_AllocatorMemoryUsed.Size(m) +} +func (m *AllocatorMemoryUsed) XXX_DiscardUnknown() { + xxx_messageInfo_AllocatorMemoryUsed.DiscardUnknown(m) +} + +var xxx_messageInfo_AllocatorMemoryUsed proto.InternalMessageInfo + +func (m *AllocatorMemoryUsed) GetAllocatorName() string { + if m != nil { + return m.AllocatorName + } + return "" +} + +func (m *AllocatorMemoryUsed) GetTotalBytes() int64 { + if m != nil { + return m.TotalBytes + } + return 0 +} + +func (m *AllocatorMemoryUsed) GetPeakBytes() int64 { + if m != nil { + return m.PeakBytes + } + return 0 +} + +func (m *AllocatorMemoryUsed) GetLiveBytes() int64 { + if m != nil { + return m.LiveBytes + } + return 0 +} + +func (m *AllocatorMemoryUsed) GetAllocationRecords() []*AllocationRecord { + if m != nil { + return m.AllocationRecords + } + return nil +} + +func (m *AllocatorMemoryUsed) GetAllocatorBytesInUse() int64 { + if m != nil { + return m.AllocatorBytesInUse + } + return 0 +} + +// Output sizes recorded for a single execution of a graph node. +type NodeOutput struct { + Slot int32 `protobuf:"varint,1,opt,name=slot,proto3" json:"slot,omitempty"` + TensorDescription *tensor_description_go_proto.TensorDescription `protobuf:"bytes,3,opt,name=tensor_description,json=tensorDescription,proto3" json:"tensor_description,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NodeOutput) Reset() { *m = NodeOutput{} } +func (m *NodeOutput) String() string { return proto.CompactTextString(m) } +func (*NodeOutput) ProtoMessage() {} +func (*NodeOutput) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{2} +} + +func (m *NodeOutput) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NodeOutput.Unmarshal(m, b) +} +func (m *NodeOutput) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NodeOutput.Marshal(b, m, deterministic) +} +func (m *NodeOutput) XXX_Merge(src proto.Message) { + xxx_messageInfo_NodeOutput.Merge(m, src) +} +func (m *NodeOutput) XXX_Size() int { + return xxx_messageInfo_NodeOutput.Size(m) +} +func (m *NodeOutput) XXX_DiscardUnknown() { + xxx_messageInfo_NodeOutput.DiscardUnknown(m) +} + +var xxx_messageInfo_NodeOutput proto.InternalMessageInfo + +func (m *NodeOutput) GetSlot() int32 { + if m != nil { + return m.Slot + } + return 0 +} + +func (m *NodeOutput) GetTensorDescription() *tensor_description_go_proto.TensorDescription { + if m != nil { + return m.TensorDescription + } + return nil +} + +// For memory tracking. +type MemoryStats struct { + TempMemorySize int64 `protobuf:"varint,1,opt,name=temp_memory_size,json=tempMemorySize,proto3" json:"temp_memory_size,omitempty"` + PersistentMemorySize int64 `protobuf:"varint,3,opt,name=persistent_memory_size,json=persistentMemorySize,proto3" json:"persistent_memory_size,omitempty"` + PersistentTensorAllocIds []int64 `protobuf:"varint,5,rep,packed,name=persistent_tensor_alloc_ids,json=persistentTensorAllocIds,proto3" json:"persistent_tensor_alloc_ids,omitempty"` + DeviceTempMemorySize int64 `protobuf:"varint,2,opt,name=device_temp_memory_size,json=deviceTempMemorySize,proto3" json:"device_temp_memory_size,omitempty"` // Deprecated: Do not use. + DevicePersistentMemorySize int64 `protobuf:"varint,4,opt,name=device_persistent_memory_size,json=devicePersistentMemorySize,proto3" json:"device_persistent_memory_size,omitempty"` // Deprecated: Do not use. + DevicePersistentTensorAllocIds []int64 `protobuf:"varint,6,rep,packed,name=device_persistent_tensor_alloc_ids,json=devicePersistentTensorAllocIds,proto3" json:"device_persistent_tensor_alloc_ids,omitempty"` // Deprecated: Do not use. + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryStats) Reset() { *m = MemoryStats{} } +func (m *MemoryStats) String() string { return proto.CompactTextString(m) } +func (*MemoryStats) ProtoMessage() {} +func (*MemoryStats) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{3} +} + +func (m *MemoryStats) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryStats.Unmarshal(m, b) +} +func (m *MemoryStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryStats.Marshal(b, m, deterministic) +} +func (m *MemoryStats) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryStats.Merge(m, src) +} +func (m *MemoryStats) XXX_Size() int { + return xxx_messageInfo_MemoryStats.Size(m) +} +func (m *MemoryStats) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryStats.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryStats proto.InternalMessageInfo + +func (m *MemoryStats) GetTempMemorySize() int64 { + if m != nil { + return m.TempMemorySize + } + return 0 +} + +func (m *MemoryStats) GetPersistentMemorySize() int64 { + if m != nil { + return m.PersistentMemorySize + } + return 0 +} + +func (m *MemoryStats) GetPersistentTensorAllocIds() []int64 { + if m != nil { + return m.PersistentTensorAllocIds + } + return nil +} + +// Deprecated: Do not use. +func (m *MemoryStats) GetDeviceTempMemorySize() int64 { + if m != nil { + return m.DeviceTempMemorySize + } + return 0 +} + +// Deprecated: Do not use. +func (m *MemoryStats) GetDevicePersistentMemorySize() int64 { + if m != nil { + return m.DevicePersistentMemorySize + } + return 0 +} + +// Deprecated: Do not use. +func (m *MemoryStats) GetDevicePersistentTensorAllocIds() []int64 { + if m != nil { + return m.DevicePersistentTensorAllocIds + } + return nil +} + +// Time/size stats recorded for a single execution of a graph node. +type NodeExecStats struct { + // TODO(tucker): Use some more compact form of node identity than + // the full string name. Either all processes should agree on a + // global id (cost_id?) for each node, or we should use a hash of + // the name. + NodeName string `protobuf:"bytes,1,opt,name=node_name,json=nodeName,proto3" json:"node_name,omitempty"` + AllStartMicros int64 `protobuf:"varint,2,opt,name=all_start_micros,json=allStartMicros,proto3" json:"all_start_micros,omitempty"` + OpStartRelMicros int64 `protobuf:"varint,3,opt,name=op_start_rel_micros,json=opStartRelMicros,proto3" json:"op_start_rel_micros,omitempty"` + OpEndRelMicros int64 `protobuf:"varint,4,opt,name=op_end_rel_micros,json=opEndRelMicros,proto3" json:"op_end_rel_micros,omitempty"` + AllEndRelMicros int64 `protobuf:"varint,5,opt,name=all_end_rel_micros,json=allEndRelMicros,proto3" json:"all_end_rel_micros,omitempty"` + Memory []*AllocatorMemoryUsed `protobuf:"bytes,6,rep,name=memory,proto3" json:"memory,omitempty"` + Output []*NodeOutput `protobuf:"bytes,7,rep,name=output,proto3" json:"output,omitempty"` + TimelineLabel string `protobuf:"bytes,8,opt,name=timeline_label,json=timelineLabel,proto3" json:"timeline_label,omitempty"` + ScheduledMicros int64 `protobuf:"varint,9,opt,name=scheduled_micros,json=scheduledMicros,proto3" json:"scheduled_micros,omitempty"` + ThreadId uint32 `protobuf:"varint,10,opt,name=thread_id,json=threadId,proto3" json:"thread_id,omitempty"` + ReferencedTensor []*allocation_description_go_proto.AllocationDescription `protobuf:"bytes,11,rep,name=referenced_tensor,json=referencedTensor,proto3" json:"referenced_tensor,omitempty"` + MemoryStats *MemoryStats `protobuf:"bytes,12,opt,name=memory_stats,json=memoryStats,proto3" json:"memory_stats,omitempty"` + AllStartNanos int64 `protobuf:"varint,13,opt,name=all_start_nanos,json=allStartNanos,proto3" json:"all_start_nanos,omitempty"` + OpStartRelNanos int64 `protobuf:"varint,14,opt,name=op_start_rel_nanos,json=opStartRelNanos,proto3" json:"op_start_rel_nanos,omitempty"` + OpEndRelNanos int64 `protobuf:"varint,15,opt,name=op_end_rel_nanos,json=opEndRelNanos,proto3" json:"op_end_rel_nanos,omitempty"` + AllEndRelNanos int64 `protobuf:"varint,16,opt,name=all_end_rel_nanos,json=allEndRelNanos,proto3" json:"all_end_rel_nanos,omitempty"` + ScheduledNanos int64 `protobuf:"varint,17,opt,name=scheduled_nanos,json=scheduledNanos,proto3" json:"scheduled_nanos,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NodeExecStats) Reset() { *m = NodeExecStats{} } +func (m *NodeExecStats) String() string { return proto.CompactTextString(m) } +func (*NodeExecStats) ProtoMessage() {} +func (*NodeExecStats) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{4} +} + +func (m *NodeExecStats) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NodeExecStats.Unmarshal(m, b) +} +func (m *NodeExecStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NodeExecStats.Marshal(b, m, deterministic) +} +func (m *NodeExecStats) XXX_Merge(src proto.Message) { + xxx_messageInfo_NodeExecStats.Merge(m, src) +} +func (m *NodeExecStats) XXX_Size() int { + return xxx_messageInfo_NodeExecStats.Size(m) +} +func (m *NodeExecStats) XXX_DiscardUnknown() { + xxx_messageInfo_NodeExecStats.DiscardUnknown(m) +} + +var xxx_messageInfo_NodeExecStats proto.InternalMessageInfo + +func (m *NodeExecStats) GetNodeName() string { + if m != nil { + return m.NodeName + } + return "" +} + +func (m *NodeExecStats) GetAllStartMicros() int64 { + if m != nil { + return m.AllStartMicros + } + return 0 +} + +func (m *NodeExecStats) GetOpStartRelMicros() int64 { + if m != nil { + return m.OpStartRelMicros + } + return 0 +} + +func (m *NodeExecStats) GetOpEndRelMicros() int64 { + if m != nil { + return m.OpEndRelMicros + } + return 0 +} + +func (m *NodeExecStats) GetAllEndRelMicros() int64 { + if m != nil { + return m.AllEndRelMicros + } + return 0 +} + +func (m *NodeExecStats) GetMemory() []*AllocatorMemoryUsed { + if m != nil { + return m.Memory + } + return nil +} + +func (m *NodeExecStats) GetOutput() []*NodeOutput { + if m != nil { + return m.Output + } + return nil +} + +func (m *NodeExecStats) GetTimelineLabel() string { + if m != nil { + return m.TimelineLabel + } + return "" +} + +func (m *NodeExecStats) GetScheduledMicros() int64 { + if m != nil { + return m.ScheduledMicros + } + return 0 +} + +func (m *NodeExecStats) GetThreadId() uint32 { + if m != nil { + return m.ThreadId + } + return 0 +} + +func (m *NodeExecStats) GetReferencedTensor() []*allocation_description_go_proto.AllocationDescription { + if m != nil { + return m.ReferencedTensor + } + return nil +} + +func (m *NodeExecStats) GetMemoryStats() *MemoryStats { + if m != nil { + return m.MemoryStats + } + return nil +} + +func (m *NodeExecStats) GetAllStartNanos() int64 { + if m != nil { + return m.AllStartNanos + } + return 0 +} + +func (m *NodeExecStats) GetOpStartRelNanos() int64 { + if m != nil { + return m.OpStartRelNanos + } + return 0 +} + +func (m *NodeExecStats) GetOpEndRelNanos() int64 { + if m != nil { + return m.OpEndRelNanos + } + return 0 +} + +func (m *NodeExecStats) GetAllEndRelNanos() int64 { + if m != nil { + return m.AllEndRelNanos + } + return 0 +} + +func (m *NodeExecStats) GetScheduledNanos() int64 { + if m != nil { + return m.ScheduledNanos + } + return 0 +} + +type DeviceStepStats struct { + Device string `protobuf:"bytes,1,opt,name=device,proto3" json:"device,omitempty"` + NodeStats []*NodeExecStats `protobuf:"bytes,2,rep,name=node_stats,json=nodeStats,proto3" json:"node_stats,omitempty"` + // Its key is thread id. + ThreadNames map[uint32]string `protobuf:"bytes,3,rep,name=thread_names,json=threadNames,proto3" json:"thread_names,omitempty" protobuf_key:"varint,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeviceStepStats) Reset() { *m = DeviceStepStats{} } +func (m *DeviceStepStats) String() string { return proto.CompactTextString(m) } +func (*DeviceStepStats) ProtoMessage() {} +func (*DeviceStepStats) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{5} +} + +func (m *DeviceStepStats) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeviceStepStats.Unmarshal(m, b) +} +func (m *DeviceStepStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeviceStepStats.Marshal(b, m, deterministic) +} +func (m *DeviceStepStats) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeviceStepStats.Merge(m, src) +} +func (m *DeviceStepStats) XXX_Size() int { + return xxx_messageInfo_DeviceStepStats.Size(m) +} +func (m *DeviceStepStats) XXX_DiscardUnknown() { + xxx_messageInfo_DeviceStepStats.DiscardUnknown(m) +} + +var xxx_messageInfo_DeviceStepStats proto.InternalMessageInfo + +func (m *DeviceStepStats) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *DeviceStepStats) GetNodeStats() []*NodeExecStats { + if m != nil { + return m.NodeStats + } + return nil +} + +func (m *DeviceStepStats) GetThreadNames() map[uint32]string { + if m != nil { + return m.ThreadNames + } + return nil +} + +type StepStats struct { + DevStats []*DeviceStepStats `protobuf:"bytes,1,rep,name=dev_stats,json=devStats,proto3" json:"dev_stats,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *StepStats) Reset() { *m = StepStats{} } +func (m *StepStats) String() string { return proto.CompactTextString(m) } +func (*StepStats) ProtoMessage() {} +func (*StepStats) Descriptor() ([]byte, []int) { + return fileDescriptor_1e915309f7ed52e5, []int{6} +} + +func (m *StepStats) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_StepStats.Unmarshal(m, b) +} +func (m *StepStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_StepStats.Marshal(b, m, deterministic) +} +func (m *StepStats) XXX_Merge(src proto.Message) { + xxx_messageInfo_StepStats.Merge(m, src) +} +func (m *StepStats) XXX_Size() int { + return xxx_messageInfo_StepStats.Size(m) +} +func (m *StepStats) XXX_DiscardUnknown() { + xxx_messageInfo_StepStats.DiscardUnknown(m) +} + +var xxx_messageInfo_StepStats proto.InternalMessageInfo + +func (m *StepStats) GetDevStats() []*DeviceStepStats { + if m != nil { + return m.DevStats + } + return nil +} + +func init() { + proto.RegisterType((*AllocationRecord)(nil), "tensorflow.AllocationRecord") + proto.RegisterType((*AllocatorMemoryUsed)(nil), "tensorflow.AllocatorMemoryUsed") + proto.RegisterType((*NodeOutput)(nil), "tensorflow.NodeOutput") + proto.RegisterType((*MemoryStats)(nil), "tensorflow.MemoryStats") + proto.RegisterType((*NodeExecStats)(nil), "tensorflow.NodeExecStats") + proto.RegisterType((*DeviceStepStats)(nil), "tensorflow.DeviceStepStats") + proto.RegisterMapType((map[uint32]string)(nil), "tensorflow.DeviceStepStats.ThreadNamesEntry") + proto.RegisterType((*StepStats)(nil), "tensorflow.StepStats") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/step_stats.proto", fileDescriptor_1e915309f7ed52e5) +} + +var fileDescriptor_1e915309f7ed52e5 = []byte{ + // 963 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x56, 0xcf, 0x4f, 0xe3, 0x46, + 0x14, 0x96, 0x13, 0x48, 0xc9, 0x0b, 0x01, 0x67, 0x40, 0xac, 0x0b, 0xa5, 0xcb, 0x46, 0x6a, 0x37, + 0xf4, 0x47, 0x90, 0xd8, 0xaa, 0xa5, 0x2b, 0xb5, 0x52, 0xd1, 0xe6, 0x80, 0xba, 0x9b, 0xa5, 0x86, + 0xed, 0xa1, 0x17, 0xcb, 0xd8, 0x0f, 0xb0, 0xb0, 0x3d, 0xd6, 0xcc, 0x24, 0x5b, 0xf6, 0xda, 0x3f, + 0xa1, 0xe7, 0xfe, 0x97, 0x3d, 0xf4, 0x54, 0x55, 0xf3, 0x66, 0x62, 0x3b, 0x09, 0xf4, 0x36, 0xfe, + 0xe6, 0x7b, 0xcf, 0xef, 0xcd, 0xfb, 0xbe, 0xb1, 0xe1, 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DO NOT EDIT. +// source: tensorflow/core/framework/summary.proto + +package summary_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type DataClass int32 + +const ( + // Unknown data class, used (implicitly) for legacy data. Will not be + // processed by data ingestion pipelines. + DataClass_DATA_CLASS_UNKNOWN DataClass = 0 + // Scalar time series. Each `Value` for the corresponding tag must have + // `tensor` set to a rank-0 tensor of floating-point dtype, which will be + // converted to float64. + DataClass_DATA_CLASS_SCALAR DataClass = 1 + // Tensor time series. Each `Value` for the corresponding tag must have + // `tensor` set. The tensor value is arbitrary, but should be small to + // accommodate direct storage in database backends: an upper bound of a few + // kilobytes is a reasonable rule of thumb. + DataClass_DATA_CLASS_TENSOR DataClass = 2 + // Blob sequence time series. Each `Value` for the corresponding tag must + // have `tensor` set to a rank-1 tensor of bytestring dtype. + DataClass_DATA_CLASS_BLOB_SEQUENCE DataClass = 3 +) + +var DataClass_name = map[int32]string{ + 0: "DATA_CLASS_UNKNOWN", + 1: "DATA_CLASS_SCALAR", + 2: "DATA_CLASS_TENSOR", + 3: "DATA_CLASS_BLOB_SEQUENCE", +} + +var DataClass_value = map[string]int32{ + "DATA_CLASS_UNKNOWN": 0, + "DATA_CLASS_SCALAR": 1, + "DATA_CLASS_TENSOR": 2, + "DATA_CLASS_BLOB_SEQUENCE": 3, +} + +func (x DataClass) String() string { + return proto.EnumName(DataClass_name, int32(x)) +} + +func (DataClass) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{0} +} + +// Metadata associated with a series of Summary data +type SummaryDescription struct { + // Hint on how plugins should process the data in this series. + // Supported values include "scalar", "histogram", "image", "audio" + TypeHint string `protobuf:"bytes,1,opt,name=type_hint,json=typeHint,proto3" json:"type_hint,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SummaryDescription) Reset() { *m = SummaryDescription{} } +func (m *SummaryDescription) String() string { return proto.CompactTextString(m) } +func (*SummaryDescription) ProtoMessage() {} +func (*SummaryDescription) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{0} +} + +func (m *SummaryDescription) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SummaryDescription.Unmarshal(m, b) +} +func (m *SummaryDescription) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SummaryDescription.Marshal(b, m, deterministic) +} +func (m *SummaryDescription) XXX_Merge(src proto.Message) { + xxx_messageInfo_SummaryDescription.Merge(m, src) +} +func (m *SummaryDescription) XXX_Size() int { + return xxx_messageInfo_SummaryDescription.Size(m) +} +func (m *SummaryDescription) XXX_DiscardUnknown() { + xxx_messageInfo_SummaryDescription.DiscardUnknown(m) +} + +var xxx_messageInfo_SummaryDescription proto.InternalMessageInfo + +func (m *SummaryDescription) GetTypeHint() string { + if m != nil { + return m.TypeHint + } + return "" +} + +// Serialization format for histogram module in +// core/lib/histogram/histogram.h +type HistogramProto struct { + Min float64 `protobuf:"fixed64,1,opt,name=min,proto3" json:"min,omitempty"` + Max float64 `protobuf:"fixed64,2,opt,name=max,proto3" json:"max,omitempty"` + Num float64 `protobuf:"fixed64,3,opt,name=num,proto3" json:"num,omitempty"` + Sum float64 `protobuf:"fixed64,4,opt,name=sum,proto3" json:"sum,omitempty"` + SumSquares float64 `protobuf:"fixed64,5,opt,name=sum_squares,json=sumSquares,proto3" json:"sum_squares,omitempty"` + // Parallel arrays encoding the bucket boundaries and the bucket values. + // bucket(i) is the count for the bucket i. The range for + // a bucket is: + // i == 0: -DBL_MAX .. bucket_limit(0) + // i != 0: bucket_limit(i-1) .. bucket_limit(i) + BucketLimit []float64 `protobuf:"fixed64,6,rep,packed,name=bucket_limit,json=bucketLimit,proto3" json:"bucket_limit,omitempty"` + Bucket []float64 `protobuf:"fixed64,7,rep,packed,name=bucket,proto3" json:"bucket,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *HistogramProto) Reset() { *m = HistogramProto{} } +func (m *HistogramProto) String() string { return proto.CompactTextString(m) } +func (*HistogramProto) ProtoMessage() {} +func (*HistogramProto) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{1} +} + +func (m *HistogramProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_HistogramProto.Unmarshal(m, b) +} +func (m *HistogramProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_HistogramProto.Marshal(b, m, deterministic) +} +func (m *HistogramProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_HistogramProto.Merge(m, src) +} +func (m *HistogramProto) XXX_Size() int { + return xxx_messageInfo_HistogramProto.Size(m) +} +func (m *HistogramProto) XXX_DiscardUnknown() { + xxx_messageInfo_HistogramProto.DiscardUnknown(m) +} + +var xxx_messageInfo_HistogramProto proto.InternalMessageInfo + +func (m *HistogramProto) GetMin() float64 { + if m != nil { + return m.Min + } + return 0 +} + +func (m *HistogramProto) GetMax() float64 { + if m != nil { + return m.Max + } + return 0 +} + +func (m *HistogramProto) GetNum() float64 { + if m != nil { + return m.Num + } + return 0 +} + +func (m *HistogramProto) GetSum() float64 { + if m != nil { + return m.Sum + } + return 0 +} + +func (m *HistogramProto) GetSumSquares() float64 { + if m != nil { + return m.SumSquares + } + return 0 +} + +func (m *HistogramProto) GetBucketLimit() []float64 { + if m != nil { + return m.BucketLimit + } + return nil +} + +func (m *HistogramProto) GetBucket() []float64 { + if m != nil { + return m.Bucket + } + return nil +} + +// A SummaryMetadata encapsulates information on which plugins are able to make +// use of a certain summary value. +type SummaryMetadata struct { + // Data that associates a summary with a certain plugin. + PluginData *SummaryMetadata_PluginData `protobuf:"bytes,1,opt,name=plugin_data,json=pluginData,proto3" json:"plugin_data,omitempty"` + // Display name for viewing in TensorBoard. + DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"` + // Longform readable description of the summary sequence. Markdown supported. + SummaryDescription string `protobuf:"bytes,3,opt,name=summary_description,json=summaryDescription,proto3" json:"summary_description,omitempty"` + // Class of data stored in this time series. Required for compatibility with + // TensorBoard's generic data facilities (`DataProvider`, et al.). This value + // imposes constraints on the dtype and shape of the corresponding tensor + // values. See `DataClass` docs for details. + DataClass DataClass `protobuf:"varint,4,opt,name=data_class,json=dataClass,proto3,enum=tensorflow.DataClass" json:"data_class,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SummaryMetadata) Reset() { *m = SummaryMetadata{} } +func (m *SummaryMetadata) String() string { return proto.CompactTextString(m) } +func (*SummaryMetadata) ProtoMessage() {} +func (*SummaryMetadata) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{2} +} + +func (m *SummaryMetadata) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SummaryMetadata.Unmarshal(m, b) +} +func (m *SummaryMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SummaryMetadata.Marshal(b, m, deterministic) +} +func (m *SummaryMetadata) XXX_Merge(src proto.Message) { + xxx_messageInfo_SummaryMetadata.Merge(m, src) +} +func (m *SummaryMetadata) XXX_Size() int { + return xxx_messageInfo_SummaryMetadata.Size(m) +} +func (m *SummaryMetadata) XXX_DiscardUnknown() { + xxx_messageInfo_SummaryMetadata.DiscardUnknown(m) +} + +var xxx_messageInfo_SummaryMetadata proto.InternalMessageInfo + +func (m *SummaryMetadata) GetPluginData() *SummaryMetadata_PluginData { + if m != nil { + return m.PluginData + } + return nil +} + +func (m *SummaryMetadata) GetDisplayName() string { + if m != nil { + return m.DisplayName + } + return "" +} + +func (m *SummaryMetadata) GetSummaryDescription() string { + if m != nil { + return m.SummaryDescription + } + return "" +} + +func (m *SummaryMetadata) GetDataClass() DataClass { + if m != nil { + return m.DataClass + } + return DataClass_DATA_CLASS_UNKNOWN +} + +type SummaryMetadata_PluginData struct { + // The name of the plugin this data pertains to. + PluginName string `protobuf:"bytes,1,opt,name=plugin_name,json=pluginName,proto3" json:"plugin_name,omitempty"` + // The content to store for the plugin. The best practice is for this to be + // a binary serialized protocol buffer. + Content []byte `protobuf:"bytes,2,opt,name=content,proto3" json:"content,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SummaryMetadata_PluginData) Reset() { *m = SummaryMetadata_PluginData{} } +func (m *SummaryMetadata_PluginData) String() string { return proto.CompactTextString(m) } +func (*SummaryMetadata_PluginData) ProtoMessage() {} +func (*SummaryMetadata_PluginData) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{2, 0} +} + +func (m *SummaryMetadata_PluginData) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SummaryMetadata_PluginData.Unmarshal(m, b) +} +func (m *SummaryMetadata_PluginData) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SummaryMetadata_PluginData.Marshal(b, m, deterministic) +} +func (m *SummaryMetadata_PluginData) XXX_Merge(src proto.Message) { + xxx_messageInfo_SummaryMetadata_PluginData.Merge(m, src) +} +func (m *SummaryMetadata_PluginData) XXX_Size() int { + return xxx_messageInfo_SummaryMetadata_PluginData.Size(m) +} +func (m *SummaryMetadata_PluginData) XXX_DiscardUnknown() { + xxx_messageInfo_SummaryMetadata_PluginData.DiscardUnknown(m) +} + +var xxx_messageInfo_SummaryMetadata_PluginData proto.InternalMessageInfo + +func (m *SummaryMetadata_PluginData) GetPluginName() string { + if m != nil { + return m.PluginName + } + return "" +} + +func (m *SummaryMetadata_PluginData) GetContent() []byte { + if m != nil { + return m.Content + } + return nil +} + +// A Summary is a set of named values to be displayed by the +// visualizer. +// +// Summaries are produced regularly during training, as controlled by +// the "summary_interval_secs" attribute of the training operation. +// Summaries are also produced at the end of an evaluation. +type Summary struct { + // Set of values for the summary. + Value []*Summary_Value `protobuf:"bytes,1,rep,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Summary) Reset() { *m = Summary{} } +func (m *Summary) String() string { return proto.CompactTextString(m) } +func (*Summary) ProtoMessage() {} +func (*Summary) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{3} +} + +func (m *Summary) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Summary.Unmarshal(m, b) +} +func (m *Summary) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Summary.Marshal(b, m, deterministic) +} +func (m *Summary) XXX_Merge(src proto.Message) { + xxx_messageInfo_Summary.Merge(m, src) +} +func (m *Summary) XXX_Size() int { + return xxx_messageInfo_Summary.Size(m) +} +func (m *Summary) XXX_DiscardUnknown() { + xxx_messageInfo_Summary.DiscardUnknown(m) +} + +var xxx_messageInfo_Summary proto.InternalMessageInfo + +func (m *Summary) GetValue() []*Summary_Value { + if m != nil { + return m.Value + } + return nil +} + +type Summary_Image struct { + // Dimensions of the image. + Height int32 `protobuf:"varint,1,opt,name=height,proto3" json:"height,omitempty"` + Width int32 `protobuf:"varint,2,opt,name=width,proto3" json:"width,omitempty"` + // Valid colorspace values are + // 1 - grayscale + // 2 - grayscale + alpha + // 3 - RGB + // 4 - RGBA + // 5 - DIGITAL_YUV + // 6 - BGRA + Colorspace int32 `protobuf:"varint,3,opt,name=colorspace,proto3" json:"colorspace,omitempty"` + // Image data in encoded format. All image formats supported by + // image_codec::CoderUtil can be stored here. + EncodedImageString []byte `protobuf:"bytes,4,opt,name=encoded_image_string,json=encodedImageString,proto3" json:"encoded_image_string,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Summary_Image) Reset() { *m = Summary_Image{} } +func (m *Summary_Image) String() string { return proto.CompactTextString(m) } +func (*Summary_Image) ProtoMessage() {} +func (*Summary_Image) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{3, 0} +} + +func (m *Summary_Image) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Summary_Image.Unmarshal(m, b) +} +func (m *Summary_Image) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Summary_Image.Marshal(b, m, deterministic) +} +func (m *Summary_Image) XXX_Merge(src proto.Message) { + xxx_messageInfo_Summary_Image.Merge(m, src) +} +func (m *Summary_Image) XXX_Size() int { + return xxx_messageInfo_Summary_Image.Size(m) +} +func (m *Summary_Image) XXX_DiscardUnknown() { + xxx_messageInfo_Summary_Image.DiscardUnknown(m) +} + +var xxx_messageInfo_Summary_Image proto.InternalMessageInfo + +func (m *Summary_Image) GetHeight() int32 { + if m != nil { + return m.Height + } + return 0 +} + +func (m *Summary_Image) GetWidth() int32 { + if m != nil { + return m.Width + } + return 0 +} + +func (m *Summary_Image) GetColorspace() int32 { + if m != nil { + return m.Colorspace + } + return 0 +} + +func (m *Summary_Image) GetEncodedImageString() []byte { + if m != nil { + return m.EncodedImageString + } + return nil +} + +type Summary_Audio struct { + // Sample rate of the audio in Hz. + SampleRate float32 `protobuf:"fixed32,1,opt,name=sample_rate,json=sampleRate,proto3" json:"sample_rate,omitempty"` + // Number of channels of audio. + NumChannels int64 `protobuf:"varint,2,opt,name=num_channels,json=numChannels,proto3" json:"num_channels,omitempty"` + // Length of the audio in frames (samples per channel). + LengthFrames int64 `protobuf:"varint,3,opt,name=length_frames,json=lengthFrames,proto3" json:"length_frames,omitempty"` + // Encoded audio data and its associated RFC 2045 content type (e.g. + // "audio/wav"). + EncodedAudioString []byte `protobuf:"bytes,4,opt,name=encoded_audio_string,json=encodedAudioString,proto3" json:"encoded_audio_string,omitempty"` + ContentType string `protobuf:"bytes,5,opt,name=content_type,json=contentType,proto3" json:"content_type,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Summary_Audio) Reset() { *m = Summary_Audio{} } +func (m *Summary_Audio) String() string { return proto.CompactTextString(m) } +func (*Summary_Audio) ProtoMessage() {} +func (*Summary_Audio) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{3, 1} +} + +func (m *Summary_Audio) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Summary_Audio.Unmarshal(m, b) +} +func (m *Summary_Audio) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Summary_Audio.Marshal(b, m, deterministic) +} +func (m *Summary_Audio) XXX_Merge(src proto.Message) { + xxx_messageInfo_Summary_Audio.Merge(m, src) +} +func (m *Summary_Audio) XXX_Size() int { + return xxx_messageInfo_Summary_Audio.Size(m) +} +func (m *Summary_Audio) XXX_DiscardUnknown() { + xxx_messageInfo_Summary_Audio.DiscardUnknown(m) +} + +var xxx_messageInfo_Summary_Audio proto.InternalMessageInfo + +func (m *Summary_Audio) GetSampleRate() float32 { + if m != nil { + return m.SampleRate + } + return 0 +} + +func (m *Summary_Audio) GetNumChannels() int64 { + if m != nil { + return m.NumChannels + } + return 0 +} + +func (m *Summary_Audio) GetLengthFrames() int64 { + if m != nil { + return m.LengthFrames + } + return 0 +} + +func (m *Summary_Audio) GetEncodedAudioString() []byte { + if m != nil { + return m.EncodedAudioString + } + return nil +} + +func (m *Summary_Audio) GetContentType() string { + if m != nil { + return m.ContentType + } + return "" +} + +type Summary_Value struct { + // This field is deprecated and will not be set. + NodeName string `protobuf:"bytes,7,opt,name=node_name,json=nodeName,proto3" json:"node_name,omitempty"` + // Tag name for the data. Used by TensorBoard plugins to organize data. Tags + // are often organized by scope (which contains slashes to convey + // hierarchy). For example: foo/bar/0 + Tag string `protobuf:"bytes,1,opt,name=tag,proto3" json:"tag,omitempty"` + // Contains metadata on the summary value such as which plugins may use it. + // Take note that many summary values may lack a metadata field. This is + // because the FileWriter only keeps a metadata object on the first summary + // value with a certain tag for each tag. TensorBoard then remembers which + // tags are associated with which plugins. This saves space. + Metadata *SummaryMetadata `protobuf:"bytes,9,opt,name=metadata,proto3" json:"metadata,omitempty"` + // Value associated with the tag. + // + // Types that are valid to be assigned to Value: + // *Summary_Value_SimpleValue + // *Summary_Value_ObsoleteOldStyleHistogram + // *Summary_Value_Image + // *Summary_Value_Histo + // *Summary_Value_Audio + // *Summary_Value_Tensor + Value isSummary_Value_Value `protobuf_oneof:"value"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Summary_Value) Reset() { *m = Summary_Value{} } +func (m *Summary_Value) String() string { return proto.CompactTextString(m) } +func (*Summary_Value) ProtoMessage() {} +func (*Summary_Value) Descriptor() ([]byte, []int) { + return fileDescriptor_80d4b41d3e8d8b09, []int{3, 2} +} + +func (m *Summary_Value) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Summary_Value.Unmarshal(m, b) +} +func (m *Summary_Value) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Summary_Value.Marshal(b, m, deterministic) +} +func (m *Summary_Value) XXX_Merge(src proto.Message) { + xxx_messageInfo_Summary_Value.Merge(m, src) +} +func (m *Summary_Value) XXX_Size() int { + return xxx_messageInfo_Summary_Value.Size(m) +} +func (m *Summary_Value) XXX_DiscardUnknown() { + xxx_messageInfo_Summary_Value.DiscardUnknown(m) +} + +var xxx_messageInfo_Summary_Value proto.InternalMessageInfo + +func (m *Summary_Value) GetNodeName() string { + if m != nil { + return m.NodeName + } + return "" +} + +func (m *Summary_Value) GetTag() string { + if m != nil { + return m.Tag + } + return "" +} + +func (m *Summary_Value) GetMetadata() *SummaryMetadata { + if m != nil { + return m.Metadata + } + return nil +} + +type isSummary_Value_Value interface { + isSummary_Value_Value() +} + +type Summary_Value_SimpleValue struct { + SimpleValue float32 `protobuf:"fixed32,2,opt,name=simple_value,json=simpleValue,proto3,oneof"` +} + +type Summary_Value_ObsoleteOldStyleHistogram struct { + ObsoleteOldStyleHistogram []byte `protobuf:"bytes,3,opt,name=obsolete_old_style_histogram,json=obsoleteOldStyleHistogram,proto3,oneof"` +} + +type Summary_Value_Image struct { + Image *Summary_Image `protobuf:"bytes,4,opt,name=image,proto3,oneof"` +} + +type Summary_Value_Histo struct { + Histo *HistogramProto `protobuf:"bytes,5,opt,name=histo,proto3,oneof"` +} + +type Summary_Value_Audio struct { + Audio *Summary_Audio `protobuf:"bytes,6,opt,name=audio,proto3,oneof"` +} + +type Summary_Value_Tensor struct { + Tensor *tensor_go_proto.TensorProto `protobuf:"bytes,8,opt,name=tensor,proto3,oneof"` +} + +func (*Summary_Value_SimpleValue) isSummary_Value_Value() {} + +func (*Summary_Value_ObsoleteOldStyleHistogram) isSummary_Value_Value() {} + +func (*Summary_Value_Image) isSummary_Value_Value() {} + +func (*Summary_Value_Histo) isSummary_Value_Value() {} + +func (*Summary_Value_Audio) isSummary_Value_Value() {} + +func (*Summary_Value_Tensor) isSummary_Value_Value() {} + +func (m *Summary_Value) GetValue() isSummary_Value_Value { + if m != nil { + return m.Value + } + return nil +} + +func (m *Summary_Value) GetSimpleValue() float32 { + if x, ok := m.GetValue().(*Summary_Value_SimpleValue); ok { + return x.SimpleValue + } + return 0 +} + +func (m *Summary_Value) GetObsoleteOldStyleHistogram() []byte { + if x, ok := m.GetValue().(*Summary_Value_ObsoleteOldStyleHistogram); ok { + return x.ObsoleteOldStyleHistogram + } + return nil +} + +func (m *Summary_Value) GetImage() *Summary_Image { + if x, ok := m.GetValue().(*Summary_Value_Image); ok { + return x.Image + } + return nil +} + +func (m *Summary_Value) GetHisto() *HistogramProto { + if x, ok := m.GetValue().(*Summary_Value_Histo); ok { + return x.Histo + } + return nil +} + +func (m *Summary_Value) GetAudio() *Summary_Audio { + if x, ok := m.GetValue().(*Summary_Value_Audio); ok { + return x.Audio + } + return nil +} + +func (m *Summary_Value) GetTensor() *tensor_go_proto.TensorProto { + if x, ok := m.GetValue().(*Summary_Value_Tensor); ok { + return x.Tensor + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*Summary_Value) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*Summary_Value_SimpleValue)(nil), + (*Summary_Value_ObsoleteOldStyleHistogram)(nil), + (*Summary_Value_Image)(nil), + (*Summary_Value_Histo)(nil), + (*Summary_Value_Audio)(nil), + (*Summary_Value_Tensor)(nil), + } +} + +func init() { + proto.RegisterEnum("tensorflow.DataClass", DataClass_name, DataClass_value) + proto.RegisterType((*SummaryDescription)(nil), "tensorflow.SummaryDescription") + proto.RegisterType((*HistogramProto)(nil), "tensorflow.HistogramProto") + proto.RegisterType((*SummaryMetadata)(nil), "tensorflow.SummaryMetadata") + proto.RegisterType((*SummaryMetadata_PluginData)(nil), "tensorflow.SummaryMetadata.PluginData") + proto.RegisterType((*Summary)(nil), "tensorflow.Summary") + proto.RegisterType((*Summary_Image)(nil), "tensorflow.Summary.Image") + proto.RegisterType((*Summary_Audio)(nil), "tensorflow.Summary.Audio") + proto.RegisterType((*Summary_Value)(nil), "tensorflow.Summary.Value") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/summary.proto", fileDescriptor_80d4b41d3e8d8b09) +} + +var fileDescriptor_80d4b41d3e8d8b09 = []byte{ + // 872 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x55, 0x41, 0x73, 0x1b, 0x35, + 0x14, 0xce, 0xda, 0xac, 0x13, 0xbf, 0x75, 0x8b, 0x11, 0x6d, 0xd9, 0xba, 0x1d, 0x28, 0xe9, 0x50, + 0x32, 0x1c, 0x6c, 0x12, 0x98, 0xe1, 0x6c, 0x27, 0xa1, 0x66, 0x08, 0x4e, 0x90, 0x53, 0x98, 0xe1, + 0xa2, 0x51, 0x76, 0xd5, 0xf5, 0x4e, 0x57, 0x92, 0x59, 0x69, 0x49, 0x7d, 0xe1, 0xca, 0xaf, 0xe9, + 0x3f, 0xe8, 0x95, 0xff, 0xc4, 0x91, 0xd1, 0x93, 0xe2, 0x98, 0x92, 0xf4, 0xf6, 0xf4, 0xe9, 0x7b, + 0xfb, 0x3e, 0x7d, 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0xc4, + 0xc9, 0x76, 0x98, 0x94, 0xaf, 0x34, 0x74, 0xd7, 0xf3, 0x49, 0x1e, 0x00, 0x39, 0x1a, 0x9f, 0x8f, + 0xd9, 0xe1, 0xc9, 0x78, 0x3e, 0x67, 0x2f, 0x66, 0x3f, 0xce, 0x4e, 0x7f, 0x9d, 0xf5, 0xb7, 0xc8, + 0x7d, 0xf8, 0x68, 0x03, 0x9f, 0x1f, 0x8e, 0x4f, 0xc6, 0xb4, 0x1f, 0xbd, 0x03, 0x9f, 0x1f, 0xcf, + 0xe6, 0xa7, 0xb4, 0xdf, 0x22, 0x8f, 0x21, 0xdd, 0x80, 0x27, 0x27, 0xa7, 0x13, 0x36, 0x3f, 0xfe, + 0xf9, 0xc5, 0xf1, 0xec, 0xf0, 0xb8, 0xdf, 0x9e, 0xfc, 0x09, 0xa9, 0xae, 0x8b, 0x4d, 0x85, 0xeb, + 0xa7, 0x7f, 0x72, 0x27, 0x1c, 0x10, 0xd5, 0x9a, 0xb3, 0xe8, 0xb7, 0x59, 0x51, 0xda, 0x45, 0x73, + 0x31, 0xcc, 0xb4, 0x1c, 0x6d, 0xfc, 0x31, 0x6e, 0x0e, 0x0b, 0x7d, 0xcb, 0xff, 0x87, 0x15, 0x9a, + 0xe1, 0xef, 0xe4, 0x9f, 0x28, 0xba, 0xe8, 0x60, 0xf4, 0xcd, 0xbf, 0x01, 0x00, 0x00, 0xff, 0xff, + 0x9c, 0xe3, 0xe7, 0x5a, 0xb7, 0x06, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/tensor_description_go_proto/tensor_description.pb.go b/tensorflow/go/core/framework/tensor_description_go_proto/tensor_description.pb.go new file mode 100644 index 0000000..f5117ac --- /dev/null +++ b/tensorflow/go/core/framework/tensor_description_go_proto/tensor_description.pb.go @@ -0,0 +1,111 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/tensor_description.proto + +package tensor_description_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + allocation_description_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/allocation_description_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type TensorDescription struct { + // Data type of tensor elements + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + // Shape of the tensor. + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + // Information about the size and allocator used for the data + AllocationDescription *allocation_description_go_proto.AllocationDescription `protobuf:"bytes,4,opt,name=allocation_description,json=allocationDescription,proto3" json:"allocation_description,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorDescription) Reset() { *m = TensorDescription{} } +func (m *TensorDescription) String() string { return proto.CompactTextString(m) } +func (*TensorDescription) ProtoMessage() {} +func (*TensorDescription) Descriptor() ([]byte, []int) { + return fileDescriptor_aa203ffb9e427669, []int{0} +} + +func (m *TensorDescription) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorDescription.Unmarshal(m, b) +} +func (m *TensorDescription) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorDescription.Marshal(b, m, deterministic) +} +func (m *TensorDescription) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorDescription.Merge(m, src) +} +func (m *TensorDescription) XXX_Size() int { + return xxx_messageInfo_TensorDescription.Size(m) +} +func (m *TensorDescription) XXX_DiscardUnknown() { + xxx_messageInfo_TensorDescription.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorDescription proto.InternalMessageInfo + +func (m *TensorDescription) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *TensorDescription) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *TensorDescription) GetAllocationDescription() *allocation_description_go_proto.AllocationDescription { + if m != nil { + return m.AllocationDescription + } + return nil +} + +func init() { + proto.RegisterType((*TensorDescription)(nil), "tensorflow.TensorDescription") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/tensor_description.proto", fileDescriptor_aa203ffb9e427669) +} + +var fileDescriptor_aa203ffb9e427669 = []byte{ + // 263 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xe2, 0x32, 0x2a, 0x49, 0xcd, 0x2b, + 0xce, 0x2f, 0x4a, 0xcb, 0xc9, 0x2f, 0xd7, 0x4f, 0xce, 0x2f, 0x4a, 0xd5, 0x4f, 0x2b, 0x4a, 0xcc, + 0x4d, 0x2d, 0xcf, 0x2f, 0xca, 0xd6, 0x87, 0xc8, 0xc4, 0xa7, 0xa4, 0x16, 0x27, 0x17, 0x65, 0x16, + 0x94, 0x64, 0xe6, 0xe7, 0xe9, 0x15, 0x14, 0xe5, 0x97, 0xe4, 0x0b, 0x71, 0x21, 0xf4, 0x48, 0x99, + 0xe1, 0xd6, 0x9f, 0x98, 0x93, 0x93, 0x9f, 0x9c, 0x08, 0xd2, 0x87, 0x69, 0x86, 0x94, 0x0e, 0x41, + 0x7b, 0x8b, 0x33, 0x12, 0x0b, 0x52, 0xa1, 0xaa, 0x55, 0xf1, 0xa8, 0xae, 0x2c, 0x48, 0x2d, 0x86, + 0x28, 0x53, 0x3a, 0xcb, 0xc8, 0x25, 0x18, 0x02, 0x56, 0xe9, 0x82, 0xb0, 0x50, 0x48, 0x8b, 0x8b, + 0x35, 0x05, 0xa4, 0x4a, 0x82, 0x51, 0x81, 0x51, 0x83, 0xcf, 0x48, 0x44, 0x0f, 0x61, 0x98, 0x9e, + 0x4b, 0x62, 0x49, 0x62, 0x48, 0x65, 0x41, 0x6a, 0x10, 0x44, 0x89, 0x90, 0x11, 0x17, 0x2b, 0xd8, + 0x5e, 0x09, 0x26, 0x05, 0x46, 0x0d, 0x6e, 0x23, 0x19, 0x64, 0xb5, 0x10, 0x93, 0x83, 0x41, 0xd2, + 0x01, 0x20, 0xeb, 0x82, 0x20, 0x4a, 0x85, 0x22, 0xb8, 0xc4, 0xb0, 0x7b, 0x55, 0x82, 0x05, 0x6c, + 0x88, 0x22, 0xb2, 0x21, 0x8e, 0x70, 0x95, 0x48, 0x4e, 0x0c, 0x12, 0x4d, 0xc4, 0x26, 0xec, 0x34, + 0x99, 0x91, 0x4b, 0x22, 0xbf, 0x28, 0x1d, 0x59, 0x3f, 0xdc, 0xe3, 0x4e, 0xe2, 0x18, 0x3e, 0x05, + 0xbb, 0xaa, 0x38, 0x80, 0x31, 0x2a, 0x32, 0x3d, 0xb3, 0x24, 0xa3, 0x34, 0x49, 0x2f, 0x39, 0x3f, + 0x57, 0x1f, 0x29, 0xe4, 0xb0, 0x33, 0xd3, 0xf3, 0x09, 0x47, 0x7c, 0x7c, 0x7a, 0x7e, 0x3c, 0x38, + 0x88, 0x7f, 0x30, 0x32, 0x26, 0xb1, 0x81, 0x59, 0xc6, 0x80, 0x00, 0x00, 0x00, 0xff, 0xff, 0xda, + 0xb9, 0x9f, 0x68, 0x3b, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/tensor_go_proto/tensor.pb.go b/tensorflow/go/core/framework/tensor_go_proto/tensor.pb.go new file mode 100644 index 0000000..5838374 --- /dev/null +++ b/tensorflow/go/core/framework/tensor_go_proto/tensor.pb.go @@ -0,0 +1,327 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/tensor.proto + +package tensor_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + resource_handle_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/resource_handle_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing a tensor. +type TensorProto struct { + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + // Shape of the tensor. TODO(touts): sort out the 0-rank issues. + TensorShape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=tensor_shape,json=tensorShape,proto3" json:"tensor_shape,omitempty"` + // Version number. + // + // In version 0, if the "repeated xxx" representations contain only one + // element, that element is repeated to fill the shape. This makes it easy + // to represent a constant Tensor with a single value. + VersionNumber int32 `protobuf:"varint,3,opt,name=version_number,json=versionNumber,proto3" json:"version_number,omitempty"` + // Serialized raw tensor content from either Tensor::AsProtoTensorContent or + // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation + // can be used for all tensor types. The purpose of this representation is to + // reduce serialization overhead during RPC call by avoiding serialization of + // many repeated small items. + TensorContent []byte `protobuf:"bytes,4,opt,name=tensor_content,json=tensorContent,proto3" json:"tensor_content,omitempty"` + // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll + // have some pointless zero padding for each value here. + HalfVal []int32 `protobuf:"varint,13,rep,packed,name=half_val,json=halfVal,proto3" json:"half_val,omitempty"` + // DT_FLOAT. + FloatVal []float32 `protobuf:"fixed32,5,rep,packed,name=float_val,json=floatVal,proto3" json:"float_val,omitempty"` + // DT_DOUBLE. + DoubleVal []float64 `protobuf:"fixed64,6,rep,packed,name=double_val,json=doubleVal,proto3" json:"double_val,omitempty"` + // DT_INT32, DT_INT16, DT_INT8, DT_UINT8. + IntVal []int32 `protobuf:"varint,7,rep,packed,name=int_val,json=intVal,proto3" json:"int_val,omitempty"` + // DT_STRING + StringVal [][]byte `protobuf:"bytes,8,rep,name=string_val,json=stringVal,proto3" json:"string_val,omitempty"` + // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real + // and imaginary parts of i-th single precision complex. + ScomplexVal []float32 `protobuf:"fixed32,9,rep,packed,name=scomplex_val,json=scomplexVal,proto3" json:"scomplex_val,omitempty"` + // DT_INT64 + Int64Val []int64 `protobuf:"varint,10,rep,packed,name=int64_val,json=int64Val,proto3" json:"int64_val,omitempty"` + // DT_BOOL + BoolVal []bool `protobuf:"varint,11,rep,packed,name=bool_val,json=boolVal,proto3" json:"bool_val,omitempty"` + // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real + // and imaginary parts of i-th double precision complex. + DcomplexVal []float64 `protobuf:"fixed64,12,rep,packed,name=dcomplex_val,json=dcomplexVal,proto3" json:"dcomplex_val,omitempty"` + // DT_RESOURCE + ResourceHandleVal []*resource_handle_go_proto.ResourceHandleProto `protobuf:"bytes,14,rep,name=resource_handle_val,json=resourceHandleVal,proto3" json:"resource_handle_val,omitempty"` + // DT_VARIANT + VariantVal []*VariantTensorDataProto `protobuf:"bytes,15,rep,name=variant_val,json=variantVal,proto3" json:"variant_val,omitempty"` + // DT_UINT32 + Uint32Val []uint32 `protobuf:"varint,16,rep,packed,name=uint32_val,json=uint32Val,proto3" json:"uint32_val,omitempty"` + // DT_UINT64 + Uint64Val []uint64 `protobuf:"varint,17,rep,packed,name=uint64_val,json=uint64Val,proto3" json:"uint64_val,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorProto) Reset() { *m = TensorProto{} } +func (m *TensorProto) String() string { return proto.CompactTextString(m) } +func (*TensorProto) ProtoMessage() {} +func (*TensorProto) Descriptor() ([]byte, []int) { + return fileDescriptor_efa68180bc31e4fc, []int{0} +} + +func (m *TensorProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorProto.Unmarshal(m, b) +} +func (m *TensorProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorProto.Marshal(b, m, deterministic) +} +func (m *TensorProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorProto.Merge(m, src) +} +func (m *TensorProto) XXX_Size() int { + return xxx_messageInfo_TensorProto.Size(m) +} +func (m *TensorProto) XXX_DiscardUnknown() { + xxx_messageInfo_TensorProto.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorProto proto.InternalMessageInfo + +func (m *TensorProto) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *TensorProto) GetTensorShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.TensorShape + } + return nil +} + +func (m *TensorProto) GetVersionNumber() int32 { + if m != nil { + return m.VersionNumber + } + return 0 +} + +func (m *TensorProto) GetTensorContent() []byte { + if m != nil { + return m.TensorContent + } + return nil +} + +func (m *TensorProto) GetHalfVal() []int32 { + if m != nil { + return m.HalfVal + } + return nil +} + +func (m *TensorProto) GetFloatVal() []float32 { + if m != nil { + return m.FloatVal + } + return nil +} + +func (m *TensorProto) GetDoubleVal() []float64 { + if m != nil { + return m.DoubleVal + } + return nil +} + +func (m *TensorProto) GetIntVal() []int32 { + if m != nil { + return m.IntVal + } + return nil +} + +func (m *TensorProto) GetStringVal() [][]byte { + if m != nil { + return m.StringVal + } + return nil +} + +func (m *TensorProto) GetScomplexVal() []float32 { + if m != nil { + return m.ScomplexVal + } + return nil +} + +func (m *TensorProto) GetInt64Val() []int64 { + if m != nil { + return m.Int64Val + } + return nil +} + +func (m *TensorProto) GetBoolVal() []bool { + if m != nil { + return m.BoolVal + } + return nil +} + +func (m *TensorProto) GetDcomplexVal() []float64 { + if m != nil { + return m.DcomplexVal + } + return nil +} + +func (m *TensorProto) GetResourceHandleVal() []*resource_handle_go_proto.ResourceHandleProto { + if m != nil { + return m.ResourceHandleVal + } + return nil +} + +func (m *TensorProto) GetVariantVal() []*VariantTensorDataProto { + if m != nil { + return m.VariantVal + } + return nil +} + +func (m *TensorProto) GetUint32Val() []uint32 { + if m != nil { + return m.Uint32Val + } + return nil +} + +func (m *TensorProto) GetUint64Val() []uint64 { + if m != nil { + return m.Uint64Val + } + return nil +} + +// Protocol buffer representing the serialization format of DT_VARIANT tensors. +type VariantTensorDataProto struct { + // Name of the type of objects being serialized. + TypeName string `protobuf:"bytes,1,opt,name=type_name,json=typeName,proto3" json:"type_name,omitempty"` + // Portions of the object that are not Tensors. + Metadata []byte `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` + // Tensors contained within objects being serialized. + Tensors []*TensorProto `protobuf:"bytes,3,rep,name=tensors,proto3" json:"tensors,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *VariantTensorDataProto) Reset() { *m = VariantTensorDataProto{} } +func (m *VariantTensorDataProto) String() string { return proto.CompactTextString(m) } +func (*VariantTensorDataProto) ProtoMessage() {} +func (*VariantTensorDataProto) Descriptor() ([]byte, []int) { + return fileDescriptor_efa68180bc31e4fc, []int{1} +} + +func (m *VariantTensorDataProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_VariantTensorDataProto.Unmarshal(m, b) +} +func (m *VariantTensorDataProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_VariantTensorDataProto.Marshal(b, m, deterministic) +} +func (m *VariantTensorDataProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_VariantTensorDataProto.Merge(m, src) +} +func (m *VariantTensorDataProto) XXX_Size() int { + return xxx_messageInfo_VariantTensorDataProto.Size(m) +} +func (m *VariantTensorDataProto) XXX_DiscardUnknown() { + xxx_messageInfo_VariantTensorDataProto.DiscardUnknown(m) +} + +var xxx_messageInfo_VariantTensorDataProto proto.InternalMessageInfo + +func (m *VariantTensorDataProto) GetTypeName() string { + if m != nil { + return m.TypeName + } + return "" +} + +func (m *VariantTensorDataProto) GetMetadata() []byte { + if m != nil { + return m.Metadata + } + return nil +} + +func (m *VariantTensorDataProto) GetTensors() []*TensorProto { + if m != nil { + return m.Tensors + } + return nil +} + +func init() { + proto.RegisterType((*TensorProto)(nil), "tensorflow.TensorProto") + proto.RegisterType((*VariantTensorDataProto)(nil), "tensorflow.VariantTensorDataProto") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/tensor.proto", fileDescriptor_efa68180bc31e4fc) +} + +var fileDescriptor_efa68180bc31e4fc = []byte{ + // 568 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x94, 0x4f, 0x6f, 0xd3, 0x30, + 0x18, 0xc6, 0x95, 0x7a, 0x6d, 0x13, 0x27, 0x2d, 0x5b, 0x40, 0x10, 0x75, 0x4c, 0x33, 0x95, 0x8a, + 0x2c, 0x84, 0x5a, 0xd1, 0x21, 0xae, 0x48, 0x1d, 0x07, 0x2e, 0x8c, 0x29, 0x4c, 0x3b, 0x70, 0x89, + 0xdc, 0xc6, 0x6d, 0x23, 0x12, 0xbb, 0x72, 0xdc, 0x8e, 0x49, 0x1c, 0xf9, 0x82, 0x7c, 0x1b, 0x8e, + 0xc8, 0xaf, 0xd3, 0x36, 0xc0, 0x06, 0xb7, 0xf4, 0x79, 0x7f, 0xef, 0xf3, 0xf8, 0xcf, 0xeb, 0xe2, + 0xe7, 0x9a, 0x8b, 0x52, 0xaa, 0x79, 0x2e, 0x6f, 0x46, 0x33, 0xa9, 0xf8, 0x68, 0xae, 0x58, 0xc1, + 0x6f, 0xa4, 0xfa, 0x32, 0xb2, 0x95, 0xe1, 0x4a, 0x49, 0x2d, 0x43, 0xbc, 0xe7, 0x7a, 0xa3, 0xfb, + 0x7b, 0x14, 0x2f, 0xe5, 0x5a, 0xcd, 0x78, 0xb2, 0x64, 0x22, 0xcd, 0xb9, 0x6d, 0xee, 0xbd, 0xfc, + 0x5f, 0x48, 0x52, 0x2e, 0xd9, 0x6a, 0x4b, 0x0f, 0xfe, 0x41, 0xdf, 0xae, 0x78, 0x69, 0xb1, 0xfe, + 0x8f, 0x26, 0xf6, 0xaf, 0x80, 0xbc, 0x84, 0x15, 0xbe, 0xc0, 0xcd, 0xd4, 0xd4, 0x23, 0x87, 0x38, + 0xb4, 0x3b, 0x7e, 0x34, 0xdc, 0xdb, 0x0c, 0xdf, 0x31, 0xcd, 0xae, 0x6e, 0x57, 0x3c, 0xb6, 0x48, + 0xf8, 0x16, 0x07, 0xf5, 0xe0, 0xa8, 0x41, 0x1c, 0xea, 0x8f, 0x9f, 0xd6, 0x5b, 0xac, 0xf5, 0x27, + 0x53, 0x06, 0xff, 0xd8, 0xd7, 0x7b, 0x25, 0x1c, 0xe0, 0xee, 0x86, 0xab, 0x32, 0x93, 0x22, 0x11, + 0xeb, 0x62, 0xca, 0x55, 0x84, 0x88, 0x43, 0x9b, 0x71, 0xa7, 0x52, 0x2f, 0x40, 0x34, 0x58, 0x95, + 0x33, 0x93, 0x42, 0x73, 0xa1, 0xa3, 0x03, 0xe2, 0xd0, 0x20, 0xee, 0x58, 0xf5, 0xdc, 0x8a, 0xe1, + 0x09, 0x76, 0x97, 0x2c, 0x9f, 0x27, 0x1b, 0x96, 0x47, 0x1d, 0x82, 0x68, 0x73, 0xd2, 0x38, 0x74, + 0xe2, 0xb6, 0xd1, 0xae, 0x59, 0x1e, 0x9e, 0x62, 0x6f, 0x9e, 0x4b, 0xa6, 0xa1, 0xde, 0x24, 0x88, + 0x36, 0xa0, 0xee, 0x82, 0x68, 0x80, 0x67, 0x18, 0xa7, 0x72, 0x3d, 0xcd, 0x39, 0x10, 0x2d, 0x82, + 0xa8, 0x03, 0x84, 0x67, 0x55, 0x83, 0x1c, 0xe3, 0x76, 0x26, 0xac, 0x43, 0x7b, 0x97, 0xd0, 0xca, + 0x04, 0xf4, 0x9f, 0x60, 0x5c, 0x6a, 0x95, 0x89, 0x05, 0xd4, 0x5d, 0x82, 0x68, 0x10, 0x7b, 0x56, + 0x31, 0xe5, 0x01, 0x0e, 0xca, 0x99, 0x2c, 0x56, 0x39, 0xff, 0x0a, 0x80, 0xb7, 0x5b, 0x82, 0xbf, + 0xd5, 0xab, 0x65, 0x66, 0x42, 0xbf, 0x79, 0x0d, 0x0c, 0x26, 0x88, 0x22, 0xbb, 0x4c, 0x10, 0x6d, + 0x8c, 0x3b, 0x95, 0x32, 0x87, 0xba, 0x4f, 0x10, 0x75, 0xed, 0x36, 0x8d, 0x56, 0xc5, 0xa4, 0xf5, + 0x98, 0x60, 0xb7, 0x0f, 0x3f, 0xad, 0xc5, 0x7c, 0xc4, 0x0f, 0xff, 0x98, 0x32, 0xa0, 0xbb, 0x04, + 0x51, 0x7f, 0x7c, 0x5a, 0xbf, 0xc2, 0xb8, 0xc2, 0xde, 0x03, 0x65, 0x6f, 0xf1, 0x48, 0xfd, 0x26, + 0x1a, 0xc3, 0x73, 0xec, 0x6f, 0x98, 0xca, 0x58, 0x75, 0x3c, 0x0f, 0xc0, 0xa8, 0x5f, 0x37, 0xba, + 0xb6, 0x65, 0x3b, 0x12, 0x66, 0x96, 0xac, 0x17, 0xae, 0xda, 0xaa, 0x2b, 0x58, 0x67, 0x42, 0x9f, + 0x8d, 0xc1, 0xe3, 0x90, 0x20, 0xda, 0xb1, 0x57, 0x60, 0xd5, 0x1a, 0x52, 0x1d, 0xd0, 0x11, 0x41, + 0xf4, 0x60, 0x8f, 0xc0, 0x09, 0xf5, 0xbf, 0x3b, 0xf8, 0xf1, 0xdd, 0x61, 0xe1, 0x31, 0xf6, 0xcc, + 0xe8, 0x26, 0x82, 0x15, 0x76, 0xc4, 0xbd, 0xd8, 0x35, 0xc2, 0x05, 0x2b, 0x78, 0xd8, 0xc3, 0x6e, + 0xc1, 0x35, 0x4b, 0x99, 0x66, 0x30, 0xcb, 0x41, 0xbc, 0xfb, 0x1d, 0xbe, 0xc2, 0x6d, 0xbb, 0x95, + 0x32, 0x42, 0xb0, 0xb5, 0x27, 0x7f, 0x8f, 0xb9, 0xdd, 0xcf, 0x96, 0x9b, 0x7c, 0xc3, 0x91, 0x54, + 0x8b, 0x3a, 0xb6, 0x7b, 0x82, 0x93, 0xa0, 0xd6, 0x51, 0x5e, 0x3a, 0x9f, 0x3f, 0x2c, 0x32, 0xbd, + 0x5c, 0x4f, 0x87, 0x33, 0x59, 0xd4, 0xff, 0x17, 0xee, 0xfe, 0x5c, 0xc8, 0x7b, 0xde, 0xff, 0x42, + 0x26, 0xf0, 0xaa, 0x7f, 0x3a, 0xce, 0xb4, 0x05, 0x5f, 0x67, 0xbf, 0x02, 0x00, 0x00, 0xff, 0xff, + 0x14, 0x4a, 0xc4, 0xd5, 0x9b, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/tensor_shape_go_proto/tensor_shape.pb.go b/tensorflow/go/core/framework/tensor_shape_go_proto/tensor_shape.pb.go new file mode 100644 index 0000000..a4836ee --- /dev/null +++ b/tensorflow/go/core/framework/tensor_shape_go_proto/tensor_shape.pb.go @@ -0,0 +1,167 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/tensor_shape.proto + +package tensor_shape_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Dimensions of a tensor. +type TensorShapeProto struct { + // Dimensions of the tensor, such as {"input", 30}, {"output", 40} + // for a 30 x 40 2D tensor. If an entry has size -1, this + // corresponds to a dimension of unknown size. The names are + // optional. + // + // The order of entries in "dim" matters: It indicates the layout of the + // values in the tensor in-memory representation. + // + // The first entry in "dim" is the outermost dimension used to layout the + // values, the last entry is the innermost dimension. This matches the + // in-memory layout of RowMajor Eigen tensors. + // + // If "dim.size()" > 0, "unknown_rank" must be false. + Dim []*TensorShapeProto_Dim `protobuf:"bytes,2,rep,name=dim,proto3" json:"dim,omitempty"` + // If true, the number of dimensions in the shape is unknown. + // + // If true, "dim.size()" must be 0. + UnknownRank bool `protobuf:"varint,3,opt,name=unknown_rank,json=unknownRank,proto3" json:"unknown_rank,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorShapeProto) Reset() { *m = TensorShapeProto{} } +func (m *TensorShapeProto) String() string { return proto.CompactTextString(m) } +func (*TensorShapeProto) ProtoMessage() {} +func (*TensorShapeProto) Descriptor() ([]byte, []int) { + return fileDescriptor_cd43873e75c1f7ac, []int{0} +} + +func (m *TensorShapeProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorShapeProto.Unmarshal(m, b) +} +func (m *TensorShapeProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorShapeProto.Marshal(b, m, deterministic) +} +func (m *TensorShapeProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorShapeProto.Merge(m, src) +} +func (m *TensorShapeProto) XXX_Size() int { + return xxx_messageInfo_TensorShapeProto.Size(m) +} +func (m *TensorShapeProto) XXX_DiscardUnknown() { + xxx_messageInfo_TensorShapeProto.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorShapeProto proto.InternalMessageInfo + +func (m *TensorShapeProto) GetDim() []*TensorShapeProto_Dim { + if m != nil { + return m.Dim + } + return nil +} + +func (m *TensorShapeProto) GetUnknownRank() bool { + if m != nil { + return m.UnknownRank + } + return false +} + +// One dimension of the tensor. +type TensorShapeProto_Dim struct { + // Size of the tensor in that dimension. + // This value must be >= -1, but values of -1 are reserved for "unknown" + // shapes (values of -1 mean "unknown" dimension). Certain wrappers + // that work with TensorShapeProto may fail at runtime when deserializing + // a TensorShapeProto containing a dim value of -1. + Size int64 `protobuf:"varint,1,opt,name=size,proto3" json:"size,omitempty"` + // Optional name of the tensor dimension. + Name string `protobuf:"bytes,2,opt,name=name,proto3" json:"name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorShapeProto_Dim) Reset() { *m = TensorShapeProto_Dim{} } +func (m *TensorShapeProto_Dim) String() string { return proto.CompactTextString(m) } +func (*TensorShapeProto_Dim) ProtoMessage() {} +func (*TensorShapeProto_Dim) Descriptor() ([]byte, []int) { + return fileDescriptor_cd43873e75c1f7ac, []int{0, 0} +} + +func (m *TensorShapeProto_Dim) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorShapeProto_Dim.Unmarshal(m, b) +} +func (m *TensorShapeProto_Dim) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorShapeProto_Dim.Marshal(b, m, deterministic) +} +func (m *TensorShapeProto_Dim) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorShapeProto_Dim.Merge(m, src) +} +func (m *TensorShapeProto_Dim) XXX_Size() int { + return xxx_messageInfo_TensorShapeProto_Dim.Size(m) +} +func (m *TensorShapeProto_Dim) XXX_DiscardUnknown() { + xxx_messageInfo_TensorShapeProto_Dim.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorShapeProto_Dim proto.InternalMessageInfo + +func (m *TensorShapeProto_Dim) GetSize() int64 { + if m != nil { + return m.Size + } + return 0 +} + +func (m *TensorShapeProto_Dim) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func init() { + proto.RegisterType((*TensorShapeProto)(nil), "tensorflow.TensorShapeProto") + proto.RegisterType((*TensorShapeProto_Dim)(nil), "tensorflow.TensorShapeProto.Dim") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/tensor_shape.proto", fileDescriptor_cd43873e75c1f7ac) +} + +var fileDescriptor_cd43873e75c1f7ac = []byte{ + // 235 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xe2, 0xd2, 0x29, 0x49, 0xcd, 0x2b, + 0xce, 0x2f, 0x4a, 0xcb, 0xc9, 0x2f, 0xd7, 0x4f, 0xce, 0x2f, 0x4a, 0xd5, 0x4f, 0x2b, 0x4a, 0xcc, + 0x4d, 0x2d, 0xcf, 0x2f, 0xca, 0xd6, 0x87, 0xc8, 0xc4, 0x17, 0x67, 0x24, 0x16, 0xa4, 0xea, 0x15, + 0x14, 0xe5, 0x97, 0xe4, 0x0b, 0x71, 0x21, 0x54, 0x2b, 0xcd, 0x60, 0xe4, 0x12, 0x08, 0x01, 0x73, + 0x83, 0x41, 0x2a, 0x02, 0xc0, 0x0a, 0x8c, 0xb8, 0x98, 0x53, 0x32, 0x73, 0x25, 0x98, 0x14, 0x98, + 0x35, 0xb8, 0x8d, 0x14, 0xf4, 0x10, 0xca, 0xf5, 0xd0, 0x95, 0xea, 0xb9, 0x64, 0xe6, 0x06, 0x81, + 0x14, 0x0b, 0x29, 0x72, 0xf1, 0x94, 0xe6, 0x65, 0xe7, 0xe5, 0x97, 0xe7, 0xc5, 0x17, 0x25, 0xe6, + 0x65, 0x4b, 0x30, 0x2b, 0x30, 0x6a, 0x70, 0x04, 0x71, 0x43, 0xc5, 0x82, 0x12, 0xf3, 0xb2, 0xa5, + 0x74, 0xb9, 0x98, 0x5d, 0x32, 0x73, 0x85, 0x84, 0xb8, 0x58, 0x8a, 0x33, 0xab, 0x52, 0x25, 0x18, + 0x15, 0x18, 0x35, 0x98, 0x83, 0xc0, 0x6c, 0x90, 0x58, 0x5e, 0x62, 0x6e, 0xaa, 0x04, 0x93, 0x02, + 0xa3, 0x06, 0x67, 0x10, 0x98, 0xed, 0xd4, 0xce, 0xc8, 0x25, 0x91, 0x5f, 0x94, 0x8e, 0x6c, 0x3d, + 0xdc, 0x5b, 0x4e, 0x82, 0xe8, 0x2e, 0x29, 0x0e, 0x60, 0x8c, 0x0a, 0x4e, 0xcf, 0x2c, 0xc9, 0x28, + 0x4d, 0xd2, 0x4b, 0xce, 0xcf, 0xd5, 0x47, 0x0a, 0x11, 0xec, 0xcc, 0xf4, 0x7c, 0x7c, 0x41, 0x15, + 0x9f, 0x9e, 0x1f, 0x0f, 0x0e, 0xad, 0x1f, 0x8c, 0x8c, 0x49, 0x6c, 0x60, 0x96, 0x31, 0x20, 0x00, + 0x00, 0xff, 0xff, 0xcb, 0x74, 0x65, 0x90, 0x67, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/tensor_slice_go_proto/tensor_slice.pb.go b/tensorflow/go/core/framework/tensor_slice_go_proto/tensor_slice.pb.go new file mode 100644 index 0000000..f41877c --- /dev/null +++ b/tensorflow/go/core/framework/tensor_slice_go_proto/tensor_slice.pb.go @@ -0,0 +1,174 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/tensor_slice.proto + +package tensor_slice_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Can only be interpreted if you know the corresponding TensorShape. +type TensorSliceProto struct { + // Extent of the slice in all tensor dimensions. + // + // Must have one entry for each of the dimension of the tensor that this + // slice belongs to. The order of sizes is the same as the order of + // dimensions in the TensorShape. + Extent []*TensorSliceProto_Extent `protobuf:"bytes,1,rep,name=extent,proto3" json:"extent,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorSliceProto) Reset() { *m = TensorSliceProto{} } +func (m *TensorSliceProto) String() string { return proto.CompactTextString(m) } +func (*TensorSliceProto) ProtoMessage() {} +func (*TensorSliceProto) Descriptor() ([]byte, []int) { + return fileDescriptor_efadfca37d8372d8, []int{0} +} + +func (m *TensorSliceProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorSliceProto.Unmarshal(m, b) +} +func (m *TensorSliceProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorSliceProto.Marshal(b, m, deterministic) +} +func (m *TensorSliceProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorSliceProto.Merge(m, src) +} +func (m *TensorSliceProto) XXX_Size() int { + return xxx_messageInfo_TensorSliceProto.Size(m) +} +func (m *TensorSliceProto) XXX_DiscardUnknown() { + xxx_messageInfo_TensorSliceProto.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorSliceProto proto.InternalMessageInfo + +func (m *TensorSliceProto) GetExtent() []*TensorSliceProto_Extent { + if m != nil { + return m.Extent + } + return nil +} + +// Extent of the slice in one dimension. +type TensorSliceProto_Extent struct { + // Start index of the slice, starting at 0. + Start int64 `protobuf:"varint,1,opt,name=start,proto3" json:"start,omitempty"` + // Length of the slice: if the length is missing or -1 we will + // interpret this as "everything in this dimension". We use + // "oneof" to preserve information about whether the length is + // present without changing the serialization format from the + // prior proto2 version of this proto. + // + // Types that are valid to be assigned to HasLength: + // *TensorSliceProto_Extent_Length + HasLength isTensorSliceProto_Extent_HasLength `protobuf_oneof:"has_length"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorSliceProto_Extent) Reset() { *m = TensorSliceProto_Extent{} } +func (m *TensorSliceProto_Extent) String() string { return proto.CompactTextString(m) } +func (*TensorSliceProto_Extent) ProtoMessage() {} +func (*TensorSliceProto_Extent) Descriptor() ([]byte, []int) { + return fileDescriptor_efadfca37d8372d8, []int{0, 0} +} + +func (m *TensorSliceProto_Extent) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorSliceProto_Extent.Unmarshal(m, b) +} +func (m *TensorSliceProto_Extent) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorSliceProto_Extent.Marshal(b, m, deterministic) +} +func (m *TensorSliceProto_Extent) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorSliceProto_Extent.Merge(m, src) +} +func (m *TensorSliceProto_Extent) XXX_Size() int { + return xxx_messageInfo_TensorSliceProto_Extent.Size(m) +} +func (m *TensorSliceProto_Extent) XXX_DiscardUnknown() { + xxx_messageInfo_TensorSliceProto_Extent.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorSliceProto_Extent proto.InternalMessageInfo + +func (m *TensorSliceProto_Extent) GetStart() int64 { + if m != nil { + return m.Start + } + return 0 +} + +type isTensorSliceProto_Extent_HasLength interface { + isTensorSliceProto_Extent_HasLength() +} + +type TensorSliceProto_Extent_Length struct { + Length int64 `protobuf:"varint,2,opt,name=length,proto3,oneof"` +} + +func (*TensorSliceProto_Extent_Length) isTensorSliceProto_Extent_HasLength() {} + +func (m *TensorSliceProto_Extent) GetHasLength() isTensorSliceProto_Extent_HasLength { + if m != nil { + return m.HasLength + } + return nil +} + +func (m *TensorSliceProto_Extent) GetLength() int64 { + if x, ok := m.GetHasLength().(*TensorSliceProto_Extent_Length); ok { + return x.Length + } + return 0 +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*TensorSliceProto_Extent) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*TensorSliceProto_Extent_Length)(nil), + } +} + +func init() { + proto.RegisterType((*TensorSliceProto)(nil), "tensorflow.TensorSliceProto") + proto.RegisterType((*TensorSliceProto_Extent)(nil), "tensorflow.TensorSliceProto.Extent") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/tensor_slice.proto", fileDescriptor_efadfca37d8372d8) +} + +var fileDescriptor_efadfca37d8372d8 = []byte{ + // 219 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xe2, 0xd2, 0x29, 0x49, 0xcd, 0x2b, + 0xce, 0x2f, 0x4a, 0xcb, 0xc9, 0x2f, 0xd7, 0x4f, 0xce, 0x2f, 0x4a, 0xd5, 0x4f, 0x2b, 0x4a, 0xcc, + 0x4d, 0x2d, 0xcf, 0x2f, 0xca, 0xd6, 0x87, 0xc8, 0xc4, 0x17, 0xe7, 0x64, 0x26, 0xa7, 0xea, 0x15, + 0x14, 0xe5, 0x97, 0xe4, 0x0b, 0x71, 0x21, 0x54, 0x2b, 0x4d, 0x67, 0xe4, 0x12, 0x08, 0x01, 0x73, + 0x83, 0x41, 0x2a, 0x02, 0xc0, 0x0a, 0xac, 0xb9, 0xd8, 0x52, 0x2b, 0x4a, 0x52, 0xf3, 0x4a, 0x24, + 0x18, 0x15, 0x98, 0x35, 0xb8, 0x8d, 0x94, 0xf5, 0x10, 0x3a, 0xf4, 0xd0, 0x55, 0xeb, 0xb9, 0x82, + 0x95, 0x06, 0x41, 0xb5, 0x48, 0xb9, 0x71, 0xb1, 0x41, 0x44, 0x84, 0x44, 0xb8, 0x58, 0x8b, 0x4b, + 0x12, 0x8b, 0x40, 0xa6, 0x30, 0x6a, 0x30, 0x07, 0x41, 0x38, 0x42, 0x12, 0x5c, 0x6c, 0x39, 0xa9, + 0x79, 0xe9, 0x25, 0x19, 0x12, 0x4c, 0x20, 0x61, 0x0f, 0x86, 0x20, 0x28, 0xdf, 0x89, 0x87, 0x8b, + 0x2b, 0x23, 0xb1, 0x38, 0x1e, 0xca, 0x6b, 0x67, 0xe4, 0x92, 0xc8, 0x2f, 0x4a, 0x47, 0xb6, 0x1a, + 0xee, 0x2b, 0x27, 0x41, 0x74, 0x57, 0x14, 0x07, 0x30, 0x46, 0x05, 0xa7, 0x67, 0x96, 0x64, 0x94, + 0x26, 0xe9, 0x25, 0xe7, 0xe7, 0xea, 0x23, 0x05, 0x08, 0x76, 0x66, 0x7a, 0x3e, 0xbe, 0x90, 0x8a, + 0x4f, 0xcf, 0x8f, 0x07, 0x07, 0xd6, 0x0f, 0x46, 0xc6, 0x24, 0x36, 0x30, 0xcb, 0x18, 0x10, 0x00, + 0x00, 0xff, 0xff, 0xf7, 0xcd, 0x0a, 0x00, 0x66, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/types_go_proto/types.pb.go b/tensorflow/go/core/framework/types_go_proto/types.pb.go new file mode 100644 index 0000000..55aa2da --- /dev/null +++ b/tensorflow/go/core/framework/types_go_proto/types.pb.go @@ -0,0 +1,266 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/types.proto + +package types_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// (== suppress_warning documentation-presence ==) +// LINT.IfChange +type DataType int32 + +const ( + // Not a legal value for DataType. Used to indicate a DataType field + // has not been set. + DataType_DT_INVALID DataType = 0 + // Data types that all computation devices are expected to be + // capable to support. + DataType_DT_FLOAT DataType = 1 + DataType_DT_DOUBLE DataType = 2 + DataType_DT_INT32 DataType = 3 + DataType_DT_UINT8 DataType = 4 + DataType_DT_INT16 DataType = 5 + DataType_DT_INT8 DataType = 6 + DataType_DT_STRING DataType = 7 + DataType_DT_COMPLEX64 DataType = 8 + DataType_DT_INT64 DataType = 9 + DataType_DT_BOOL DataType = 10 + DataType_DT_QINT8 DataType = 11 + DataType_DT_QUINT8 DataType = 12 + DataType_DT_QINT32 DataType = 13 + DataType_DT_BFLOAT16 DataType = 14 + DataType_DT_QINT16 DataType = 15 + DataType_DT_QUINT16 DataType = 16 + DataType_DT_UINT16 DataType = 17 + DataType_DT_COMPLEX128 DataType = 18 + DataType_DT_HALF DataType = 19 + DataType_DT_RESOURCE DataType = 20 + DataType_DT_VARIANT DataType = 21 + DataType_DT_UINT32 DataType = 22 + DataType_DT_UINT64 DataType = 23 + // Do not use! These are only for parameters. Every enum above + // should have a corresponding value below (verified by types_test). + DataType_DT_FLOAT_REF DataType = 101 + DataType_DT_DOUBLE_REF DataType = 102 + DataType_DT_INT32_REF DataType = 103 + DataType_DT_UINT8_REF DataType = 104 + DataType_DT_INT16_REF DataType = 105 + DataType_DT_INT8_REF DataType = 106 + DataType_DT_STRING_REF DataType = 107 + DataType_DT_COMPLEX64_REF DataType = 108 + DataType_DT_INT64_REF DataType = 109 + DataType_DT_BOOL_REF DataType = 110 + DataType_DT_QINT8_REF DataType = 111 + DataType_DT_QUINT8_REF DataType = 112 + DataType_DT_QINT32_REF DataType = 113 + DataType_DT_BFLOAT16_REF DataType = 114 + DataType_DT_QINT16_REF DataType = 115 + DataType_DT_QUINT16_REF DataType = 116 + DataType_DT_UINT16_REF DataType = 117 + DataType_DT_COMPLEX128_REF DataType = 118 + DataType_DT_HALF_REF DataType = 119 + DataType_DT_RESOURCE_REF DataType = 120 + DataType_DT_VARIANT_REF DataType = 121 + DataType_DT_UINT32_REF DataType = 122 + DataType_DT_UINT64_REF DataType = 123 +) + +var DataType_name = map[int32]string{ + 0: "DT_INVALID", + 1: "DT_FLOAT", + 2: "DT_DOUBLE", + 3: "DT_INT32", + 4: "DT_UINT8", + 5: "DT_INT16", + 6: "DT_INT8", + 7: "DT_STRING", + 8: "DT_COMPLEX64", + 9: "DT_INT64", + 10: "DT_BOOL", + 11: "DT_QINT8", + 12: "DT_QUINT8", + 13: "DT_QINT32", + 14: "DT_BFLOAT16", + 15: "DT_QINT16", + 16: "DT_QUINT16", + 17: "DT_UINT16", + 18: "DT_COMPLEX128", + 19: "DT_HALF", + 20: "DT_RESOURCE", + 21: "DT_VARIANT", + 22: "DT_UINT32", + 23: "DT_UINT64", + 101: "DT_FLOAT_REF", + 102: "DT_DOUBLE_REF", + 103: "DT_INT32_REF", + 104: "DT_UINT8_REF", + 105: "DT_INT16_REF", + 106: "DT_INT8_REF", + 107: "DT_STRING_REF", + 108: "DT_COMPLEX64_REF", + 109: "DT_INT64_REF", + 110: "DT_BOOL_REF", + 111: "DT_QINT8_REF", + 112: "DT_QUINT8_REF", + 113: "DT_QINT32_REF", + 114: "DT_BFLOAT16_REF", + 115: "DT_QINT16_REF", + 116: "DT_QUINT16_REF", + 117: "DT_UINT16_REF", + 118: "DT_COMPLEX128_REF", + 119: "DT_HALF_REF", + 120: "DT_RESOURCE_REF", + 121: "DT_VARIANT_REF", + 122: "DT_UINT32_REF", + 123: "DT_UINT64_REF", +} + +var DataType_value = map[string]int32{ + "DT_INVALID": 0, + "DT_FLOAT": 1, + "DT_DOUBLE": 2, + "DT_INT32": 3, + "DT_UINT8": 4, + "DT_INT16": 5, + "DT_INT8": 6, + "DT_STRING": 7, + "DT_COMPLEX64": 8, + "DT_INT64": 9, + "DT_BOOL": 10, + "DT_QINT8": 11, + "DT_QUINT8": 12, + "DT_QINT32": 13, + "DT_BFLOAT16": 14, + "DT_QINT16": 15, + "DT_QUINT16": 16, + "DT_UINT16": 17, + "DT_COMPLEX128": 18, + "DT_HALF": 19, + "DT_RESOURCE": 20, + "DT_VARIANT": 21, + "DT_UINT32": 22, + "DT_UINT64": 23, + "DT_FLOAT_REF": 101, + "DT_DOUBLE_REF": 102, + "DT_INT32_REF": 103, + "DT_UINT8_REF": 104, + "DT_INT16_REF": 105, + "DT_INT8_REF": 106, + "DT_STRING_REF": 107, + "DT_COMPLEX64_REF": 108, + "DT_INT64_REF": 109, + "DT_BOOL_REF": 110, + "DT_QINT8_REF": 111, + "DT_QUINT8_REF": 112, + "DT_QINT32_REF": 113, + "DT_BFLOAT16_REF": 114, + "DT_QINT16_REF": 115, + "DT_QUINT16_REF": 116, + "DT_UINT16_REF": 117, + "DT_COMPLEX128_REF": 118, + "DT_HALF_REF": 119, + "DT_RESOURCE_REF": 120, + "DT_VARIANT_REF": 121, + "DT_UINT32_REF": 122, + "DT_UINT64_REF": 123, +} + +func (x DataType) String() string { + return proto.EnumName(DataType_name, int32(x)) +} + +func (DataType) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_472a3bf6d9d70fb7, []int{0} +} + +// For identifying the underlying type of a variant. For variants, the types +// listed here are a subset of the types in the variant type registry, +// corresponding to commonly used variants which must occasionally be +// special-cased. +type SpecializedType int32 + +const ( + // Invalid/unknown specialized type. + SpecializedType_ST_INVALID SpecializedType = 0 + // "tensorflow::TensorList" in the variant type registry. + SpecializedType_ST_TENSOR_LIST SpecializedType = 1 +) + +var SpecializedType_name = map[int32]string{ + 0: "ST_INVALID", + 1: "ST_TENSOR_LIST", +} + +var SpecializedType_value = map[string]int32{ + "ST_INVALID": 0, + "ST_TENSOR_LIST": 1, +} + +func (x SpecializedType) String() string { + return proto.EnumName(SpecializedType_name, int32(x)) +} + +func (SpecializedType) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_472a3bf6d9d70fb7, []int{1} +} + +func init() { + proto.RegisterEnum("tensorflow.DataType", DataType_name, DataType_value) + proto.RegisterEnum("tensorflow.SpecializedType", SpecializedType_name, SpecializedType_value) +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/types.proto", fileDescriptor_472a3bf6d9d70fb7) +} + +var fileDescriptor_472a3bf6d9d70fb7 = []byte{ + // 519 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x6c, 0x93, 0x4f, 0x6f, 0xda, 0x4c, + 0x10, 0xc6, 0x5f, 0xde, 0xb6, 0x84, 0x2c, 0xff, 0x86, 0x4d, 0xd2, 0xf6, 0x0b, 0xf4, 0x92, 0x03, + 0xc8, 0x40, 0x2d, 0xae, 0x10, 0x9b, 0xd6, 0x92, 0x6b, 0x83, 0xbd, 0x44, 0x55, 0x2f, 0x16, 0xa1, + 0x86, 0xd0, 0x40, 0x96, 0x1a, 0xa7, 0x14, 0xfa, 0x8d, 0xfa, 0x09, 0x7b, 0xac, 0x18, 0x8f, 0xd9, + 0xad, 0x94, 0x9b, 0xe7, 0x37, 0xb3, 0xcf, 0x3c, 0x9e, 0xd1, 0xb0, 0x77, 0x69, 0xfc, 0xb8, 0x95, + 0xc9, 0x7c, 0x25, 0x77, 0xad, 0x99, 0x4c, 0xe2, 0xd6, 0x3c, 0x99, 0xae, 0xe3, 0x9d, 0x4c, 0x1e, + 0x5a, 0xe9, 0x7e, 0x13, 0x6f, 0x9b, 0x9b, 0x44, 0xa6, 0x92, 0x33, 0x55, 0x76, 0xfd, 0xbb, 0xc8, + 0x4a, 0xd6, 0x34, 0x9d, 0x8a, 0xfd, 0x26, 0xe6, 0x35, 0xc6, 0x2c, 0x11, 0x39, 0xde, 0x6d, 0xdf, + 0x75, 0x2c, 0xf8, 0x8f, 0x57, 0x58, 0xc9, 0x12, 0xd1, 0xd0, 0xf5, 0xfb, 0x02, 0x0a, 0xbc, 0xca, + 0xce, 0x2d, 0x11, 0x59, 0xfe, 0x64, 0xe0, 0xda, 0xf0, 0x3f, 0x25, 0x1d, 0x4f, 0x74, 0xda, 0xf0, + 0x82, 0xa2, 0x89, 0xe3, 0x89, 0x1e, 0xbc, 0x54, 0x39, 0xc3, 0x84, 0x57, 0xbc, 0xcc, 0xce, 0xb2, + 0xa8, 0x07, 0x45, 0x52, 0x09, 0x45, 0xe0, 0x78, 0x1f, 0xe0, 0x8c, 0x03, 0xab, 0x58, 0x22, 0xba, + 0xf1, 0x3f, 0x8d, 0x5c, 0xfb, 0xb3, 0xd9, 0x85, 0x92, 0x7a, 0x6b, 0x76, 0xe1, 0x9c, 0xde, 0x0e, + 0x7c, 0xdf, 0x05, 0x46, 0xa9, 0x31, 0x2a, 0x95, 0x49, 0x69, 0x9c, 0xf5, 0xac, 0xe4, 0x61, 0x66, + 0xa8, 0xca, 0xeb, 0xac, 0x7c, 0x7c, 0x88, 0xe6, 0x0d, 0x13, 0x6a, 0x5a, 0xde, 0x30, 0xa1, 0x4e, + 0xff, 0x8a, 0xaf, 0x0d, 0x13, 0x80, 0xd2, 0x14, 0x36, 0x78, 0x83, 0x55, 0x95, 0x2f, 0xa3, 0xdd, + 0x03, 0x4e, 0x56, 0x3e, 0xf6, 0xdd, 0x21, 0x5c, 0x90, 0x7c, 0x60, 0x87, 0xfe, 0x24, 0xb8, 0xb1, + 0xe1, 0x92, 0xf4, 0x6e, 0xfb, 0x81, 0xd3, 0xf7, 0x04, 0x5c, 0x69, 0x7a, 0x9d, 0x36, 0xbc, 0xd6, + 0x42, 0xb3, 0x0b, 0x6f, 0xe8, 0xb7, 0xd1, 0x5c, 0x14, 0xd8, 0x43, 0x88, 0xa9, 0x61, 0x36, 0x5d, + 0x44, 0x73, 0x2a, 0x42, 0x05, 0x24, 0x0b, 0x22, 0xf8, 0xc7, 0x48, 0xee, 0x55, 0x8d, 0x61, 0x22, + 0x59, 0x92, 0xb3, 0x53, 0xc9, 0x37, 0x52, 0xce, 0x26, 0x8e, 0xe8, 0x81, 0x5f, 0x32, 0xd0, 0xa7, + 0x8e, 0x74, 0xa5, 0xb4, 0x88, 0xac, 0xf3, 0x21, 0xfa, 0xbe, 0x8b, 0xe0, 0x91, 0x4a, 0xc6, 0x27, + 0x75, 0x49, 0xea, 0x63, 0xe5, 0x69, 0x93, 0x23, 0x65, 0xfc, 0x3b, 0xbf, 0x60, 0x75, 0x6d, 0x1b, + 0x08, 0x13, 0xad, 0x8e, 0xd0, 0x96, 0x73, 0x56, 0x53, 0x5b, 0x41, 0x96, 0x52, 0x99, 0x86, 0x9e, + 0xf8, 0x15, 0x6b, 0xfc, 0xb3, 0x1d, 0xc4, 0x3f, 0xc8, 0xee, 0x71, 0x43, 0x08, 0x76, 0xd4, 0x36, + 0xdf, 0x12, 0xc2, 0x9f, 0xd4, 0x83, 0x36, 0x85, 0x6c, 0xaf, 0xf5, 0x20, 0xcb, 0x07, 0x0d, 0xd1, + 0x38, 0x7e, 0x5d, 0xbf, 0x67, 0xf5, 0x70, 0x13, 0xcf, 0x96, 0xd3, 0xd5, 0xf2, 0x10, 0x7f, 0xcd, + 0x4f, 0x26, 0xd4, 0x4f, 0x86, 0xb3, 0x5a, 0x28, 0x22, 0x61, 0x7b, 0xa1, 0x1f, 0x44, 0xae, 0x13, + 0x0a, 0x28, 0x0c, 0x0e, 0xec, 0xad, 0x4c, 0x16, 0x4d, 0x75, 0x75, 0xcd, 0xd3, 0x5d, 0x0e, 0xca, + 0x47, 0x95, 0xed, 0xe8, 0x78, 0x97, 0xdb, 0x51, 0xe1, 0x8b, 0xbb, 0x58, 0xa6, 0xf7, 0x4f, 0x77, + 0xcd, 0x99, 0x5c, 0xb7, 0xb4, 0x63, 0x7e, 0xfe, 0x73, 0x21, 0x9f, 0xbd, 0xf2, 0x68, 0x21, 0x23, + 0x3c, 0xf4, 0x3f, 0x85, 0xc2, 0x5d, 0x11, 0xbf, 0x3a, 0x7f, 0x03, 0x00, 0x00, 0xff, 0xff, 0xef, + 0x39, 0x4b, 0x74, 0x1b, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/variable_go_proto/variable.pb.go b/tensorflow/go/core/framework/variable_go_proto/variable.pb.go new file mode 100644 index 0000000..3538466 --- /dev/null +++ b/tensorflow/go/core/framework/variable_go_proto/variable.pb.go @@ -0,0 +1,334 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/variable.proto + +package variable_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Indicates when a distributed variable will be synced. +type VariableSynchronization int32 + +const ( + // `AUTO`: Indicates that the synchronization will be determined by the + // current `DistributionStrategy` (eg. With `MirroredStrategy` this would be + // `ON_WRITE`). + VariableSynchronization_VARIABLE_SYNCHRONIZATION_AUTO VariableSynchronization = 0 + // `NONE`: Indicates that there will only be one copy of the variable, so + // there is no need to sync. + VariableSynchronization_VARIABLE_SYNCHRONIZATION_NONE VariableSynchronization = 1 + // `ON_WRITE`: Indicates that the variable will be updated across devices + // every time it is written. + VariableSynchronization_VARIABLE_SYNCHRONIZATION_ON_WRITE VariableSynchronization = 2 + // `ON_READ`: Indicates that the variable will be aggregated across devices + // when it is read (eg. when checkpointing or when evaluating an op that uses + // the variable). + VariableSynchronization_VARIABLE_SYNCHRONIZATION_ON_READ VariableSynchronization = 3 +) + +var VariableSynchronization_name = map[int32]string{ + 0: "VARIABLE_SYNCHRONIZATION_AUTO", + 1: "VARIABLE_SYNCHRONIZATION_NONE", + 2: "VARIABLE_SYNCHRONIZATION_ON_WRITE", + 3: "VARIABLE_SYNCHRONIZATION_ON_READ", +} + +var VariableSynchronization_value = map[string]int32{ + "VARIABLE_SYNCHRONIZATION_AUTO": 0, + "VARIABLE_SYNCHRONIZATION_NONE": 1, + "VARIABLE_SYNCHRONIZATION_ON_WRITE": 2, + "VARIABLE_SYNCHRONIZATION_ON_READ": 3, +} + +func (x VariableSynchronization) String() string { + return proto.EnumName(VariableSynchronization_name, int32(x)) +} + +func (VariableSynchronization) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_908f2d03adae2778, []int{0} +} + +// Indicates how a distributed variable will be aggregated. +type VariableAggregation int32 + +const ( + // `NONE`: This is the default, giving an error if you use a + // variable-update operation with multiple replicas. + VariableAggregation_VARIABLE_AGGREGATION_NONE VariableAggregation = 0 + // `SUM`: Add the updates across replicas. + VariableAggregation_VARIABLE_AGGREGATION_SUM VariableAggregation = 1 + // `MEAN`: Take the arithmetic mean ("average") of the updates across + // replicas. + VariableAggregation_VARIABLE_AGGREGATION_MEAN VariableAggregation = 2 + // `ONLY_FIRST_REPLICA`: This is for when every replica is performing the same + // update, but we only want to perform the update once. Used, e.g., for the + // global step counter. + VariableAggregation_VARIABLE_AGGREGATION_ONLY_FIRST_REPLICA VariableAggregation = 3 +) + +var VariableAggregation_name = map[int32]string{ + 0: "VARIABLE_AGGREGATION_NONE", + 1: "VARIABLE_AGGREGATION_SUM", + 2: "VARIABLE_AGGREGATION_MEAN", + 3: "VARIABLE_AGGREGATION_ONLY_FIRST_REPLICA", +} + +var VariableAggregation_value = map[string]int32{ + "VARIABLE_AGGREGATION_NONE": 0, + "VARIABLE_AGGREGATION_SUM": 1, + "VARIABLE_AGGREGATION_MEAN": 2, + "VARIABLE_AGGREGATION_ONLY_FIRST_REPLICA": 3, +} + +func (x VariableAggregation) String() string { + return proto.EnumName(VariableAggregation_name, int32(x)) +} + +func (VariableAggregation) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_908f2d03adae2778, []int{1} +} + +// Protocol buffer representing a Variable. +type VariableDef struct { + // Name of the variable tensor. + VariableName string `protobuf:"bytes,1,opt,name=variable_name,json=variableName,proto3" json:"variable_name,omitempty"` + // Name of the tensor holding the variable's initial value. + InitialValueName string `protobuf:"bytes,6,opt,name=initial_value_name,json=initialValueName,proto3" json:"initial_value_name,omitempty"` + // Name of the initializer op. + InitializerName string `protobuf:"bytes,2,opt,name=initializer_name,json=initializerName,proto3" json:"initializer_name,omitempty"` + // Name of the snapshot tensor. + SnapshotName string `protobuf:"bytes,3,opt,name=snapshot_name,json=snapshotName,proto3" json:"snapshot_name,omitempty"` + // Support for saving variables as slices of a larger variable. + SaveSliceInfoDef *SaveSliceInfoDef `protobuf:"bytes,4,opt,name=save_slice_info_def,json=saveSliceInfoDef,proto3" json:"save_slice_info_def,omitempty"` + // Whether to represent this as a ResourceVariable. + IsResource bool `protobuf:"varint,5,opt,name=is_resource,json=isResource,proto3" json:"is_resource,omitempty"` + // Whether this variable should be trained. + Trainable bool `protobuf:"varint,7,opt,name=trainable,proto3" json:"trainable,omitempty"` + // Indicates when a distributed variable will be synced. + Synchronization VariableSynchronization `protobuf:"varint,8,opt,name=synchronization,proto3,enum=tensorflow.VariableSynchronization" json:"synchronization,omitempty"` + // Indicates how a distributed variable will be aggregated. + Aggregation VariableAggregation `protobuf:"varint,9,opt,name=aggregation,proto3,enum=tensorflow.VariableAggregation" json:"aggregation,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *VariableDef) Reset() { *m = VariableDef{} } +func (m *VariableDef) String() string { return proto.CompactTextString(m) } +func (*VariableDef) ProtoMessage() {} +func (*VariableDef) Descriptor() ([]byte, []int) { + return fileDescriptor_908f2d03adae2778, []int{0} +} + +func (m *VariableDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_VariableDef.Unmarshal(m, b) +} +func (m *VariableDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_VariableDef.Marshal(b, m, deterministic) +} +func (m *VariableDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_VariableDef.Merge(m, src) +} +func (m *VariableDef) XXX_Size() int { + return xxx_messageInfo_VariableDef.Size(m) +} +func (m *VariableDef) XXX_DiscardUnknown() { + xxx_messageInfo_VariableDef.DiscardUnknown(m) +} + +var xxx_messageInfo_VariableDef proto.InternalMessageInfo + +func (m *VariableDef) GetVariableName() string { + if m != nil { + return m.VariableName + } + return "" +} + +func (m *VariableDef) GetInitialValueName() string { + if m != nil { + return m.InitialValueName + } + return "" +} + +func (m *VariableDef) GetInitializerName() string { + if m != nil { + return m.InitializerName + } + return "" +} + +func (m *VariableDef) GetSnapshotName() string { + if m != nil { + return m.SnapshotName + } + return "" +} + +func (m *VariableDef) GetSaveSliceInfoDef() *SaveSliceInfoDef { + if m != nil { + return m.SaveSliceInfoDef + } + return nil +} + +func (m *VariableDef) GetIsResource() bool { + if m != nil { + return m.IsResource + } + return false +} + +func (m *VariableDef) GetTrainable() bool { + if m != nil { + return m.Trainable + } + return false +} + +func (m *VariableDef) GetSynchronization() VariableSynchronization { + if m != nil { + return m.Synchronization + } + return VariableSynchronization_VARIABLE_SYNCHRONIZATION_AUTO +} + +func (m *VariableDef) GetAggregation() VariableAggregation { + if m != nil { + return m.Aggregation + } + return VariableAggregation_VARIABLE_AGGREGATION_NONE +} + +type SaveSliceInfoDef struct { + // Name of the full variable of which this is a slice. + FullName string `protobuf:"bytes,1,opt,name=full_name,json=fullName,proto3" json:"full_name,omitempty"` + // Shape of the full variable. + FullShape []int64 `protobuf:"varint,2,rep,packed,name=full_shape,json=fullShape,proto3" json:"full_shape,omitempty"` + // Offset of this variable into the full variable. + VarOffset []int64 `protobuf:"varint,3,rep,packed,name=var_offset,json=varOffset,proto3" json:"var_offset,omitempty"` + // Shape of this variable. + VarShape []int64 `protobuf:"varint,4,rep,packed,name=var_shape,json=varShape,proto3" json:"var_shape,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SaveSliceInfoDef) Reset() { *m = SaveSliceInfoDef{} } +func (m *SaveSliceInfoDef) String() string { return proto.CompactTextString(m) } +func (*SaveSliceInfoDef) ProtoMessage() {} +func (*SaveSliceInfoDef) Descriptor() ([]byte, []int) { + return fileDescriptor_908f2d03adae2778, []int{1} +} + +func (m *SaveSliceInfoDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SaveSliceInfoDef.Unmarshal(m, b) +} +func (m *SaveSliceInfoDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SaveSliceInfoDef.Marshal(b, m, deterministic) +} +func (m *SaveSliceInfoDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_SaveSliceInfoDef.Merge(m, src) +} +func (m *SaveSliceInfoDef) XXX_Size() int { + return xxx_messageInfo_SaveSliceInfoDef.Size(m) +} +func (m *SaveSliceInfoDef) XXX_DiscardUnknown() { + xxx_messageInfo_SaveSliceInfoDef.DiscardUnknown(m) +} + +var xxx_messageInfo_SaveSliceInfoDef proto.InternalMessageInfo + +func (m *SaveSliceInfoDef) GetFullName() string { + if m != nil { + return m.FullName + } + return "" +} + +func (m *SaveSliceInfoDef) GetFullShape() []int64 { + if m != nil { + return m.FullShape + } + return nil +} + +func (m *SaveSliceInfoDef) GetVarOffset() []int64 { + if m != nil { + return m.VarOffset + } + return nil +} + +func (m *SaveSliceInfoDef) GetVarShape() []int64 { + if m != nil { + return m.VarShape + } + return nil +} + +func init() { + proto.RegisterEnum("tensorflow.VariableSynchronization", VariableSynchronization_name, VariableSynchronization_value) + proto.RegisterEnum("tensorflow.VariableAggregation", VariableAggregation_name, VariableAggregation_value) + proto.RegisterType((*VariableDef)(nil), "tensorflow.VariableDef") + proto.RegisterType((*SaveSliceInfoDef)(nil), "tensorflow.SaveSliceInfoDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/variable.proto", fileDescriptor_908f2d03adae2778) +} + +var fileDescriptor_908f2d03adae2778 = []byte{ + // 572 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x94, 0xd1, 0x4e, 0xdb, 0x30, + 0x14, 0x86, 0x31, 0x61, 0xac, 0x3d, 0x1d, 0x10, 0x99, 0x8b, 0x65, 0x1a, 0x88, 0x02, 0x9b, 0xd6, + 0xb1, 0xa9, 0x95, 0xd8, 0x13, 0x04, 0xc8, 0x58, 0x34, 0x48, 0x90, 0x53, 0x98, 0xe0, 0xc6, 0x32, + 0x9d, 0x93, 0x5a, 0x4b, 0x63, 0x64, 0xa7, 0x41, 0xe3, 0x6a, 0xd7, 0x7b, 0x88, 0x3d, 0xc1, 0x9e, + 0x65, 0xcf, 0xb3, 0xcb, 0x29, 0x6e, 0x4b, 0x33, 0xd4, 0x72, 0x67, 0xfd, 0xff, 0xf7, 0x9f, 0xf8, + 0xd8, 0xf1, 0x81, 0x56, 0xce, 0x33, 0x2d, 0x55, 0x9c, 0xca, 0xdb, 0x4e, 0x4f, 0x2a, 0xde, 0x89, + 0x15, 0x1b, 0xf0, 0x5b, 0xa9, 0xbe, 0x75, 0x0a, 0xa6, 0x04, 0xbb, 0x4e, 0x79, 0xfb, 0x46, 0xc9, + 0x5c, 0x62, 0x98, 0x92, 0x3b, 0x7f, 0x2c, 0x68, 0x5c, 0x8c, 0xed, 0x23, 0x1e, 0xe3, 0x5d, 0x58, + 0x99, 0xd0, 0x34, 0x63, 0x03, 0xee, 0xa0, 0x26, 0x6a, 0xd5, 0xc9, 0xb3, 0x89, 0x18, 0xb0, 0x01, + 0xc7, 0xef, 0x01, 0x8b, 0x4c, 0xe4, 0x82, 0xa5, 0xb4, 0x60, 0xe9, 0x70, 0x4c, 0x2e, 0x1b, 0xd2, + 0x1e, 0x3b, 0x17, 0xa5, 0x61, 0xe8, 0xb7, 0x30, 0xd1, 0xc4, 0x1d, 0x57, 0x23, 0x76, 0xd1, 0xb0, + 0x6b, 0x15, 0xdd, 0xa0, 0xbb, 0xb0, 0xa2, 0x33, 0x76, 0xa3, 0xfb, 0x32, 0x1f, 0x71, 0xd6, 0xe8, + 0xeb, 0x13, 0xd1, 0x40, 0x9f, 0x61, 0x5d, 0xb3, 0x82, 0x53, 0x9d, 0x8a, 0x1e, 0xa7, 0x22, 0x8b, + 0x25, 0xfd, 0xca, 0x63, 0x67, 0xa9, 0x89, 0x5a, 0x8d, 0xfd, 0x8d, 0xf6, 0xb4, 0xb9, 0x76, 0xc4, + 0x0a, 0x1e, 0x95, 0x94, 0x9f, 0xc5, 0xf2, 0x88, 0xc7, 0xc4, 0xd6, 0x0f, 0x14, 0xbc, 0x05, 0x0d, + 0xa1, 0xa9, 0xe2, 0x5a, 0x0e, 0x55, 0x8f, 0x3b, 0x4f, 0x9a, 0xa8, 0x55, 0x23, 0x20, 0x34, 0x19, + 0x2b, 0x78, 0x03, 0xea, 0xb9, 0x62, 0x22, 0x2b, 0x9b, 0x77, 0x9e, 0x1a, 0x7b, 0x2a, 0xe0, 0x53, + 0x58, 0xd3, 0xdf, 0xb3, 0x5e, 0x5f, 0xc9, 0x4c, 0xdc, 0xb1, 0x5c, 0xc8, 0xcc, 0xa9, 0x35, 0x51, + 0x6b, 0x75, 0x7f, 0xb7, 0xba, 0x8f, 0xc9, 0x01, 0x47, 0xff, 0xa3, 0xe4, 0x61, 0x16, 0xbb, 0xd0, + 0x60, 0x49, 0xa2, 0x78, 0x32, 0x2a, 0x55, 0x37, 0xa5, 0xb6, 0x66, 0x95, 0x72, 0xa7, 0x18, 0xa9, + 0x66, 0x76, 0x7e, 0x22, 0xb0, 0x1f, 0xf6, 0x8d, 0x5f, 0x42, 0x3d, 0x1e, 0xa6, 0x69, 0xf5, 0x46, + 0x6b, 0xa5, 0x60, 0xce, 0x73, 0x13, 0xc0, 0x98, 0xba, 0xcf, 0x6e, 0xca, 0x9b, 0xb1, 0x5a, 0x16, + 0x31, 0x78, 0x54, 0x0a, 0xa5, 0x5d, 0x30, 0x45, 0x65, 0x1c, 0x6b, 0x9e, 0x3b, 0xd6, 0xc8, 0x2e, + 0x98, 0x0a, 0x8d, 0x50, 0x96, 0x2e, 0xed, 0x51, 0x78, 0xc9, 0xb8, 0xb5, 0x82, 0x29, 0x93, 0xdd, + 0xfb, 0x8d, 0xe0, 0xf9, 0x9c, 0xe6, 0xf1, 0x36, 0x6c, 0x5e, 0xb8, 0xc4, 0x77, 0x0f, 0x4e, 0x3c, + 0x1a, 0x5d, 0x06, 0x87, 0x9f, 0x48, 0x18, 0xf8, 0x57, 0x6e, 0xd7, 0x0f, 0x03, 0xea, 0x9e, 0x77, + 0x43, 0x7b, 0xe1, 0x51, 0x24, 0x08, 0x03, 0xcf, 0x46, 0xf8, 0x35, 0x6c, 0xcf, 0x45, 0xc2, 0x80, + 0x7e, 0x21, 0x7e, 0xd7, 0xb3, 0x17, 0xf1, 0x2b, 0x68, 0x3e, 0x86, 0x11, 0xcf, 0x3d, 0xb2, 0xad, + 0xbd, 0x5f, 0x08, 0xd6, 0x67, 0x1c, 0x30, 0xde, 0x84, 0x17, 0xf7, 0x69, 0xf7, 0xf8, 0x98, 0x78, + 0xc7, 0x95, 0x3d, 0x2c, 0xe0, 0x0d, 0x70, 0x66, 0xda, 0xd1, 0xf9, 0xa9, 0x8d, 0xe6, 0x86, 0x4f, + 0x3d, 0x37, 0xb0, 0x17, 0xf1, 0x3b, 0x78, 0x33, 0xd3, 0x0e, 0x83, 0x93, 0x4b, 0xfa, 0xd1, 0x27, + 0x51, 0x97, 0x12, 0xef, 0xec, 0xc4, 0x3f, 0x74, 0x6d, 0xeb, 0xe0, 0x07, 0x02, 0x47, 0xaa, 0xa4, + 0xfa, 0x43, 0xdc, 0xbf, 0xf2, 0x83, 0xd5, 0xc9, 0xd6, 0xcf, 0xca, 0x57, 0xae, 0xcf, 0xd0, 0x55, + 0x98, 0x88, 0xbc, 0x3f, 0xbc, 0x6e, 0xf7, 0xe4, 0xa0, 0x53, 0x99, 0x0e, 0xb3, 0x97, 0x89, 0x9c, + 0x37, 0x36, 0x68, 0x22, 0xa9, 0x99, 0x1c, 0x7f, 0x11, 0xba, 0x5e, 0x36, 0xab, 0x0f, 0xff, 0x02, + 0x00, 0x00, 0xff, 0xff, 0x19, 0x77, 0xb5, 0x97, 0x6f, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/framework/versions_go_proto/versions.pb.go b/tensorflow/go/core/framework/versions_go_proto/versions.pb.go new file mode 100644 index 0000000..7b9f6b4 --- /dev/null +++ b/tensorflow/go/core/framework/versions_go_proto/versions.pb.go @@ -0,0 +1,118 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/framework/versions.proto + +package versions_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Version information for a piece of serialized data +// +// There are different types of versions for each type of data +// (GraphDef, etc.), but they all have the same common shape +// described here. +// +// Each consumer has "consumer" and "min_producer" versions (specified +// elsewhere). A consumer is allowed to consume this data if +// +// producer >= min_producer +// consumer >= min_consumer +// consumer not in bad_consumers +// +type VersionDef struct { + // The version of the code that produced this data. + Producer int32 `protobuf:"varint,1,opt,name=producer,proto3" json:"producer,omitempty"` + // Any consumer below this version is not allowed to consume this data. + MinConsumer int32 `protobuf:"varint,2,opt,name=min_consumer,json=minConsumer,proto3" json:"min_consumer,omitempty"` + // Specific consumer versions which are disallowed (e.g. due to bugs). + BadConsumers []int32 `protobuf:"varint,3,rep,packed,name=bad_consumers,json=badConsumers,proto3" json:"bad_consumers,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *VersionDef) Reset() { *m = VersionDef{} } +func (m *VersionDef) String() string { return proto.CompactTextString(m) } +func (*VersionDef) ProtoMessage() {} +func (*VersionDef) Descriptor() ([]byte, []int) { + return fileDescriptor_a28d4a384b75cac3, []int{0} +} + +func (m *VersionDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_VersionDef.Unmarshal(m, b) +} +func (m *VersionDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_VersionDef.Marshal(b, m, deterministic) +} +func (m *VersionDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_VersionDef.Merge(m, src) +} +func (m *VersionDef) XXX_Size() int { + return xxx_messageInfo_VersionDef.Size(m) +} +func (m *VersionDef) XXX_DiscardUnknown() { + xxx_messageInfo_VersionDef.DiscardUnknown(m) +} + +var xxx_messageInfo_VersionDef proto.InternalMessageInfo + +func (m *VersionDef) GetProducer() int32 { + if m != nil { + return m.Producer + } + return 0 +} + +func (m *VersionDef) GetMinConsumer() int32 { + if m != nil { + return m.MinConsumer + } + return 0 +} + +func (m *VersionDef) GetBadConsumers() []int32 { + if m != nil { + return m.BadConsumers + } + return nil +} + +func init() { + proto.RegisterType((*VersionDef)(nil), "tensorflow.VersionDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/framework/versions.proto", fileDescriptor_a28d4a384b75cac3) +} + +var fileDescriptor_a28d4a384b75cac3 = []byte{ + // 212 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x74, 0x8f, 0x31, 0x4f, 0xc3, 0x30, + 0x10, 0x85, 0x65, 0xaa, 0x22, 0x74, 0x14, 0x06, 0x4f, 0x16, 0x53, 0x81, 0x25, 0x93, 0x3d, 0xf0, + 0x0f, 0x0a, 0x3b, 0x55, 0x07, 0x06, 0x96, 0x28, 0x76, 0x1c, 0x63, 0x81, 0x7d, 0xd1, 0x5d, 0x42, + 0x56, 0x7e, 0x36, 0x23, 0x22, 0x84, 0x84, 0x81, 0x6e, 0xef, 0xee, 0xbd, 0xe1, 0xfb, 0xa0, 0xe8, + 0x7c, 0x66, 0xa4, 0xe6, 0x0d, 0x07, 0xe3, 0x90, 0xbc, 0x69, 0xa8, 0x4a, 0x7e, 0x40, 0x7a, 0x35, + 0xef, 0x9e, 0x38, 0x62, 0x66, 0xdd, 0x12, 0x76, 0x28, 0x61, 0x59, 0xde, 0xb4, 0x00, 0x4f, 0x3f, + 0xed, 0x83, 0x6f, 0xe4, 0x15, 0x9c, 0xb5, 0x84, 0x75, 0xef, 0x3c, 0x29, 0xb1, 0x15, 0xc5, 0xfa, + 0x30, 0xdf, 0xf2, 0x1a, 0x36, 0x29, 0xe6, 0xd2, 0x61, 0xe6, 0x3e, 0x79, 0x52, 0x27, 0x63, 0x7f, + 0x9e, 0x62, 0xbe, 0x9f, 0x5e, 0xf2, 0x16, 0x2e, 0x6c, 0x55, 0xcf, 0x13, 0x56, 0xab, 0xed, 0xaa, + 0x58, 0x1f, 0x36, 0xb6, 0xaa, 0x7f, 0x37, 0xbc, 0xfb, 0x10, 0xa0, 0x90, 0x82, 0x5e, 0x20, 0xf4, + 0x4c, 0xba, 0xbb, 0x9c, 0x60, 0x78, 0xff, 0x4d, 0xca, 0x7b, 0xf1, 0xfc, 0x18, 0x62, 0xf7, 0xd2, + 0x5b, 0xed, 0x30, 0x99, 0x3f, 0x86, 0xff, 0xc7, 0x80, 0xc7, 0xd4, 0xcb, 0x80, 0xe5, 0x68, 0xff, + 0x29, 0x84, 0x3d, 0x1d, 0xd3, 0xdd, 0x57, 0x00, 0x00, 0x00, 0xff, 0xff, 0x66, 0x3c, 0x7e, 0x28, + 0x33, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/autotuning.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/autotuning.pb.go new file mode 100644 index 0000000..5713ecd --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/autotuning.pb.go @@ -0,0 +1,599 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/autotuning.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + any "github.com/golang/protobuf/ptypes/any" + duration "github.com/golang/protobuf/ptypes/duration" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type AutotuneResult_FailureKind int32 + +const ( + AutotuneResult_UNKNOWN AutotuneResult_FailureKind = 0 + AutotuneResult_REDZONE_MODIFIED AutotuneResult_FailureKind = 1 + AutotuneResult_WRONG_RESULT AutotuneResult_FailureKind = 2 +) + +var AutotuneResult_FailureKind_name = map[int32]string{ + 0: "UNKNOWN", + 1: "REDZONE_MODIFIED", + 2: "WRONG_RESULT", +} + +var AutotuneResult_FailureKind_value = map[string]int32{ + "UNKNOWN": 0, + "REDZONE_MODIFIED": 1, + "WRONG_RESULT": 2, +} + +func (x AutotuneResult_FailureKind) String() string { + return proto.EnumName(AutotuneResult_FailureKind_name, int32(x)) +} + +func (AutotuneResult_FailureKind) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{2, 0} +} + +type CudnnVersion struct { + Major int32 `protobuf:"varint,1,opt,name=major,proto3" json:"major,omitempty"` + Minor int32 `protobuf:"varint,2,opt,name=minor,proto3" json:"minor,omitempty"` + Patch int32 `protobuf:"varint,3,opt,name=patch,proto3" json:"patch,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CudnnVersion) Reset() { *m = CudnnVersion{} } +func (m *CudnnVersion) String() string { return proto.CompactTextString(m) } +func (*CudnnVersion) ProtoMessage() {} +func (*CudnnVersion) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{0} +} + +func (m *CudnnVersion) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CudnnVersion.Unmarshal(m, b) +} +func (m *CudnnVersion) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CudnnVersion.Marshal(b, m, deterministic) +} +func (m *CudnnVersion) XXX_Merge(src proto.Message) { + xxx_messageInfo_CudnnVersion.Merge(m, src) +} +func (m *CudnnVersion) XXX_Size() int { + return xxx_messageInfo_CudnnVersion.Size(m) +} +func (m *CudnnVersion) XXX_DiscardUnknown() { + xxx_messageInfo_CudnnVersion.DiscardUnknown(m) +} + +var xxx_messageInfo_CudnnVersion proto.InternalMessageInfo + +func (m *CudnnVersion) GetMajor() int32 { + if m != nil { + return m.Major + } + return 0 +} + +func (m *CudnnVersion) GetMinor() int32 { + if m != nil { + return m.Minor + } + return 0 +} + +func (m *CudnnVersion) GetPatch() int32 { + if m != nil { + return m.Patch + } + return 0 +} + +type ComputeCapability struct { + Major int32 `protobuf:"varint,1,opt,name=major,proto3" json:"major,omitempty"` + Minor int32 `protobuf:"varint,2,opt,name=minor,proto3" json:"minor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ComputeCapability) Reset() { *m = ComputeCapability{} } +func (m *ComputeCapability) String() string { return proto.CompactTextString(m) } +func (*ComputeCapability) ProtoMessage() {} +func (*ComputeCapability) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{1} +} + +func (m *ComputeCapability) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ComputeCapability.Unmarshal(m, b) +} +func (m *ComputeCapability) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ComputeCapability.Marshal(b, m, deterministic) +} +func (m *ComputeCapability) XXX_Merge(src proto.Message) { + xxx_messageInfo_ComputeCapability.Merge(m, src) +} +func (m *ComputeCapability) XXX_Size() int { + return xxx_messageInfo_ComputeCapability.Size(m) +} +func (m *ComputeCapability) XXX_DiscardUnknown() { + xxx_messageInfo_ComputeCapability.DiscardUnknown(m) +} + +var xxx_messageInfo_ComputeCapability proto.InternalMessageInfo + +func (m *ComputeCapability) GetMajor() int32 { + if m != nil { + return m.Major + } + return 0 +} + +func (m *ComputeCapability) GetMinor() int32 { + if m != nil { + return m.Minor + } + return 0 +} + +type AutotuneResult struct { + ScratchBytes int64 `protobuf:"varint,8,opt,name=scratch_bytes,json=scratchBytes,proto3" json:"scratch_bytes,omitempty"` + RunTime *duration.Duration `protobuf:"bytes,9,opt,name=run_time,json=runTime,proto3" json:"run_time,omitempty"` + Failure *AutotuneResult_FailureResult `protobuf:"bytes,7,opt,name=failure,proto3" json:"failure,omitempty"` + // Types that are valid to be assigned to Key: + // *AutotuneResult_Conv + // *AutotuneResult_Gemm + Key isAutotuneResult_Key `protobuf_oneof:"key"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AutotuneResult) Reset() { *m = AutotuneResult{} } +func (m *AutotuneResult) String() string { return proto.CompactTextString(m) } +func (*AutotuneResult) ProtoMessage() {} +func (*AutotuneResult) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{2} +} + +func (m *AutotuneResult) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AutotuneResult.Unmarshal(m, b) +} +func (m *AutotuneResult) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AutotuneResult.Marshal(b, m, deterministic) +} +func (m *AutotuneResult) XXX_Merge(src proto.Message) { + xxx_messageInfo_AutotuneResult.Merge(m, src) +} +func (m *AutotuneResult) XXX_Size() int { + return xxx_messageInfo_AutotuneResult.Size(m) +} +func (m *AutotuneResult) XXX_DiscardUnknown() { + xxx_messageInfo_AutotuneResult.DiscardUnknown(m) +} + +var xxx_messageInfo_AutotuneResult proto.InternalMessageInfo + +func (m *AutotuneResult) GetScratchBytes() int64 { + if m != nil { + return m.ScratchBytes + } + return 0 +} + +func (m *AutotuneResult) GetRunTime() *duration.Duration { + if m != nil { + return m.RunTime + } + return nil +} + +func (m *AutotuneResult) GetFailure() *AutotuneResult_FailureResult { + if m != nil { + return m.Failure + } + return nil +} + +type isAutotuneResult_Key interface { + isAutotuneResult_Key() +} + +type AutotuneResult_Conv struct { + Conv *AutotuneResult_ConvKey `protobuf:"bytes,5,opt,name=conv,proto3,oneof"` +} + +type AutotuneResult_Gemm struct { + Gemm *AutotuneResult_GemmKey `protobuf:"bytes,6,opt,name=gemm,proto3,oneof"` +} + +func (*AutotuneResult_Conv) isAutotuneResult_Key() {} + +func (*AutotuneResult_Gemm) isAutotuneResult_Key() {} + +func (m *AutotuneResult) GetKey() isAutotuneResult_Key { + if m != nil { + return m.Key + } + return nil +} + +func (m *AutotuneResult) GetConv() *AutotuneResult_ConvKey { + if x, ok := m.GetKey().(*AutotuneResult_Conv); ok { + return x.Conv + } + return nil +} + +func (m *AutotuneResult) GetGemm() *AutotuneResult_GemmKey { + if x, ok := m.GetKey().(*AutotuneResult_Gemm); ok { + return x.Gemm + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*AutotuneResult) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*AutotuneResult_Conv)(nil), + (*AutotuneResult_Gemm)(nil), + } +} + +type AutotuneResult_FailureResult struct { + Kind AutotuneResult_FailureKind `protobuf:"varint,1,opt,name=kind,proto3,enum=tensorflow.AutotuneResult_FailureKind" json:"kind,omitempty"` + Msg string `protobuf:"bytes,2,opt,name=msg,proto3" json:"msg,omitempty"` + // For failure_kind == WRONG_RESULT, this field indicates the reference + // configuration that we compared against. + // + // Note that the reference algorithm isn't always correct. However, + // empirically it's more correct, as it's "algo 0", less fancy than the + // compared one. + // + // Types that are valid to be assigned to Key: + // *AutotuneResult_FailureResult_ReferenceConv + // *AutotuneResult_FailureResult_ReferenceGemm + Key isAutotuneResult_FailureResult_Key `protobuf_oneof:"key"` + BufferAddress int64 `protobuf:"varint,13,opt,name=buffer_address,json=bufferAddress,proto3" json:"buffer_address,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AutotuneResult_FailureResult) Reset() { *m = AutotuneResult_FailureResult{} } +func (m *AutotuneResult_FailureResult) String() string { return proto.CompactTextString(m) } +func (*AutotuneResult_FailureResult) ProtoMessage() {} +func (*AutotuneResult_FailureResult) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{2, 0} +} + +func (m *AutotuneResult_FailureResult) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AutotuneResult_FailureResult.Unmarshal(m, b) +} +func (m *AutotuneResult_FailureResult) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AutotuneResult_FailureResult.Marshal(b, m, deterministic) +} +func (m *AutotuneResult_FailureResult) XXX_Merge(src proto.Message) { + xxx_messageInfo_AutotuneResult_FailureResult.Merge(m, src) +} +func (m *AutotuneResult_FailureResult) XXX_Size() int { + return xxx_messageInfo_AutotuneResult_FailureResult.Size(m) +} +func (m *AutotuneResult_FailureResult) XXX_DiscardUnknown() { + xxx_messageInfo_AutotuneResult_FailureResult.DiscardUnknown(m) +} + +var xxx_messageInfo_AutotuneResult_FailureResult proto.InternalMessageInfo + +func (m *AutotuneResult_FailureResult) GetKind() AutotuneResult_FailureKind { + if m != nil { + return m.Kind + } + return AutotuneResult_UNKNOWN +} + +func (m *AutotuneResult_FailureResult) GetMsg() string { + if m != nil { + return m.Msg + } + return "" +} + +type isAutotuneResult_FailureResult_Key interface { + isAutotuneResult_FailureResult_Key() +} + +type AutotuneResult_FailureResult_ReferenceConv struct { + ReferenceConv *AutotuneResult_ConvKey `protobuf:"bytes,11,opt,name=reference_conv,json=referenceConv,proto3,oneof"` +} + +type AutotuneResult_FailureResult_ReferenceGemm struct { + ReferenceGemm *AutotuneResult_GemmKey `protobuf:"bytes,12,opt,name=reference_gemm,json=referenceGemm,proto3,oneof"` +} + +func (*AutotuneResult_FailureResult_ReferenceConv) isAutotuneResult_FailureResult_Key() {} + +func (*AutotuneResult_FailureResult_ReferenceGemm) isAutotuneResult_FailureResult_Key() {} + +func (m *AutotuneResult_FailureResult) GetKey() isAutotuneResult_FailureResult_Key { + if m != nil { + return m.Key + } + return nil +} + +func (m *AutotuneResult_FailureResult) GetReferenceConv() *AutotuneResult_ConvKey { + if x, ok := m.GetKey().(*AutotuneResult_FailureResult_ReferenceConv); ok { + return x.ReferenceConv + } + return nil +} + +func (m *AutotuneResult_FailureResult) GetReferenceGemm() *AutotuneResult_GemmKey { + if x, ok := m.GetKey().(*AutotuneResult_FailureResult_ReferenceGemm); ok { + return x.ReferenceGemm + } + return nil +} + +func (m *AutotuneResult_FailureResult) GetBufferAddress() int64 { + if m != nil { + return m.BufferAddress + } + return 0 +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*AutotuneResult_FailureResult) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*AutotuneResult_FailureResult_ReferenceConv)(nil), + (*AutotuneResult_FailureResult_ReferenceGemm)(nil), + } +} + +type AutotuneResult_ConvKey struct { + Algorithm int64 `protobuf:"varint,1,opt,name=algorithm,proto3" json:"algorithm,omitempty"` + TensorOpsEnabled bool `protobuf:"varint,2,opt,name=tensor_ops_enabled,json=tensorOpsEnabled,proto3" json:"tensor_ops_enabled,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AutotuneResult_ConvKey) Reset() { *m = AutotuneResult_ConvKey{} } +func (m *AutotuneResult_ConvKey) String() string { return proto.CompactTextString(m) } +func (*AutotuneResult_ConvKey) ProtoMessage() {} +func (*AutotuneResult_ConvKey) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{2, 1} +} + +func (m *AutotuneResult_ConvKey) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AutotuneResult_ConvKey.Unmarshal(m, b) +} +func (m *AutotuneResult_ConvKey) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AutotuneResult_ConvKey.Marshal(b, m, deterministic) +} +func (m *AutotuneResult_ConvKey) XXX_Merge(src proto.Message) { + xxx_messageInfo_AutotuneResult_ConvKey.Merge(m, src) +} +func (m *AutotuneResult_ConvKey) XXX_Size() int { + return xxx_messageInfo_AutotuneResult_ConvKey.Size(m) +} +func (m *AutotuneResult_ConvKey) XXX_DiscardUnknown() { + xxx_messageInfo_AutotuneResult_ConvKey.DiscardUnknown(m) +} + +var xxx_messageInfo_AutotuneResult_ConvKey proto.InternalMessageInfo + +func (m *AutotuneResult_ConvKey) GetAlgorithm() int64 { + if m != nil { + return m.Algorithm + } + return 0 +} + +func (m *AutotuneResult_ConvKey) GetTensorOpsEnabled() bool { + if m != nil { + return m.TensorOpsEnabled + } + return false +} + +type AutotuneResult_GemmKey struct { + Algorithm int64 `protobuf:"varint,1,opt,name=algorithm,proto3" json:"algorithm,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AutotuneResult_GemmKey) Reset() { *m = AutotuneResult_GemmKey{} } +func (m *AutotuneResult_GemmKey) String() string { return proto.CompactTextString(m) } +func (*AutotuneResult_GemmKey) ProtoMessage() {} +func (*AutotuneResult_GemmKey) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{2, 2} +} + +func (m *AutotuneResult_GemmKey) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AutotuneResult_GemmKey.Unmarshal(m, b) +} +func (m *AutotuneResult_GemmKey) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AutotuneResult_GemmKey.Marshal(b, m, deterministic) +} +func (m *AutotuneResult_GemmKey) XXX_Merge(src proto.Message) { + xxx_messageInfo_AutotuneResult_GemmKey.Merge(m, src) +} +func (m *AutotuneResult_GemmKey) XXX_Size() int { + return xxx_messageInfo_AutotuneResult_GemmKey.Size(m) +} +func (m *AutotuneResult_GemmKey) XXX_DiscardUnknown() { + xxx_messageInfo_AutotuneResult_GemmKey.DiscardUnknown(m) +} + +var xxx_messageInfo_AutotuneResult_GemmKey proto.InternalMessageInfo + +func (m *AutotuneResult_GemmKey) GetAlgorithm() int64 { + if m != nil { + return m.Algorithm + } + return 0 +} + +type AutotuningLog struct { + Instr *any.Any `protobuf:"bytes,1,opt,name=instr,proto3" json:"instr,omitempty"` + // Records all auto-tuning results per algorithm. + Results []*AutotuneResult `protobuf:"bytes,2,rep,name=results,proto3" json:"results,omitempty"` + CudnnVersion *CudnnVersion `protobuf:"bytes,3,opt,name=cudnn_version,json=cudnnVersion,proto3" json:"cudnn_version,omitempty"` + ComputeCapability *ComputeCapability `protobuf:"bytes,4,opt,name=compute_capability,json=computeCapability,proto3" json:"compute_capability,omitempty"` + // stream_executor::DeviceDescription::pci_bus_id. + DevicePciBusId string `protobuf:"bytes,5,opt,name=device_pci_bus_id,json=devicePciBusId,proto3" json:"device_pci_bus_id,omitempty"` + BlasVersion string `protobuf:"bytes,6,opt,name=blas_version,json=blasVersion,proto3" json:"blas_version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AutotuningLog) Reset() { *m = AutotuningLog{} } +func (m *AutotuningLog) String() string { return proto.CompactTextString(m) } +func (*AutotuningLog) ProtoMessage() {} +func (*AutotuningLog) Descriptor() ([]byte, []int) { + return fileDescriptor_f61248520e180396, []int{3} +} + +func (m *AutotuningLog) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AutotuningLog.Unmarshal(m, b) +} +func (m *AutotuningLog) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AutotuningLog.Marshal(b, m, deterministic) +} +func (m *AutotuningLog) XXX_Merge(src proto.Message) { + xxx_messageInfo_AutotuningLog.Merge(m, src) +} +func (m *AutotuningLog) XXX_Size() int { + return xxx_messageInfo_AutotuningLog.Size(m) +} +func (m *AutotuningLog) XXX_DiscardUnknown() { + xxx_messageInfo_AutotuningLog.DiscardUnknown(m) +} + +var xxx_messageInfo_AutotuningLog proto.InternalMessageInfo + +func (m *AutotuningLog) GetInstr() *any.Any { + if m != nil { + return m.Instr + } + return nil +} + +func (m *AutotuningLog) GetResults() []*AutotuneResult { + if m != nil { + return m.Results + } + return nil +} + +func (m *AutotuningLog) GetCudnnVersion() *CudnnVersion { + if m != nil { + return m.CudnnVersion + } + return nil +} + +func (m *AutotuningLog) GetComputeCapability() *ComputeCapability { + if m != nil { + return m.ComputeCapability + } + return nil +} + +func (m *AutotuningLog) GetDevicePciBusId() string { + if m != nil { + return m.DevicePciBusId + } + return "" +} + +func (m *AutotuningLog) GetBlasVersion() string { + if m != nil { + return m.BlasVersion + } + return "" +} + +func init() { + proto.RegisterEnum("tensorflow.AutotuneResult_FailureKind", AutotuneResult_FailureKind_name, AutotuneResult_FailureKind_value) + proto.RegisterType((*CudnnVersion)(nil), "tensorflow.CudnnVersion") + proto.RegisterType((*ComputeCapability)(nil), "tensorflow.ComputeCapability") + proto.RegisterType((*AutotuneResult)(nil), "tensorflow.AutotuneResult") + proto.RegisterType((*AutotuneResult_FailureResult)(nil), "tensorflow.AutotuneResult.FailureResult") + proto.RegisterType((*AutotuneResult_ConvKey)(nil), "tensorflow.AutotuneResult.ConvKey") + proto.RegisterType((*AutotuneResult_GemmKey)(nil), "tensorflow.AutotuneResult.GemmKey") + proto.RegisterType((*AutotuningLog)(nil), "tensorflow.AutotuningLog") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/autotuning.proto", fileDescriptor_f61248520e180396) +} + +var fileDescriptor_f61248520e180396 = []byte{ + // 711 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x54, 0xdb, 0x6e, 0x1a, 0x3b, + 0x14, 0x0d, 0x10, 0x20, 0x78, 0x00, 0x11, 0x2b, 0x0f, 0x13, 0x74, 0xce, 0x11, 0x87, 0xa3, 0xd3, + 0x92, 0xaa, 0x1a, 0x24, 0x9a, 0x87, 0xaa, 0x52, 0x55, 0x71, 0x4b, 0x1a, 0x91, 0x42, 0xe4, 0x86, + 0x46, 0xca, 0x8b, 0x35, 0x17, 0x33, 0x71, 0x33, 0x63, 0x23, 0x7b, 0x86, 0x8a, 0x1f, 0xe9, 0x1f, + 0xf4, 0x6f, 0xfa, 0x51, 0xd5, 0xd8, 0x13, 0x2e, 0x49, 0x9b, 0x28, 0x4f, 0xd8, 0x6b, 0xef, 0xb5, + 0xb4, 0xd7, 0x66, 0xbc, 0xc0, 0x51, 0x44, 0x98, 0xe4, 0x62, 0x16, 0xf0, 0x6f, 0x6d, 0x97, 0x0b, + 0xd2, 0x9e, 0x0b, 0x1e, 0x71, 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b/tensorflow/go/core/protobuf/for_core_protos_go_proto/bfc_memory_map.pb.go @@ -0,0 +1,439 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/bfc_memory_map.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Some of the data from AllocatorStats +type MemAllocatorStats struct { + NumAllocs int64 `protobuf:"varint,1,opt,name=num_allocs,json=numAllocs,proto3" json:"num_allocs,omitempty"` + BytesInUse int64 `protobuf:"varint,2,opt,name=bytes_in_use,json=bytesInUse,proto3" json:"bytes_in_use,omitempty"` + PeakBytesInUse int64 `protobuf:"varint,3,opt,name=peak_bytes_in_use,json=peakBytesInUse,proto3" json:"peak_bytes_in_use,omitempty"` + LargestAllocSize int64 `protobuf:"varint,4,opt,name=largest_alloc_size,json=largestAllocSize,proto3" json:"largest_alloc_size,omitempty"` + FragmentationMetric float32 `protobuf:"fixed32,5,opt,name=fragmentation_metric,json=fragmentationMetric,proto3" json:"fragmentation_metric,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemAllocatorStats) Reset() { *m = MemAllocatorStats{} } +func (m *MemAllocatorStats) String() string { return proto.CompactTextString(m) } +func (*MemAllocatorStats) ProtoMessage() {} +func (*MemAllocatorStats) Descriptor() ([]byte, []int) { + return fileDescriptor_fdf22777007c1f3d, []int{0} +} + +func (m *MemAllocatorStats) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemAllocatorStats.Unmarshal(m, b) +} +func (m *MemAllocatorStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemAllocatorStats.Marshal(b, m, deterministic) +} +func (m *MemAllocatorStats) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemAllocatorStats.Merge(m, src) +} +func (m *MemAllocatorStats) XXX_Size() int { + return xxx_messageInfo_MemAllocatorStats.Size(m) +} +func (m *MemAllocatorStats) XXX_DiscardUnknown() { + xxx_messageInfo_MemAllocatorStats.DiscardUnknown(m) +} + +var xxx_messageInfo_MemAllocatorStats proto.InternalMessageInfo + +func (m *MemAllocatorStats) GetNumAllocs() int64 { + if m != nil { + return m.NumAllocs + } + return 0 +} + +func (m *MemAllocatorStats) GetBytesInUse() int64 { + if m != nil { + return m.BytesInUse + } + return 0 +} + +func (m *MemAllocatorStats) GetPeakBytesInUse() int64 { + if m != nil { + return m.PeakBytesInUse + } + return 0 +} + +func (m *MemAllocatorStats) GetLargestAllocSize() int64 { + if m != nil { + return m.LargestAllocSize + } + return 0 +} + +func (m *MemAllocatorStats) GetFragmentationMetric() float32 { + if m != nil { + return m.FragmentationMetric + } + return 0 +} + +type MemChunk struct { + Address uint64 `protobuf:"varint,1,opt,name=address,proto3" json:"address,omitempty"` + Size int64 `protobuf:"varint,2,opt,name=size,proto3" json:"size,omitempty"` + RequestedSize int64 `protobuf:"varint,3,opt,name=requested_size,json=requestedSize,proto3" json:"requested_size,omitempty"` + Bin int32 `protobuf:"varint,4,opt,name=bin,proto3" json:"bin,omitempty"` + OpName string `protobuf:"bytes,5,opt,name=op_name,json=opName,proto3" json:"op_name,omitempty"` + FreedAtCount uint64 `protobuf:"varint,6,opt,name=freed_at_count,json=freedAtCount,proto3" json:"freed_at_count,omitempty"` + ActionCount uint64 `protobuf:"varint,7,opt,name=action_count,json=actionCount,proto3" json:"action_count,omitempty"` + InUse bool `protobuf:"varint,8,opt,name=in_use,json=inUse,proto3" json:"in_use,omitempty"` + StepId uint64 `protobuf:"varint,9,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemChunk) Reset() { *m = MemChunk{} } +func (m *MemChunk) String() string { return proto.CompactTextString(m) } +func (*MemChunk) ProtoMessage() {} +func (*MemChunk) Descriptor() ([]byte, []int) { + return fileDescriptor_fdf22777007c1f3d, []int{1} +} + +func (m *MemChunk) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemChunk.Unmarshal(m, b) +} +func (m *MemChunk) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemChunk.Marshal(b, m, deterministic) +} +func (m *MemChunk) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemChunk.Merge(m, src) +} +func (m *MemChunk) XXX_Size() int { + return xxx_messageInfo_MemChunk.Size(m) +} +func (m *MemChunk) XXX_DiscardUnknown() { + xxx_messageInfo_MemChunk.DiscardUnknown(m) +} + +var xxx_messageInfo_MemChunk proto.InternalMessageInfo + +func (m *MemChunk) GetAddress() uint64 { + if m != nil { + return m.Address + } + return 0 +} + +func (m *MemChunk) GetSize() int64 { + if m != nil { + return m.Size + } + return 0 +} + +func (m *MemChunk) GetRequestedSize() int64 { + if m != nil { + return m.RequestedSize + } + return 0 +} + +func (m *MemChunk) GetBin() int32 { + if m != nil { + return m.Bin + } + return 0 +} + +func (m *MemChunk) GetOpName() string { + if m != nil { + return m.OpName + } + return "" +} + +func (m *MemChunk) GetFreedAtCount() uint64 { + if m != nil { + return m.FreedAtCount + } + return 0 +} + +func (m *MemChunk) GetActionCount() uint64 { + if m != nil { + return m.ActionCount + } + return 0 +} + +func (m *MemChunk) GetInUse() bool { + if m != nil { + return m.InUse + } + return false +} + +func (m *MemChunk) GetStepId() uint64 { + if m != nil { + return m.StepId + } + return 0 +} + +type BinSummary struct { + Bin int32 `protobuf:"varint,1,opt,name=bin,proto3" json:"bin,omitempty"` + TotalBytesInUse int64 `protobuf:"varint,2,opt,name=total_bytes_in_use,json=totalBytesInUse,proto3" json:"total_bytes_in_use,omitempty"` + TotalBytesInBin int64 `protobuf:"varint,3,opt,name=total_bytes_in_bin,json=totalBytesInBin,proto3" json:"total_bytes_in_bin,omitempty"` + TotalChunksInUse int64 `protobuf:"varint,4,opt,name=total_chunks_in_use,json=totalChunksInUse,proto3" json:"total_chunks_in_use,omitempty"` + TotalChunksInBin int64 `protobuf:"varint,5,opt,name=total_chunks_in_bin,json=totalChunksInBin,proto3" json:"total_chunks_in_bin,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *BinSummary) Reset() { *m = BinSummary{} } +func (m *BinSummary) String() string { return proto.CompactTextString(m) } +func (*BinSummary) ProtoMessage() {} +func (*BinSummary) Descriptor() ([]byte, []int) { + return fileDescriptor_fdf22777007c1f3d, []int{2} +} + +func (m *BinSummary) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_BinSummary.Unmarshal(m, b) +} +func (m *BinSummary) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_BinSummary.Marshal(b, m, deterministic) +} +func (m *BinSummary) XXX_Merge(src proto.Message) { + xxx_messageInfo_BinSummary.Merge(m, src) +} +func (m *BinSummary) XXX_Size() int { + return xxx_messageInfo_BinSummary.Size(m) +} +func (m *BinSummary) XXX_DiscardUnknown() { + xxx_messageInfo_BinSummary.DiscardUnknown(m) +} + +var xxx_messageInfo_BinSummary proto.InternalMessageInfo + +func (m *BinSummary) GetBin() int32 { + if m != nil { + return m.Bin + } + return 0 +} + +func (m *BinSummary) GetTotalBytesInUse() int64 { + if m != nil { + return m.TotalBytesInUse + } + return 0 +} + +func (m *BinSummary) GetTotalBytesInBin() int64 { + if m != nil { + return m.TotalBytesInBin + } + return 0 +} + +func (m *BinSummary) GetTotalChunksInUse() int64 { + if m != nil { + return m.TotalChunksInUse + } + return 0 +} + +func (m *BinSummary) GetTotalChunksInBin() int64 { + if m != nil { + return m.TotalChunksInBin + } + return 0 +} + +type SnapShot struct { + ActionCount uint64 `protobuf:"varint,1,opt,name=action_count,json=actionCount,proto3" json:"action_count,omitempty"` + Size int64 `protobuf:"varint,2,opt,name=size,proto3" json:"size,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SnapShot) Reset() { *m = SnapShot{} } +func (m *SnapShot) String() string { return proto.CompactTextString(m) } +func (*SnapShot) ProtoMessage() {} +func (*SnapShot) Descriptor() ([]byte, []int) { + return fileDescriptor_fdf22777007c1f3d, []int{3} +} + +func (m *SnapShot) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SnapShot.Unmarshal(m, b) +} +func (m *SnapShot) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SnapShot.Marshal(b, m, deterministic) +} +func (m *SnapShot) XXX_Merge(src proto.Message) { + xxx_messageInfo_SnapShot.Merge(m, src) +} +func (m *SnapShot) XXX_Size() int { + return xxx_messageInfo_SnapShot.Size(m) +} +func (m *SnapShot) XXX_DiscardUnknown() { + xxx_messageInfo_SnapShot.DiscardUnknown(m) +} + +var xxx_messageInfo_SnapShot proto.InternalMessageInfo + +func (m *SnapShot) GetActionCount() uint64 { + if m != nil { + return m.ActionCount + } + return 0 +} + +func (m *SnapShot) GetSize() int64 { + if m != nil { + return m.Size + } + return 0 +} + +type MemoryDump struct { + AllocatorName string `protobuf:"bytes,1,opt,name=allocator_name,json=allocatorName,proto3" json:"allocator_name,omitempty"` + BinSummary []*BinSummary `protobuf:"bytes,2,rep,name=bin_summary,json=binSummary,proto3" json:"bin_summary,omitempty"` + Chunk []*MemChunk `protobuf:"bytes,3,rep,name=chunk,proto3" json:"chunk,omitempty"` + SnapShot []*SnapShot `protobuf:"bytes,4,rep,name=snap_shot,json=snapShot,proto3" json:"snap_shot,omitempty"` + Stats *MemAllocatorStats `protobuf:"bytes,5,opt,name=stats,proto3" json:"stats,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MemoryDump) Reset() { *m = MemoryDump{} } +func (m *MemoryDump) String() string { return proto.CompactTextString(m) } +func (*MemoryDump) ProtoMessage() {} +func (*MemoryDump) Descriptor() ([]byte, []int) { + return fileDescriptor_fdf22777007c1f3d, []int{4} +} + +func (m *MemoryDump) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MemoryDump.Unmarshal(m, b) +} +func (m *MemoryDump) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MemoryDump.Marshal(b, m, deterministic) +} +func (m *MemoryDump) XXX_Merge(src proto.Message) { + xxx_messageInfo_MemoryDump.Merge(m, src) +} +func (m *MemoryDump) XXX_Size() int { + return xxx_messageInfo_MemoryDump.Size(m) +} +func (m *MemoryDump) XXX_DiscardUnknown() { + xxx_messageInfo_MemoryDump.DiscardUnknown(m) +} + +var xxx_messageInfo_MemoryDump proto.InternalMessageInfo + +func (m *MemoryDump) GetAllocatorName() string { + if m != nil { + return m.AllocatorName + } + return "" +} + +func (m *MemoryDump) GetBinSummary() []*BinSummary { + if m != nil { + return m.BinSummary + } + return nil +} + +func (m *MemoryDump) GetChunk() []*MemChunk { + if m != nil { + return m.Chunk + } + return nil +} + +func (m *MemoryDump) GetSnapShot() []*SnapShot { + if m != nil { + return m.SnapShot + } + return nil +} + +func (m *MemoryDump) GetStats() *MemAllocatorStats { + if m != nil { + return m.Stats + } + return nil +} + +func init() { + proto.RegisterType((*MemAllocatorStats)(nil), "tensorflow.MemAllocatorStats") + proto.RegisterType((*MemChunk)(nil), "tensorflow.MemChunk") + proto.RegisterType((*BinSummary)(nil), "tensorflow.BinSummary") + proto.RegisterType((*SnapShot)(nil), "tensorflow.SnapShot") + proto.RegisterType((*MemoryDump)(nil), "tensorflow.MemoryDump") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/bfc_memory_map.proto", fileDescriptor_fdf22777007c1f3d) +} + +var fileDescriptor_fdf22777007c1f3d = []byte{ + // 599 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x6c, 0x94, 0x3d, 0x6f, 0xdb, 0x30, + 0x10, 0x86, 0x21, 0x7f, 0xfb, 0x9c, 0xa4, 0x09, 0x93, 0x36, 0x5a, 0x02, 0xb8, 0x46, 0x0b, 0xb8, + 0x1f, 0xb1, 0x91, 0x64, 0xe8, 0x1c, 0xa7, 0x4b, 0x06, 0x77, 0x90, 0x11, 0x14, 0xe8, 0x42, 0x50, + 0x32, 0x65, 0x13, 0x31, 0x49, 0x95, 0xa4, 0x50, 0x24, 0x53, 0x81, 0xfe, 0xc7, 0xce, 0xfd, 0x29, + 0x85, 0x8e, 0x56, 0x14, 0xc7, 0x9e, 0x4c, 0xbd, 0xf7, 0xe8, 0x78, 0x77, 0xaf, 0x4f, 0x70, 0xee, + 0xb8, 0xb2, 0xda, 0xa4, 0x2b, 0xfd, 0x6b, 0x9c, 0x68, 0xc3, 0xc7, 0x99, 0xd1, 0x4e, 0xc7, 0x79, + 0x3a, 0x8e, 0xd3, 0x84, 0x4a, 0x2e, 0xb5, 0x79, 0xa0, 0x92, 0x65, 0x23, 0xd4, 0x09, 0x54, 0xf8, + 0xe0, 0x5f, 0x00, 0x47, 0x53, 0x2e, 0xaf, 0x57, 0x2b, 0x9d, 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a/tensorflow/go/core/protobuf/for_core_protos_go_proto/cluster.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/cluster.pb.go new file mode 100644 index 0000000..8d46cb0 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/cluster.pb.go @@ -0,0 +1,147 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/cluster.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Defines a single job in a TensorFlow cluster. +type JobDef struct { + // The name of this job. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Mapping from task ID to "hostname:port" string. + // + // If the `name` field contains "worker", and the `tasks` map contains a + // mapping from 7 to "example.org:2222", then the device prefix + // "/job:worker/task:7" will be assigned to "example.org:2222". + Tasks map[int32]string `protobuf:"bytes,2,rep,name=tasks,proto3" json:"tasks,omitempty" protobuf_key:"varint,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *JobDef) Reset() { *m = JobDef{} } +func (m *JobDef) String() string { return proto.CompactTextString(m) } +func (*JobDef) ProtoMessage() {} +func (*JobDef) Descriptor() ([]byte, []int) { + return fileDescriptor_8ea47a9615190cff, []int{0} +} + +func (m *JobDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_JobDef.Unmarshal(m, b) +} +func (m *JobDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_JobDef.Marshal(b, m, deterministic) +} +func (m *JobDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_JobDef.Merge(m, src) +} +func (m *JobDef) XXX_Size() int { + return xxx_messageInfo_JobDef.Size(m) +} +func (m *JobDef) XXX_DiscardUnknown() { + xxx_messageInfo_JobDef.DiscardUnknown(m) +} + +var xxx_messageInfo_JobDef proto.InternalMessageInfo + +func (m *JobDef) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *JobDef) GetTasks() map[int32]string { + if m != nil { + return m.Tasks + } + return nil +} + +// Defines a TensorFlow cluster as a set of jobs. +type ClusterDef struct { + // The jobs that comprise the cluster. + Job []*JobDef `protobuf:"bytes,1,rep,name=job,proto3" json:"job,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ClusterDef) Reset() { *m = ClusterDef{} } +func (m *ClusterDef) String() string { return proto.CompactTextString(m) } +func (*ClusterDef) ProtoMessage() {} +func (*ClusterDef) Descriptor() ([]byte, []int) { + return fileDescriptor_8ea47a9615190cff, []int{1} +} + +func (m *ClusterDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ClusterDef.Unmarshal(m, b) +} +func (m *ClusterDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ClusterDef.Marshal(b, m, deterministic) +} +func (m *ClusterDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_ClusterDef.Merge(m, src) +} +func (m *ClusterDef) XXX_Size() int { + return xxx_messageInfo_ClusterDef.Size(m) +} +func (m *ClusterDef) XXX_DiscardUnknown() { + xxx_messageInfo_ClusterDef.DiscardUnknown(m) +} + +var xxx_messageInfo_ClusterDef proto.InternalMessageInfo + +func (m *ClusterDef) GetJob() []*JobDef { + if m != nil { + return m.Job + } + return nil +} + +func init() { + proto.RegisterType((*JobDef)(nil), "tensorflow.JobDef") + proto.RegisterMapType((map[int32]string)(nil), "tensorflow.JobDef.TasksEntry") + proto.RegisterType((*ClusterDef)(nil), "tensorflow.ClusterDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/cluster.proto", fileDescriptor_8ea47a9615190cff) +} + +var fileDescriptor_8ea47a9615190cff = []byte{ + // 265 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x6c, 0x90, 0x4f, 0x4b, 0xf3, 0x40, + 0x10, 0xc6, 0x99, 0xe4, 0x4d, 0xe1, 0x1d, 0x11, 0x64, 0xf1, 0x10, 0x0a, 0x42, 0x29, 0x22, 0x3d, + 0x6d, 0xa0, 0xbd, 0x14, 0x8f, 0x55, 0x2f, 0x9e, 0x4a, 0xd0, 0x8b, 0x97, 0x90, 0x8d, 0x9b, 0x18, + 0x9b, 0x64, 0x64, 0xff, 0x28, 0xfd, 0x04, 0x1e, 0xfc, 0xc2, 0x1e, 0x65, 0x77, 0x85, 0x14, 0xf5, + 0xf6, 0xdb, 0xe7, 0x99, 0x99, 0x67, 0x76, 0xf0, 0xc2, 0xc8, 0x41, 0x93, 0xaa, 0x3b, 0x7a, 0xcb, + 0x2a, 0x52, 0x32, 0x7b, 0x51, 0x64, 0x48, 0xd8, 0x3a, 0xab, 0x3a, 0xab, 0x8d, 0x54, 0xdc, 0x0b, + 0x0c, 0xc7, 0xba, 0xf9, 0x07, 0xe0, 0xe4, 0x96, 0xc4, 0xb5, 0xac, 0x19, 0xc3, 0x7f, 0x43, 0xd9, + 0xcb, 0x14, 0x66, 0xb0, 0xf8, 0x9f, 0x7b, 0x66, 0x2b, 0x4c, 0x4c, 0xa9, 0x77, 0x3a, 0x8d, 0x66, + 0xf1, 0xe2, 0x68, 0x79, 0xc6, 0xc7, 0x56, 0x1e, 0xda, 0xf8, 0x9d, 0xf3, 0x6f, 0x06, 0xa3, 0xf6, + 0x79, 0xa8, 0x9d, 0xae, 0x11, 0x47, 0x91, 0x9d, 0x60, 0xbc, 0x93, 0x7b, 0x3f, 0x35, 0xc9, 0x1d, + 0xb2, 0x53, 0x4c, 0x5e, 0xcb, 0xce, 0xca, 0x34, 0xf2, 0x49, 0xe1, 0x71, 0x19, 0xad, 0x61, 0xbe, + 0x44, 0xbc, 0x0a, 0xab, 0xba, 0x85, 0xce, 0x31, 0x7e, 0x26, 0x91, 0x82, 0x8f, 0x66, 0xbf, 0xa3, + 0x73, 0x67, 0x6f, 0xde, 0x01, 0xa7, 0xa4, 0x9a, 0x43, 0xfb, 0xb1, 0xd5, 0x46, 0xd9, 0xc1, 0xb4, + 0xbd, 0xdc, 0x1c, 0x7f, 0x0f, 0xdc, 0xba, 0xaf, 0xeb, 0x2d, 0x3c, 0xdc, 0x37, 0xad, 0x79, 0xb2, + 0x82, 0x57, 0xd4, 0x67, 0x07, 0x07, 0xfb, 0x1b, 0x1b, 0xfa, 0x71, 0xc9, 0x9a, 0x54, 0xe1, 0x94, + 0xc2, 0x2b, 0xba, 0x68, 0x28, 0xd0, 0x27, 0x80, 0x98, 0x78, 0x5a, 0x7d, 0x05, 0x00, 0x00, 0xff, + 0xff, 0xd0, 0xec, 0x2d, 0x87, 0x88, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/config.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/config.pb.go new file mode 100644 index 0000000..8427b53 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/config.pb.go @@ -0,0 +1,2263 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/config.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + cost_graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/cost_graph_go_proto" + graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto" + step_stats_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/step_stats_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Optimization level +type OptimizerOptions_Level int32 + +const ( + // L1 is the default level. + // Optimization performed at L1 : + // 1. Common subexpression elimination + // 2. Constant folding + OptimizerOptions_L1 OptimizerOptions_Level = 0 + // No optimizations + OptimizerOptions_L0 OptimizerOptions_Level = -1 +) + +var OptimizerOptions_Level_name = map[int32]string{ + 0: "L1", + -1: "L0", +} + +var OptimizerOptions_Level_value = map[string]int32{ + "L1": 0, + "L0": -1, +} + +func (x OptimizerOptions_Level) String() string { + return proto.EnumName(OptimizerOptions_Level_name, int32(x)) +} + +func (OptimizerOptions_Level) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{1, 0} +} + +// Control the use of the compiler/jit. Experimental. +type OptimizerOptions_GlobalJitLevel int32 + +const ( + OptimizerOptions_DEFAULT OptimizerOptions_GlobalJitLevel = 0 + OptimizerOptions_OFF OptimizerOptions_GlobalJitLevel = -1 + // The following settings turn on compilation, with higher values being + // more aggressive. Higher values may reduce opportunities for parallelism + // and may use more memory. (At present, there is no distinction, but this + // is expected to change.) + OptimizerOptions_ON_1 OptimizerOptions_GlobalJitLevel = 1 + OptimizerOptions_ON_2 OptimizerOptions_GlobalJitLevel = 2 +) + +var OptimizerOptions_GlobalJitLevel_name = map[int32]string{ + 0: "DEFAULT", + -1: "OFF", + 1: "ON_1", + 2: "ON_2", +} + +var OptimizerOptions_GlobalJitLevel_value = map[string]int32{ + "DEFAULT": 0, + "OFF": -1, + "ON_1": 1, + "ON_2": 2, +} + +func (x OptimizerOptions_GlobalJitLevel) String() string { + return proto.EnumName(OptimizerOptions_GlobalJitLevel_name, int32(x)) +} + +func (OptimizerOptions_GlobalJitLevel) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{1, 1} +} + +// An enum that describes the state of the MLIR bridge rollout. +type ConfigProto_Experimental_MlirBridgeRollout int32 + +const ( + // If this field is left unspecified, the MLIR bridge may be selectively + // enabled on a per graph basis. + ConfigProto_Experimental_MLIR_BRIDGE_ROLLOUT_UNSPECIFIED ConfigProto_Experimental_MlirBridgeRollout = 0 + // Enabling the MLIR bridge enables it for all graphs in this session. + ConfigProto_Experimental_MLIR_BRIDGE_ROLLOUT_ENABLED ConfigProto_Experimental_MlirBridgeRollout = 1 + // Disabling the MLIR bridge disables it for all graphs in this session. + ConfigProto_Experimental_MLIR_BRIDGE_ROLLOUT_DISABLED ConfigProto_Experimental_MlirBridgeRollout = 2 +) + +var ConfigProto_Experimental_MlirBridgeRollout_name = map[int32]string{ + 0: "MLIR_BRIDGE_ROLLOUT_UNSPECIFIED", + 1: "MLIR_BRIDGE_ROLLOUT_ENABLED", + 2: "MLIR_BRIDGE_ROLLOUT_DISABLED", +} + +var ConfigProto_Experimental_MlirBridgeRollout_value = map[string]int32{ + "MLIR_BRIDGE_ROLLOUT_UNSPECIFIED": 0, + "MLIR_BRIDGE_ROLLOUT_ENABLED": 1, + "MLIR_BRIDGE_ROLLOUT_DISABLED": 2, +} + +func (x ConfigProto_Experimental_MlirBridgeRollout) String() string { + return proto.EnumName(ConfigProto_Experimental_MlirBridgeRollout_name, int32(x)) +} + +func (ConfigProto_Experimental_MlirBridgeRollout) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{6, 1, 0} +} + +// TODO(pbar) Turn this into a TraceOptions proto which allows +// tracing to be controlled in a more orthogonal manner? +type RunOptions_TraceLevel int32 + +const ( + RunOptions_NO_TRACE RunOptions_TraceLevel = 0 + RunOptions_SOFTWARE_TRACE RunOptions_TraceLevel = 1 + RunOptions_HARDWARE_TRACE RunOptions_TraceLevel = 2 + RunOptions_FULL_TRACE RunOptions_TraceLevel = 3 +) + +var RunOptions_TraceLevel_name = map[int32]string{ + 0: "NO_TRACE", + 1: "SOFTWARE_TRACE", + 2: "HARDWARE_TRACE", + 3: "FULL_TRACE", +} + +var RunOptions_TraceLevel_value = map[string]int32{ + "NO_TRACE": 0, + "SOFTWARE_TRACE": 1, + "HARDWARE_TRACE": 2, + "FULL_TRACE": 3, +} + +func (x RunOptions_TraceLevel) String() string { + return proto.EnumName(RunOptions_TraceLevel_name, int32(x)) +} + +func (RunOptions_TraceLevel) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{7, 0} +} + +type GPUOptions struct { + // Fraction of the available GPU memory to allocate for each process. + // 1 means to allocate all of the GPU memory, 0.5 means the process + // allocates up to ~50% of the available GPU memory. + // + // GPU memory is pre-allocated unless the allow_growth option is enabled. + // + // If greater than 1.0, uses CUDA unified memory to potentially oversubscribe + // the amount of memory available on the GPU device by using host memory as a + // swap space. Accessing memory not available on the device will be + // significantly slower as that would require memory transfer between the host + // and the device. Options to reduce the memory requirement should be + // considered before enabling this option as this may come with a negative + // performance impact. Oversubscription using the unified memory requires + // Pascal class or newer GPUs and it is currently only supported on the Linux + // operating system. See + // https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-requirements + // for the detailed requirements. + PerProcessGpuMemoryFraction float64 `protobuf:"fixed64,1,opt,name=per_process_gpu_memory_fraction,json=perProcessGpuMemoryFraction,proto3" json:"per_process_gpu_memory_fraction,omitempty"` + // If true, the allocator does not pre-allocate the entire specified + // GPU memory region, instead starting small and growing as needed. + AllowGrowth bool `protobuf:"varint,4,opt,name=allow_growth,json=allowGrowth,proto3" json:"allow_growth,omitempty"` + // The type of GPU allocation strategy to use. + // + // Allowed values: + // "": The empty string (default) uses a system-chosen default + // which may change over time. + // + // "BFC": A "Best-fit with coalescing" algorithm, simplified from a + // version of dlmalloc. + AllocatorType string `protobuf:"bytes,2,opt,name=allocator_type,json=allocatorType,proto3" json:"allocator_type,omitempty"` + // Delay deletion of up to this many bytes to reduce the number of + // interactions with gpu driver code. If 0, the system chooses + // a reasonable default (several MBs). + DeferredDeletionBytes int64 `protobuf:"varint,3,opt,name=deferred_deletion_bytes,json=deferredDeletionBytes,proto3" json:"deferred_deletion_bytes,omitempty"` + // A comma-separated list of GPU ids that determines the 'visible' + // to 'virtual' mapping of GPU devices. For example, if TensorFlow + // can see 8 GPU devices in the process, and one wanted to map + // visible GPU devices 5 and 3 as "/device:GPU:0", and "/device:GPU:1", + // then one would specify this field as "5,3". This field is similar in + // spirit to the CUDA_VISIBLE_DEVICES environment variable, except + // it applies to the visible GPU devices in the process. + // + // NOTE: + // 1. The GPU driver provides the process with the visible GPUs + // in an order which is not guaranteed to have any correlation to + // the *physical* GPU id in the machine. This field is used for + // remapping "visible" to "virtual", which means this operates only + // after the process starts. Users are required to use vendor + // specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the + // physical to visible device mapping prior to invoking TensorFlow. + // 2. In the code, the ids in this list are also called "platform GPU id"s, + // and the 'virtual' ids of GPU devices (i.e. the ids in the device + // name "/device:GPU:") are also called "TF GPU id"s. Please + // refer to third_party/tensorflow/core/common_runtime/gpu/gpu_id.h + // for more information. + VisibleDeviceList string `protobuf:"bytes,5,opt,name=visible_device_list,json=visibleDeviceList,proto3" json:"visible_device_list,omitempty"` + // In the event polling loop sleep this many microseconds between + // PollEvents calls, when the queue is not empty. If value is not + // set or set to 0, gets set to a non-zero default. + PollingActiveDelayUsecs int32 `protobuf:"varint,6,opt,name=polling_active_delay_usecs,json=pollingActiveDelayUsecs,proto3" json:"polling_active_delay_usecs,omitempty"` + // This field is deprecated and ignored. + PollingInactiveDelayMsecs int32 `protobuf:"varint,7,opt,name=polling_inactive_delay_msecs,json=pollingInactiveDelayMsecs,proto3" json:"polling_inactive_delay_msecs,omitempty"` + // Force all tensors to be gpu_compatible. On a GPU-enabled TensorFlow, + // enabling this option forces all CPU tensors to be allocated with Cuda + // pinned memory. Normally, TensorFlow will infer which tensors should be + // allocated as the pinned memory. But in case where the inference is + // incomplete, this option can significantly speed up the cross-device memory + // copy performance as long as it fits the memory. + // Note that this option is not something that should be + // enabled by default for unknown or very large models, since all Cuda pinned + // memory is unpageable, having too much pinned memory might negatively impact + // the overall host system performance. + ForceGpuCompatible bool `protobuf:"varint,8,opt,name=force_gpu_compatible,json=forceGpuCompatible,proto3" json:"force_gpu_compatible,omitempty"` + // Everything inside experimental is subject to change and is not subject + // to API stability guarantees in + // https://www.tensorflow.org/guide/version_compat. + Experimental *GPUOptions_Experimental `protobuf:"bytes,9,opt,name=experimental,proto3" json:"experimental,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GPUOptions) Reset() { *m = GPUOptions{} } +func (m *GPUOptions) String() string { return proto.CompactTextString(m) } +func (*GPUOptions) ProtoMessage() {} +func (*GPUOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{0} +} + +func (m *GPUOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GPUOptions.Unmarshal(m, b) +} +func (m *GPUOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GPUOptions.Marshal(b, m, deterministic) +} +func (m *GPUOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_GPUOptions.Merge(m, src) +} +func (m *GPUOptions) XXX_Size() int { + return xxx_messageInfo_GPUOptions.Size(m) +} +func (m *GPUOptions) XXX_DiscardUnknown() { + xxx_messageInfo_GPUOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_GPUOptions proto.InternalMessageInfo + +func (m *GPUOptions) GetPerProcessGpuMemoryFraction() float64 { + if m != nil { + return m.PerProcessGpuMemoryFraction + } + return 0 +} + +func (m *GPUOptions) GetAllowGrowth() bool { + if m != nil { + return m.AllowGrowth + } + return false +} + +func (m *GPUOptions) GetAllocatorType() string { + if m != nil { + return m.AllocatorType + } + return "" +} + +func (m *GPUOptions) GetDeferredDeletionBytes() int64 { + if m != nil { + return m.DeferredDeletionBytes + } + return 0 +} + +func (m *GPUOptions) GetVisibleDeviceList() string { + if m != nil { + return m.VisibleDeviceList + } + return "" +} + +func (m *GPUOptions) GetPollingActiveDelayUsecs() int32 { + if m != nil { + return m.PollingActiveDelayUsecs + } + return 0 +} + +func (m *GPUOptions) GetPollingInactiveDelayMsecs() int32 { + if m != nil { + return m.PollingInactiveDelayMsecs + } + return 0 +} + +func (m *GPUOptions) GetForceGpuCompatible() bool { + if m != nil { + return m.ForceGpuCompatible + } + return false +} + +func (m *GPUOptions) GetExperimental() *GPUOptions_Experimental { + if m != nil { + return m.Experimental + } + return nil +} + +type GPUOptions_Experimental struct { + // The multi virtual device settings. If empty (not set), it will create + // single virtual device on each visible GPU, according to the settings + // in "visible_device_list" above. Otherwise, the number of elements in the + // list must be the same as the number of visible GPUs (after + // "visible_device_list" filtering if it is set), and the string represented + // device names (e.g. /device:GPU:) will refer to the virtual + // devices and have the field assigned sequentially starting from 0, + // according to the order they appear in this list and the "memory_limit" + // list inside each element. For example, + // visible_device_list = "1,0" + // virtual_devices { memory_limit: 1GB memory_limit: 2GB } + // virtual_devices {} + // will create three virtual devices as: + // /device:GPU:0 -> visible GPU 1 with 1GB memory + // /device:GPU:1 -> visible GPU 1 with 2GB memory + // /device:GPU:2 -> visible GPU 0 with all available memory + // + // NOTE: + // 1. It's invalid to set both this and "per_process_gpu_memory_fraction" + // at the same time. + // 2. Currently this setting is per-process, not per-session. Using + // different settings in different sessions within same process will + // result in undefined behavior. + VirtualDevices []*GPUOptions_Experimental_VirtualDevices `protobuf:"bytes,1,rep,name=virtual_devices,json=virtualDevices,proto3" json:"virtual_devices,omitempty"` + // If true, uses CUDA unified memory for memory allocations. If + // per_process_gpu_memory_fraction option is greater than 1.0, then unified + // memory is used regardless of the value for this field. See comments for + // per_process_gpu_memory_fraction field for more details and requirements + // of the unified memory. This option is useful to oversubscribe memory if + // multiple processes are sharing a single GPU while individually using less + // than 1.0 per process memory fraction. + UseUnifiedMemory bool `protobuf:"varint,2,opt,name=use_unified_memory,json=useUnifiedMemory,proto3" json:"use_unified_memory,omitempty"` + // If > 1, the number of device-to-device copy streams to create + // for each GPUDevice. Default value is 0, which is automatically + // converted to 1. + NumDevToDevCopyStreams int32 `protobuf:"varint,3,opt,name=num_dev_to_dev_copy_streams,json=numDevToDevCopyStreams,proto3" json:"num_dev_to_dev_copy_streams,omitempty"` + // If non-empty, defines a good GPU ring order on a single worker based on + // device interconnect. This assumes that all workers have the same GPU + // topology. Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4". + // This ring order is used by the RingReducer implementation of + // CollectiveReduce, and serves as an override to automatic ring order + // generation in OrderTaskDeviceMap() during CollectiveParam resolution. + CollectiveRingOrder string `protobuf:"bytes,4,opt,name=collective_ring_order,json=collectiveRingOrder,proto3" json:"collective_ring_order,omitempty"` + // If true then extra work is done by GPUDevice and GPUBFCAllocator to + // keep track of when GPU memory is freed and when kernels actually + // complete so that we can know when a nominally free memory chunk + // is really not subject to pending use. + TimestampedAllocator bool `protobuf:"varint,5,opt,name=timestamped_allocator,json=timestampedAllocator,proto3" json:"timestamped_allocator,omitempty"` + // Parameters for GPUKernelTracker. By default no kernel tracking is done. + // Note that timestamped_allocator is only effective if some tracking is + // specified. + // + // If kernel_tracker_max_interval = n > 0, then a tracking event + // is inserted after every n kernels without an event. + KernelTrackerMaxInterval int32 `protobuf:"varint,7,opt,name=kernel_tracker_max_interval,json=kernelTrackerMaxInterval,proto3" json:"kernel_tracker_max_interval,omitempty"` + // If kernel_tracker_max_bytes = n > 0, then a tracking event is + // inserted after every series of kernels allocating a sum of + // memory >= n. If one kernel allocates b * n bytes, then one + // event will be inserted after it, but it will count as b against + // the pending limit. + KernelTrackerMaxBytes int32 `protobuf:"varint,8,opt,name=kernel_tracker_max_bytes,json=kernelTrackerMaxBytes,proto3" json:"kernel_tracker_max_bytes,omitempty"` + // If kernel_tracker_max_pending > 0 then no more than this many + // tracking events can be outstanding at a time. An attempt to + // launch an additional kernel will stall until an event + // completes. + KernelTrackerMaxPending int32 `protobuf:"varint,9,opt,name=kernel_tracker_max_pending,json=kernelTrackerMaxPending,proto3" json:"kernel_tracker_max_pending,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GPUOptions_Experimental) Reset() { *m = GPUOptions_Experimental{} } +func (m *GPUOptions_Experimental) String() string { return proto.CompactTextString(m) } +func (*GPUOptions_Experimental) ProtoMessage() {} +func (*GPUOptions_Experimental) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{0, 0} +} + +func (m *GPUOptions_Experimental) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GPUOptions_Experimental.Unmarshal(m, b) +} +func (m *GPUOptions_Experimental) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GPUOptions_Experimental.Marshal(b, m, deterministic) +} +func (m *GPUOptions_Experimental) XXX_Merge(src proto.Message) { + xxx_messageInfo_GPUOptions_Experimental.Merge(m, src) +} +func (m *GPUOptions_Experimental) XXX_Size() int { + return xxx_messageInfo_GPUOptions_Experimental.Size(m) +} +func (m *GPUOptions_Experimental) XXX_DiscardUnknown() { + xxx_messageInfo_GPUOptions_Experimental.DiscardUnknown(m) +} + +var xxx_messageInfo_GPUOptions_Experimental proto.InternalMessageInfo + +func (m *GPUOptions_Experimental) GetVirtualDevices() []*GPUOptions_Experimental_VirtualDevices { + if m != nil { + return m.VirtualDevices + } + return nil +} + +func (m *GPUOptions_Experimental) GetUseUnifiedMemory() bool { + if m != nil { + return m.UseUnifiedMemory + } + return false +} + +func (m *GPUOptions_Experimental) GetNumDevToDevCopyStreams() int32 { + if m != nil { + return m.NumDevToDevCopyStreams + } + return 0 +} + +func (m *GPUOptions_Experimental) GetCollectiveRingOrder() string { + if m != nil { + return m.CollectiveRingOrder + } + return "" +} + +func (m *GPUOptions_Experimental) GetTimestampedAllocator() bool { + if m != nil { + return m.TimestampedAllocator + } + return false +} + +func (m *GPUOptions_Experimental) GetKernelTrackerMaxInterval() int32 { + if m != nil { + return m.KernelTrackerMaxInterval + } + return 0 +} + +func (m *GPUOptions_Experimental) GetKernelTrackerMaxBytes() int32 { + if m != nil { + return m.KernelTrackerMaxBytes + } + return 0 +} + +func (m *GPUOptions_Experimental) GetKernelTrackerMaxPending() int32 { + if m != nil { + return m.KernelTrackerMaxPending + } + return 0 +} + +// Configuration for breaking down a visible GPU into multiple "virtual" +// devices. +type GPUOptions_Experimental_VirtualDevices struct { + // Per "virtual" device memory limit, in MB. The number of elements in + // the list is the number of virtual devices to create on the + // corresponding visible GPU (see "virtual_devices" below). + // If empty, it will create single virtual device taking all available + // memory from the device. + // + // For the concept of "visible" and "virtual" GPU, see the comments for + // "visible_device_list" above for more information. + MemoryLimitMb []float32 `protobuf:"fixed32,1,rep,packed,name=memory_limit_mb,json=memoryLimitMb,proto3" json:"memory_limit_mb,omitempty"` + // Priority values to use with the virtual devices. Use the cuda function + // cudaDeviceGetStreamPriorityRange to query for valid range of values for + // priority. + // + // On a P4000 GPU with cuda 10.1, the priority range reported was 0 for + // least priority and -1 for greatest priority. + // + // If this field is not specified, then the virtual devices will be + // created with the default. If this field has values set, then the size + // of this must match with the above memory_limit_mb. + Priority []int32 `protobuf:"varint,2,rep,packed,name=priority,proto3" json:"priority,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GPUOptions_Experimental_VirtualDevices) Reset() { + *m = GPUOptions_Experimental_VirtualDevices{} +} +func (m *GPUOptions_Experimental_VirtualDevices) String() string { return proto.CompactTextString(m) } +func (*GPUOptions_Experimental_VirtualDevices) ProtoMessage() {} +func (*GPUOptions_Experimental_VirtualDevices) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{0, 0, 0} +} + +func (m *GPUOptions_Experimental_VirtualDevices) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GPUOptions_Experimental_VirtualDevices.Unmarshal(m, b) +} +func (m *GPUOptions_Experimental_VirtualDevices) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GPUOptions_Experimental_VirtualDevices.Marshal(b, m, deterministic) +} +func (m *GPUOptions_Experimental_VirtualDevices) XXX_Merge(src proto.Message) { + xxx_messageInfo_GPUOptions_Experimental_VirtualDevices.Merge(m, src) +} +func (m *GPUOptions_Experimental_VirtualDevices) XXX_Size() int { + return xxx_messageInfo_GPUOptions_Experimental_VirtualDevices.Size(m) +} +func (m *GPUOptions_Experimental_VirtualDevices) XXX_DiscardUnknown() { + xxx_messageInfo_GPUOptions_Experimental_VirtualDevices.DiscardUnknown(m) +} + +var xxx_messageInfo_GPUOptions_Experimental_VirtualDevices proto.InternalMessageInfo + +func (m *GPUOptions_Experimental_VirtualDevices) GetMemoryLimitMb() []float32 { + if m != nil { + return m.MemoryLimitMb + } + return nil +} + +func (m *GPUOptions_Experimental_VirtualDevices) GetPriority() []int32 { + if m != nil { + return m.Priority + } + return nil +} + +// Options passed to the graph optimizer +type OptimizerOptions struct { + // If true, optimize the graph using common subexpression elimination. + DoCommonSubexpressionElimination bool `protobuf:"varint,1,opt,name=do_common_subexpression_elimination,json=doCommonSubexpressionElimination,proto3" json:"do_common_subexpression_elimination,omitempty"` + // If true, perform constant folding optimization on the graph. + DoConstantFolding bool `protobuf:"varint,2,opt,name=do_constant_folding,json=doConstantFolding,proto3" json:"do_constant_folding,omitempty"` + // Constant folding optimization replaces tensors whose values can be + // predetermined, with constant nodes. To avoid inserting too large constants, + // the size of each constant created can be limited. If this value is zero, a + // default limit of 10 MiB will be applied. If constant folding optimization + // is disabled, this value is ignored. + MaxFoldedConstantInBytes int64 `protobuf:"varint,6,opt,name=max_folded_constant_in_bytes,json=maxFoldedConstantInBytes,proto3" json:"max_folded_constant_in_bytes,omitempty"` + // If true, perform function inlining on the graph. + DoFunctionInlining bool `protobuf:"varint,4,opt,name=do_function_inlining,json=doFunctionInlining,proto3" json:"do_function_inlining,omitempty"` + // Overall optimization level. The actual optimizations applied will be the + // logical OR of the flags that this level implies and any flags already set. + OptLevel OptimizerOptions_Level `protobuf:"varint,3,opt,name=opt_level,json=optLevel,proto3,enum=tensorflow.OptimizerOptions_Level" json:"opt_level,omitempty"` + GlobalJitLevel OptimizerOptions_GlobalJitLevel `protobuf:"varint,5,opt,name=global_jit_level,json=globalJitLevel,proto3,enum=tensorflow.OptimizerOptions_GlobalJitLevel" json:"global_jit_level,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *OptimizerOptions) Reset() { *m = OptimizerOptions{} } +func (m *OptimizerOptions) String() string { return proto.CompactTextString(m) } +func (*OptimizerOptions) ProtoMessage() {} +func (*OptimizerOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{1} +} + +func (m *OptimizerOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_OptimizerOptions.Unmarshal(m, b) +} +func (m *OptimizerOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_OptimizerOptions.Marshal(b, m, deterministic) +} +func (m *OptimizerOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_OptimizerOptions.Merge(m, src) +} +func (m *OptimizerOptions) XXX_Size() int { + return xxx_messageInfo_OptimizerOptions.Size(m) +} +func (m *OptimizerOptions) XXX_DiscardUnknown() { + xxx_messageInfo_OptimizerOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_OptimizerOptions proto.InternalMessageInfo + +func (m *OptimizerOptions) GetDoCommonSubexpressionElimination() bool { + if m != nil { + return m.DoCommonSubexpressionElimination + } + return false +} + +func (m *OptimizerOptions) GetDoConstantFolding() bool { + if m != nil { + return m.DoConstantFolding + } + return false +} + +func (m *OptimizerOptions) GetMaxFoldedConstantInBytes() int64 { + if m != nil { + return m.MaxFoldedConstantInBytes + } + return 0 +} + +func (m *OptimizerOptions) GetDoFunctionInlining() bool { + if m != nil { + return m.DoFunctionInlining + } + return false +} + +func (m *OptimizerOptions) GetOptLevel() OptimizerOptions_Level { + if m != nil { + return m.OptLevel + } + return OptimizerOptions_L1 +} + +func (m *OptimizerOptions) GetGlobalJitLevel() OptimizerOptions_GlobalJitLevel { + if m != nil { + return m.GlobalJitLevel + } + return OptimizerOptions_DEFAULT +} + +type GraphOptions struct { + // If true, use control flow to schedule the activation of Recv nodes. + // (Currently ignored.) + EnableRecvScheduling bool `protobuf:"varint,2,opt,name=enable_recv_scheduling,json=enableRecvScheduling,proto3" json:"enable_recv_scheduling,omitempty"` + // Options controlling how graph is optimized. + OptimizerOptions *OptimizerOptions `protobuf:"bytes,3,opt,name=optimizer_options,json=optimizerOptions,proto3" json:"optimizer_options,omitempty"` + // The number of steps to run before returning a cost model detailing + // the memory usage and performance of each node of the graph. 0 means + // no cost model. + BuildCostModel int64 `protobuf:"varint,4,opt,name=build_cost_model,json=buildCostModel,proto3" json:"build_cost_model,omitempty"` + // The number of steps to skip before collecting statistics for the + // cost model. + BuildCostModelAfter int64 `protobuf:"varint,9,opt,name=build_cost_model_after,json=buildCostModelAfter,proto3" json:"build_cost_model_after,omitempty"` + // Annotate each Node with Op output shape data, to the extent it can + // be statically inferred. + InferShapes bool `protobuf:"varint,5,opt,name=infer_shapes,json=inferShapes,proto3" json:"infer_shapes,omitempty"` + // Only place the subgraphs that are run, rather than the entire graph. + // + // This is useful for interactive graph building, where one might + // produce graphs that cannot be placed during the debugging + // process. In particular, it allows the client to continue work in + // a session after adding a node to a graph whose placement + // constraints are unsatisfiable. + PlacePrunedGraph bool `protobuf:"varint,6,opt,name=place_pruned_graph,json=placePrunedGraph,proto3" json:"place_pruned_graph,omitempty"` + // If true, transfer float values between processes as bfloat16. + EnableBfloat16Sendrecv bool `protobuf:"varint,7,opt,name=enable_bfloat16_sendrecv,json=enableBfloat16Sendrecv,proto3" json:"enable_bfloat16_sendrecv,omitempty"` + // If > 0, record a timeline every this many steps. + // EXPERIMENTAL: This currently has no effect in MasterSession. + TimelineStep int32 `protobuf:"varint,8,opt,name=timeline_step,json=timelineStep,proto3" json:"timeline_step,omitempty"` + // Options that control the type and amount of graph rewriting. + // Not currently configurable via the public Python API (i.e. there is no API + // stability guarantee if you import RewriterConfig explicitly). + RewriteOptions *RewriterConfig `protobuf:"bytes,10,opt,name=rewrite_options,json=rewriteOptions,proto3" json:"rewrite_options,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphOptions) Reset() { *m = GraphOptions{} } +func (m *GraphOptions) String() string { return proto.CompactTextString(m) } +func (*GraphOptions) ProtoMessage() {} +func (*GraphOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{2} +} + +func (m *GraphOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphOptions.Unmarshal(m, b) +} +func (m *GraphOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphOptions.Marshal(b, m, deterministic) +} +func (m *GraphOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphOptions.Merge(m, src) +} +func (m *GraphOptions) XXX_Size() int { + return xxx_messageInfo_GraphOptions.Size(m) +} +func (m *GraphOptions) XXX_DiscardUnknown() { + xxx_messageInfo_GraphOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphOptions proto.InternalMessageInfo + +func (m *GraphOptions) GetEnableRecvScheduling() bool { + if m != nil { + return m.EnableRecvScheduling + } + return false +} + +func (m *GraphOptions) GetOptimizerOptions() *OptimizerOptions { + if m != nil { + return m.OptimizerOptions + } + return nil +} + +func (m *GraphOptions) GetBuildCostModel() int64 { + if m != nil { + return m.BuildCostModel + } + return 0 +} + +func (m *GraphOptions) GetBuildCostModelAfter() int64 { + if m != nil { + return m.BuildCostModelAfter + } + return 0 +} + +func (m *GraphOptions) GetInferShapes() bool { + if m != nil { + return m.InferShapes + } + return false +} + +func (m *GraphOptions) GetPlacePrunedGraph() bool { + if m != nil { + return m.PlacePrunedGraph + } + return false +} + +func (m *GraphOptions) GetEnableBfloat16Sendrecv() bool { + if m != nil { + return m.EnableBfloat16Sendrecv + } + return false +} + +func (m *GraphOptions) GetTimelineStep() int32 { + if m != nil { + return m.TimelineStep + } + return 0 +} + +func (m *GraphOptions) GetRewriteOptions() *RewriterConfig { + if m != nil { + return m.RewriteOptions + } + return nil +} + +type ThreadPoolOptionProto struct { + // The number of threads in the pool. + // + // 0 means the system picks a value based on where this option proto is used + // (see the declaration of the specific field for more info). + NumThreads int32 `protobuf:"varint,1,opt,name=num_threads,json=numThreads,proto3" json:"num_threads,omitempty"` + // The global name of the threadpool. + // + // If empty, then the threadpool is made and used according to the scope it's + // in - e.g., for a session threadpool, it is used by that session only. + // + // If non-empty, then: + // - a global threadpool associated with this name is looked + // up or created. This allows, for example, sharing one threadpool across + // many sessions (e.g., like the default behavior, if + // inter_op_parallelism_threads is not configured), but still partitioning + // into a large and small pool. + // - if the threadpool for this global_name already exists, then it is an + // error if the existing pool was created using a different num_threads + // value as is specified on this call. + // - threadpools created this way are never garbage collected. + GlobalName string `protobuf:"bytes,2,opt,name=global_name,json=globalName,proto3" json:"global_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ThreadPoolOptionProto) Reset() { *m = ThreadPoolOptionProto{} } +func (m *ThreadPoolOptionProto) String() string { return proto.CompactTextString(m) } +func (*ThreadPoolOptionProto) ProtoMessage() {} +func (*ThreadPoolOptionProto) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{3} +} + +func (m *ThreadPoolOptionProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ThreadPoolOptionProto.Unmarshal(m, b) +} +func (m *ThreadPoolOptionProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ThreadPoolOptionProto.Marshal(b, m, deterministic) +} +func (m *ThreadPoolOptionProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_ThreadPoolOptionProto.Merge(m, src) +} +func (m *ThreadPoolOptionProto) XXX_Size() int { + return xxx_messageInfo_ThreadPoolOptionProto.Size(m) +} +func (m *ThreadPoolOptionProto) XXX_DiscardUnknown() { + xxx_messageInfo_ThreadPoolOptionProto.DiscardUnknown(m) +} + +var xxx_messageInfo_ThreadPoolOptionProto proto.InternalMessageInfo + +func (m *ThreadPoolOptionProto) GetNumThreads() int32 { + if m != nil { + return m.NumThreads + } + return 0 +} + +func (m *ThreadPoolOptionProto) GetGlobalName() string { + if m != nil { + return m.GlobalName + } + return "" +} + +type RPCOptions struct { + // If true, always use RPC to contact the session target. + // + // If false (the default option), TensorFlow may use an optimized + // transport for client-master communication that avoids the RPC + // stack. This option is primarily for used testing the RPC stack. + UseRpcForInprocessMaster bool `protobuf:"varint,1,opt,name=use_rpc_for_inprocess_master,json=useRpcForInprocessMaster,proto3" json:"use_rpc_for_inprocess_master,omitempty"` + // The compression algorithm to be used. One of "deflate", "gzip". + CompressionAlgorithm string `protobuf:"bytes,2,opt,name=compression_algorithm,json=compressionAlgorithm,proto3" json:"compression_algorithm,omitempty"` + // If compression_algorithm is set, the compression level to be used. + // From 0 (no compression), up to 3. + CompressionLevel int32 `protobuf:"varint,3,opt,name=compression_level,json=compressionLevel,proto3" json:"compression_level,omitempty"` + // Setting cache_rpc_response to true will enable sender side caching of + // response for RecvTensorAsync and RecvBufAsync to allow receiver to retry + // requests . This is only necessary when the network fabric is experiencing a + // significant error rate. Without it we'll fail a step on an network error, + // while with it we'll be able to complete long steps (like complex + // initializations) in the face of some network errors during RecvTensor. + CacheRpcResponse bool `protobuf:"varint,4,opt,name=cache_rpc_response,json=cacheRpcResponse,proto3" json:"cache_rpc_response,omitempty"` + // Disables TCP connection sharing when opening a new RPC channel. + DisableSessionConnectionSharing bool `protobuf:"varint,5,opt,name=disable_session_connection_sharing,json=disableSessionConnectionSharing,proto3" json:"disable_session_connection_sharing,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RPCOptions) Reset() { *m = RPCOptions{} } +func (m *RPCOptions) String() string { return proto.CompactTextString(m) } +func (*RPCOptions) ProtoMessage() {} +func (*RPCOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{4} +} + +func (m *RPCOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RPCOptions.Unmarshal(m, b) +} +func (m *RPCOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RPCOptions.Marshal(b, m, deterministic) +} +func (m *RPCOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_RPCOptions.Merge(m, src) +} +func (m *RPCOptions) XXX_Size() int { + return xxx_messageInfo_RPCOptions.Size(m) +} +func (m *RPCOptions) XXX_DiscardUnknown() { + xxx_messageInfo_RPCOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_RPCOptions proto.InternalMessageInfo + +func (m *RPCOptions) GetUseRpcForInprocessMaster() bool { + if m != nil { + return m.UseRpcForInprocessMaster + } + return false +} + +func (m *RPCOptions) GetCompressionAlgorithm() string { + if m != nil { + return m.CompressionAlgorithm + } + return "" +} + +func (m *RPCOptions) GetCompressionLevel() int32 { + if m != nil { + return m.CompressionLevel + } + return 0 +} + +func (m *RPCOptions) GetCacheRpcResponse() bool { + if m != nil { + return m.CacheRpcResponse + } + return false +} + +func (m *RPCOptions) GetDisableSessionConnectionSharing() bool { + if m != nil { + return m.DisableSessionConnectionSharing + } + return false +} + +// Metadata about the session. +// +// This can be used by the runtime and the Ops for debugging, monitoring, etc. +// +// The (name, version) tuple is expected to be a unique identifier for +// sessions within the same process. +// +// NOTE: This is currently used and propagated only by the direct session. +type SessionMetadata struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // The version is optional. If set, needs to be >= 0. + Version int64 `protobuf:"varint,2,opt,name=version,proto3" json:"version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SessionMetadata) Reset() { *m = SessionMetadata{} } +func (m *SessionMetadata) String() string { return proto.CompactTextString(m) } +func (*SessionMetadata) ProtoMessage() {} +func (*SessionMetadata) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{5} +} + +func (m *SessionMetadata) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SessionMetadata.Unmarshal(m, b) +} +func (m *SessionMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SessionMetadata.Marshal(b, m, deterministic) +} +func (m *SessionMetadata) XXX_Merge(src proto.Message) { + xxx_messageInfo_SessionMetadata.Merge(m, src) +} +func (m *SessionMetadata) XXX_Size() int { + return xxx_messageInfo_SessionMetadata.Size(m) +} +func (m *SessionMetadata) XXX_DiscardUnknown() { + xxx_messageInfo_SessionMetadata.DiscardUnknown(m) +} + +var xxx_messageInfo_SessionMetadata proto.InternalMessageInfo + +func (m *SessionMetadata) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *SessionMetadata) GetVersion() int64 { + if m != nil { + return m.Version + } + return 0 +} + +// Session configuration parameters. +// The system picks appropriate values for fields that are not set. +type ConfigProto struct { + // Map from device type name (e.g., "CPU" or "GPU" ) to maximum + // number of devices of that type to use. If a particular device + // type is not found in the map, the system picks an appropriate + // number. + DeviceCount map[string]int32 `protobuf:"bytes,1,rep,name=device_count,json=deviceCount,proto3" json:"device_count,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"varint,2,opt,name=value,proto3"` + // The execution of an individual op (for some op types) can be + // parallelized on a pool of intra_op_parallelism_threads. + // 0 means the system picks an appropriate number. + // + // If you create an ordinary session, e.g., from Python or C++, + // then there is exactly one intra op thread pool per process. + // The first session created determines the number of threads in this pool. + // All subsequent sessions reuse/share this one global pool. + // + // There are notable exceptions to the default behavior describe above: + // 1. There is an environment variable for overriding this thread pool, + // named TF_OVERRIDE_GLOBAL_THREADPOOL. + // 2. When connecting to a server, such as a remote `tf.train.Server` + // instance, then this option will be ignored altogether. + IntraOpParallelismThreads int32 `protobuf:"varint,2,opt,name=intra_op_parallelism_threads,json=intraOpParallelismThreads,proto3" json:"intra_op_parallelism_threads,omitempty"` + // Nodes that perform blocking operations are enqueued on a pool of + // inter_op_parallelism_threads available in each process. + // + // 0 means the system picks an appropriate number. + // Negative means all operations are performed in caller's thread. + // + // Note that the first Session created in the process sets the + // number of threads for all future sessions unless use_per_session_threads is + // true or session_inter_op_thread_pool is configured. + InterOpParallelismThreads int32 `protobuf:"varint,5,opt,name=inter_op_parallelism_threads,json=interOpParallelismThreads,proto3" json:"inter_op_parallelism_threads,omitempty"` + // If true, use a new set of threads for this session rather than the global + // pool of threads. Only supported by direct sessions. + // + // If false, use the global threads created by the first session, or the + // per-session thread pools configured by session_inter_op_thread_pool. + // + // This option is deprecated. The same effect can be achieved by setting + // session_inter_op_thread_pool to have one element, whose num_threads equals + // inter_op_parallelism_threads. + UsePerSessionThreads bool `protobuf:"varint,9,opt,name=use_per_session_threads,json=usePerSessionThreads,proto3" json:"use_per_session_threads,omitempty"` + // This option is experimental - it may be replaced with a different mechanism + // in the future. + // + // Configures session thread pools. If this is configured, then RunOptions for + // a Run call can select the thread pool to use. + // + // The intended use is for when some session invocations need to run in a + // background pool limited to a small number of threads: + // - For example, a session may be configured to have one large pool (for + // regular compute) and one small pool (for periodic, low priority work); + // using the small pool is currently the mechanism for limiting the inter-op + // parallelism of the low priority work. Note that it does not limit the + // parallelism of work spawned by a single op kernel implementation. + // - Using this setting is normally not needed in training, but may help some + // serving use cases. + // - It is also generally recommended to set the global_name field of this + // proto, to avoid creating multiple large pools. It is typically better to + // run the non-low-priority work, even across sessions, in a single large + // pool. + SessionInterOpThreadPool []*ThreadPoolOptionProto `protobuf:"bytes,12,rep,name=session_inter_op_thread_pool,json=sessionInterOpThreadPool,proto3" json:"session_inter_op_thread_pool,omitempty"` + // Assignment of Nodes to Devices is recomputed every placement_period + // steps until the system warms up (at which point the recomputation + // typically slows down automatically). + PlacementPeriod int32 `protobuf:"varint,3,opt,name=placement_period,json=placementPeriod,proto3" json:"placement_period,omitempty"` + // When any filters are present sessions will ignore all devices which do not + // match the filters. Each filter can be partially specified, e.g. "/job:ps" + // "/job:worker/replica:3", etc. + DeviceFilters []string `protobuf:"bytes,4,rep,name=device_filters,json=deviceFilters,proto3" json:"device_filters,omitempty"` + // Options that apply to all GPUs. + GpuOptions *GPUOptions `protobuf:"bytes,6,opt,name=gpu_options,json=gpuOptions,proto3" json:"gpu_options,omitempty"` + // Whether soft placement is allowed. If allow_soft_placement is true, + // an op will be placed on CPU if + // 1. there's no GPU implementation for the OP + // or + // 2. no GPU devices are known or registered + // or + // 3. need to co-locate with reftype input(s) which are from CPU. + AllowSoftPlacement bool `protobuf:"varint,7,opt,name=allow_soft_placement,json=allowSoftPlacement,proto3" json:"allow_soft_placement,omitempty"` + // Whether device placements should be logged. + LogDevicePlacement bool `protobuf:"varint,8,opt,name=log_device_placement,json=logDevicePlacement,proto3" json:"log_device_placement,omitempty"` + // Options that apply to all graphs. + GraphOptions *GraphOptions `protobuf:"bytes,10,opt,name=graph_options,json=graphOptions,proto3" json:"graph_options,omitempty"` + // Global timeout for all blocking operations in this session. If non-zero, + // and not overridden on a per-operation basis, this value will be used as the + // deadline for all blocking operations. + OperationTimeoutInMs int64 `protobuf:"varint,11,opt,name=operation_timeout_in_ms,json=operationTimeoutInMs,proto3" json:"operation_timeout_in_ms,omitempty"` + // Options that apply when this session uses the distributed runtime. + RpcOptions *RPCOptions `protobuf:"bytes,13,opt,name=rpc_options,json=rpcOptions,proto3" json:"rpc_options,omitempty"` + // Optional list of all workers to use in this session. + ClusterDef *ClusterDef `protobuf:"bytes,14,opt,name=cluster_def,json=clusterDef,proto3" json:"cluster_def,omitempty"` + // If true, any resources such as Variables used in the session will not be + // shared with other sessions. However, when clusterspec propagation is + // enabled, this field is ignored and sessions are always isolated. + IsolateSessionState bool `protobuf:"varint,15,opt,name=isolate_session_state,json=isolateSessionState,proto3" json:"isolate_session_state,omitempty"` + // When true, WorkerSessions are created with device attributes from the + // full cluster. + // This is helpful when a worker wants to partition a graph + // (for example during a PartitionedCallOp). + ShareClusterDevicesInSession bool `protobuf:"varint,17,opt,name=share_cluster_devices_in_session,json=shareClusterDevicesInSession,proto3" json:"share_cluster_devices_in_session,omitempty"` + Experimental *ConfigProto_Experimental `protobuf:"bytes,16,opt,name=experimental,proto3" json:"experimental,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ConfigProto) Reset() { *m = ConfigProto{} } +func (m *ConfigProto) String() string { return proto.CompactTextString(m) } +func (*ConfigProto) ProtoMessage() {} +func (*ConfigProto) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{6} +} + +func (m *ConfigProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ConfigProto.Unmarshal(m, b) +} +func (m *ConfigProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ConfigProto.Marshal(b, m, deterministic) +} +func (m *ConfigProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_ConfigProto.Merge(m, src) +} +func (m *ConfigProto) XXX_Size() int { + return xxx_messageInfo_ConfigProto.Size(m) +} +func (m *ConfigProto) XXX_DiscardUnknown() { + xxx_messageInfo_ConfigProto.DiscardUnknown(m) +} + +var xxx_messageInfo_ConfigProto proto.InternalMessageInfo + +func (m *ConfigProto) GetDeviceCount() map[string]int32 { + if m != nil { + return m.DeviceCount + } + return nil +} + +func (m *ConfigProto) GetIntraOpParallelismThreads() int32 { + if m != nil { + return m.IntraOpParallelismThreads + } + return 0 +} + +func (m *ConfigProto) GetInterOpParallelismThreads() int32 { + if m != nil { + return m.InterOpParallelismThreads + } + return 0 +} + +func (m *ConfigProto) GetUsePerSessionThreads() bool { + if m != nil { + return m.UsePerSessionThreads + } + return false +} + +func (m *ConfigProto) GetSessionInterOpThreadPool() []*ThreadPoolOptionProto { + if m != nil { + return m.SessionInterOpThreadPool + } + return nil +} + +func (m *ConfigProto) GetPlacementPeriod() int32 { + if m != nil { + return m.PlacementPeriod + } + return 0 +} + +func (m *ConfigProto) GetDeviceFilters() []string { + if m != nil { + return m.DeviceFilters + } + return nil +} + +func (m *ConfigProto) GetGpuOptions() *GPUOptions { + if m != nil { + return m.GpuOptions + } + return nil +} + +func (m *ConfigProto) GetAllowSoftPlacement() bool { + if m != nil { + return m.AllowSoftPlacement + } + return false +} + +func (m *ConfigProto) GetLogDevicePlacement() bool { + if m != nil { + return m.LogDevicePlacement + } + return false +} + +func (m *ConfigProto) GetGraphOptions() *GraphOptions { + if m != nil { + return m.GraphOptions + } + return nil +} + +func (m *ConfigProto) GetOperationTimeoutInMs() int64 { + if m != nil { + return m.OperationTimeoutInMs + } + return 0 +} + +func (m *ConfigProto) GetRpcOptions() *RPCOptions { + if m != nil { + return m.RpcOptions + } + return nil +} + +func (m *ConfigProto) GetClusterDef() *ClusterDef { + if m != nil { + return m.ClusterDef + } + return nil +} + +func (m *ConfigProto) GetIsolateSessionState() bool { + if m != nil { + return m.IsolateSessionState + } + return false +} + +func (m *ConfigProto) GetShareClusterDevicesInSession() bool { + if m != nil { + return m.ShareClusterDevicesInSession + } + return false +} + +func (m *ConfigProto) GetExperimental() *ConfigProto_Experimental { + if m != nil { + return m.Experimental + } + return nil +} + +// Everything inside Experimental is subject to change and is not subject +// to API stability guarantees in +// https://www.tensorflow.org/guide/version_compat. +type ConfigProto_Experimental struct { + // Task name for group resolution. + CollectiveGroupLeader string `protobuf:"bytes,1,opt,name=collective_group_leader,json=collectiveGroupLeader,proto3" json:"collective_group_leader,omitempty"` + // Which executor to use, the default executor will be used + // if it is an empty string or "DEFAULT" + ExecutorType string `protobuf:"bytes,3,opt,name=executor_type,json=executorType,proto3" json:"executor_type,omitempty"` + // Guidance to formatting of large RecvBuf fields for transfer. + // Any positive value sets the max chunk size. 0 defaults to 4096. + // Any negative value indicates no max, i.e. one chunk only. + RecvBufMaxChunk int32 `protobuf:"varint,4,opt,name=recv_buf_max_chunk,json=recvBufMaxChunk,proto3" json:"recv_buf_max_chunk,omitempty"` + // If true, and supported by the platform, the runtime will attempt to + // use NUMA affinity where applicable. One consequence will be the + // existence of as many CPU devices as there are available NUMA nodes. + UseNumaAffinity bool `protobuf:"varint,5,opt,name=use_numa_affinity,json=useNumaAffinity,proto3" json:"use_numa_affinity,omitempty"` + // If true, make collective op execution order sequential and deterministic + // for potentially concurrent collective instances. + CollectiveDeterministicSequentialExecution bool `protobuf:"varint,6,opt,name=collective_deterministic_sequential_execution,json=collectiveDeterministicSequentialExecution,proto3" json:"collective_deterministic_sequential_execution,omitempty"` + // If true, use NCCL for CollectiveOps. This feature is highly + // experimental. + CollectiveNccl bool `protobuf:"varint,7,opt,name=collective_nccl,json=collectiveNccl,proto3" json:"collective_nccl,omitempty"` + // In the following, session state means the value of a variable, elements + // in a hash table, or any other resource, accessible by worker sessions + // held by a TF server. + // + // When ClusterSpec propagation is enabled, the value of + // isolate_session_state is ignored when deciding whether to share session + // states in a TF server (for backwards compatibility reasons). + // - If share_session_state_in_clusterspec_propagation is true, the session + // states are shared. + // - If share_session_state_in_clusterspec_propagation is false, session + // states are isolated. + // + // When clusterspec propagation is not used, the value of + // share_session_state_in_clusterspec_propagation is ignored when deciding + // whether to share session states in a TF server. + // - If isolate_session_state is true, session states are isolated. + // - If isolate_session_state is false, session states are shared. + // + // TODO(b/129330037): Add a single API that consistently treats + // isolate_session_state and ClusterSpec propagation. + ShareSessionStateInClusterspecPropagation bool `protobuf:"varint,8,opt,name=share_session_state_in_clusterspec_propagation,json=shareSessionStateInClusterspecPropagation,proto3" json:"share_session_state_in_clusterspec_propagation,omitempty"` + // If using a direct session, disable spinning while waiting for work in + // the thread pool. This may result in higher latency for completing ops, + // but in the case where there is a lot of spinning may result in lower + // CPU usage. + DisableThreadSpinning bool `protobuf:"varint,9,opt,name=disable_thread_spinning,json=disableThreadSpinning,proto3" json:"disable_thread_spinning,omitempty"` + // This was promoted to a non-experimental API. Please use + // ConfigProto.share_cluster_devices_in_session instead. + ShareClusterDevicesInSession bool `protobuf:"varint,10,opt,name=share_cluster_devices_in_session,json=shareClusterDevicesInSession,proto3" json:"share_cluster_devices_in_session,omitempty"` + // Metadata about the session. + // + // If set, this can be used by the runtime and the Ops for debugging, + // monitoring, etc. + // + // NOTE: This is currently used and propagated only by the direct session. + SessionMetadata *SessionMetadata `protobuf:"bytes,11,opt,name=session_metadata,json=sessionMetadata,proto3" json:"session_metadata,omitempty"` + // If true, the session may treat the graph as being static for optimization + // purposes. + // + // If this option is set to true when a session is created, the full + // GraphDef must be passed in a single call to Session::Create(), and + // Session::Extend() may not be supported. + OptimizeForStaticGraph bool `protobuf:"varint,12,opt,name=optimize_for_static_graph,json=optimizeForStaticGraph,proto3" json:"optimize_for_static_graph,omitempty"` + // This field will eventually be deprecated and replaced by + // mlir_bridge_rollout (b/166038521). + // + // Whether to enable the MLIR-based TF->XLA bridge. + // + // This is a replacement to the existing bridge, and not ready for + // production usage yet. + // If this option is set to true when a session is created, MLIR is used to + // perform the set of graph transformations to put the graph in a form that + // can be executed with delegation of some computations to an accelerator. + // This builds on the model of XLA where a subset of the graph is + // encapsulated and attached to a "compile" operation, whose result is fed + // to an "execute" operation. The kernel for these operations is responsible + // to lower the encapsulated graph to a particular device. + EnableMlirBridge bool `protobuf:"varint,13,opt,name=enable_mlir_bridge,json=enableMlirBridge,proto3" json:"enable_mlir_bridge,omitempty"` + // This field is underdevelopment, for now use enable_mlir_bridge + // (b/166038521). + // + // Whether to enable the MLIR-based TF->XLA bridge. + MlirBridgeRollout ConfigProto_Experimental_MlirBridgeRollout `protobuf:"varint,17,opt,name=mlir_bridge_rollout,json=mlirBridgeRollout,proto3,enum=tensorflow.ConfigProto_Experimental_MlirBridgeRollout" json:"mlir_bridge_rollout,omitempty"` + // Whether to enable the MLIR-based Graph optimizations. + // + // This will become a part of standard Tensorflow graph optimization + // pipeline, currently this is only used for gradual migration and testing + // new passes that are replacing existing optimizations in Grappler. + EnableMlirGraphOptimization bool `protobuf:"varint,16,opt,name=enable_mlir_graph_optimization,json=enableMlirGraphOptimization,proto3" json:"enable_mlir_graph_optimization,omitempty"` + // If true, the session will not store an additional copy of the graph for + // each subgraph. + // + // If this option is set to true when a session is created, the + // `RunOptions.output_partition_graphs` options must not be set. + DisableOutputPartitionGraphs bool `protobuf:"varint,14,opt,name=disable_output_partition_graphs,json=disableOutputPartitionGraphs,proto3" json:"disable_output_partition_graphs,omitempty"` + // Minimum number of batches run through the XLA graph before XLA fusion + // autotuner is enabled. Default value of zero disables the autotuner. + // + // The XLA fusion autotuner can improve performance by executing a heuristic + // search on the compiler parameters. + XlaFusionAutotunerThresh int64 `protobuf:"varint,15,opt,name=xla_fusion_autotuner_thresh,json=xlaFusionAutotunerThresh,proto3" json:"xla_fusion_autotuner_thresh,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ConfigProto_Experimental) Reset() { *m = ConfigProto_Experimental{} } +func (m *ConfigProto_Experimental) String() string { return proto.CompactTextString(m) } +func (*ConfigProto_Experimental) ProtoMessage() {} +func (*ConfigProto_Experimental) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{6, 1} +} + +func (m *ConfigProto_Experimental) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ConfigProto_Experimental.Unmarshal(m, b) +} +func (m *ConfigProto_Experimental) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ConfigProto_Experimental.Marshal(b, m, deterministic) +} +func (m *ConfigProto_Experimental) XXX_Merge(src proto.Message) { + xxx_messageInfo_ConfigProto_Experimental.Merge(m, src) +} +func (m *ConfigProto_Experimental) XXX_Size() int { + return xxx_messageInfo_ConfigProto_Experimental.Size(m) +} +func (m *ConfigProto_Experimental) XXX_DiscardUnknown() { + xxx_messageInfo_ConfigProto_Experimental.DiscardUnknown(m) +} + +var xxx_messageInfo_ConfigProto_Experimental proto.InternalMessageInfo + +func (m *ConfigProto_Experimental) GetCollectiveGroupLeader() string { + if m != nil { + return m.CollectiveGroupLeader + } + return "" +} + +func (m *ConfigProto_Experimental) GetExecutorType() string { + if m != nil { + return m.ExecutorType + } + return "" +} + +func (m *ConfigProto_Experimental) GetRecvBufMaxChunk() int32 { + if m != nil { + return m.RecvBufMaxChunk + } + return 0 +} + +func (m *ConfigProto_Experimental) GetUseNumaAffinity() bool { + if m != nil { + return m.UseNumaAffinity + } + return false +} + +func (m *ConfigProto_Experimental) GetCollectiveDeterministicSequentialExecution() bool { + if m != nil { + return m.CollectiveDeterministicSequentialExecution + } + return false +} + +func (m *ConfigProto_Experimental) GetCollectiveNccl() bool { + if m != nil { + return m.CollectiveNccl + } + return false +} + +func (m *ConfigProto_Experimental) GetShareSessionStateInClusterspecPropagation() bool { + if m != nil { + return m.ShareSessionStateInClusterspecPropagation + } + return false +} + +func (m *ConfigProto_Experimental) GetDisableThreadSpinning() bool { + if m != nil { + return m.DisableThreadSpinning + } + return false +} + +func (m *ConfigProto_Experimental) GetShareClusterDevicesInSession() bool { + if m != nil { + return m.ShareClusterDevicesInSession + } + return false +} + +func (m *ConfigProto_Experimental) GetSessionMetadata() *SessionMetadata { + if m != nil { + return m.SessionMetadata + } + return nil +} + +func (m *ConfigProto_Experimental) GetOptimizeForStaticGraph() bool { + if m != nil { + return m.OptimizeForStaticGraph + } + return false +} + +func (m *ConfigProto_Experimental) GetEnableMlirBridge() bool { + if m != nil { + return m.EnableMlirBridge + } + return false +} + +func (m *ConfigProto_Experimental) GetMlirBridgeRollout() ConfigProto_Experimental_MlirBridgeRollout { + if m != nil { + return m.MlirBridgeRollout + } + return ConfigProto_Experimental_MLIR_BRIDGE_ROLLOUT_UNSPECIFIED +} + +func (m *ConfigProto_Experimental) GetEnableMlirGraphOptimization() bool { + if m != nil { + return m.EnableMlirGraphOptimization + } + return false +} + +func (m *ConfigProto_Experimental) GetDisableOutputPartitionGraphs() bool { + if m != nil { + return m.DisableOutputPartitionGraphs + } + return false +} + +func (m *ConfigProto_Experimental) GetXlaFusionAutotunerThresh() int64 { + if m != nil { + return m.XlaFusionAutotunerThresh + } + return 0 +} + +// Options for a single Run() call. +type RunOptions struct { + TraceLevel RunOptions_TraceLevel `protobuf:"varint,1,opt,name=trace_level,json=traceLevel,proto3,enum=tensorflow.RunOptions_TraceLevel" json:"trace_level,omitempty"` + // Time to wait for operation to complete in milliseconds. + TimeoutInMs int64 `protobuf:"varint,2,opt,name=timeout_in_ms,json=timeoutInMs,proto3" json:"timeout_in_ms,omitempty"` + // The thread pool to use, if session_inter_op_thread_pool is configured. + // To use the caller thread set this to -1 - this uses the caller thread + // to execute Session::Run() and thus avoids a context switch. Using the + // caller thread to execute Session::Run() should be done ONLY for simple + // graphs, where the overhead of an additional context switch is + // comparable with the overhead of Session::Run(). + InterOpThreadPool int32 `protobuf:"varint,3,opt,name=inter_op_thread_pool,json=interOpThreadPool,proto3" json:"inter_op_thread_pool,omitempty"` + // Whether the partition graph(s) executed by the executor(s) should be + // outputted via RunMetadata. + OutputPartitionGraphs bool `protobuf:"varint,5,opt,name=output_partition_graphs,json=outputPartitionGraphs,proto3" json:"output_partition_graphs,omitempty"` + // EXPERIMENTAL. Options used to initialize DebuggerState, if enabled. + DebugOptions *DebugOptions `protobuf:"bytes,6,opt,name=debug_options,json=debugOptions,proto3" json:"debug_options,omitempty"` + // When enabled, causes tensor allocation information to be included in + // the error message when the Run() call fails because the allocator ran + // out of memory (OOM). + // + // Enabling this option can slow down the Run() call. + ReportTensorAllocationsUponOom bool `protobuf:"varint,7,opt,name=report_tensor_allocations_upon_oom,json=reportTensorAllocationsUponOom,proto3" json:"report_tensor_allocations_upon_oom,omitempty"` + Experimental *RunOptions_Experimental `protobuf:"bytes,8,opt,name=experimental,proto3" json:"experimental,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunOptions) Reset() { *m = RunOptions{} } +func (m *RunOptions) String() string { return proto.CompactTextString(m) } +func (*RunOptions) ProtoMessage() {} +func (*RunOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{7} +} + +func (m *RunOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunOptions.Unmarshal(m, b) +} +func (m *RunOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunOptions.Marshal(b, m, deterministic) +} +func (m *RunOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunOptions.Merge(m, src) +} +func (m *RunOptions) XXX_Size() int { + return xxx_messageInfo_RunOptions.Size(m) +} +func (m *RunOptions) XXX_DiscardUnknown() { + xxx_messageInfo_RunOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_RunOptions proto.InternalMessageInfo + +func (m *RunOptions) GetTraceLevel() RunOptions_TraceLevel { + if m != nil { + return m.TraceLevel + } + return RunOptions_NO_TRACE +} + +func (m *RunOptions) GetTimeoutInMs() int64 { + if m != nil { + return m.TimeoutInMs + } + return 0 +} + +func (m *RunOptions) GetInterOpThreadPool() int32 { + if m != nil { + return m.InterOpThreadPool + } + return 0 +} + +func (m *RunOptions) GetOutputPartitionGraphs() bool { + if m != nil { + return m.OutputPartitionGraphs + } + return false +} + +func (m *RunOptions) GetDebugOptions() *DebugOptions { + if m != nil { + return m.DebugOptions + } + return nil +} + +func (m *RunOptions) GetReportTensorAllocationsUponOom() bool { + if m != nil { + return m.ReportTensorAllocationsUponOom + } + return false +} + +func (m *RunOptions) GetExperimental() *RunOptions_Experimental { + if m != nil { + return m.Experimental + } + return nil +} + +// Everything inside Experimental is subject to change and is not subject +// to API stability guarantees in +// https://www.tensorflow.org/guide/version_compat. +type RunOptions_Experimental struct { + // If non-zero, declares that this graph is going to use collective + // ops and must synchronize step_ids with any other graph with this + // same group_key value (in a distributed computation where tasks + // run disjoint graphs). + CollectiveGraphKey int64 `protobuf:"varint,1,opt,name=collective_graph_key,json=collectiveGraphKey,proto3" json:"collective_graph_key,omitempty"` + // If true, then operations (using the inter-op pool) across all + // session::run() calls will be centrally scheduled, optimizing for (median + // and tail) latency. + // Consider using this option for CPU-bound workloads like inference. + UseRunHandlerPool bool `protobuf:"varint,2,opt,name=use_run_handler_pool,json=useRunHandlerPool,proto3" json:"use_run_handler_pool,omitempty"` + RunHandlerPoolOptions *RunOptions_Experimental_RunHandlerPoolOptions `protobuf:"bytes,3,opt,name=run_handler_pool_options,json=runHandlerPoolOptions,proto3" json:"run_handler_pool_options,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunOptions_Experimental) Reset() { *m = RunOptions_Experimental{} } +func (m *RunOptions_Experimental) String() string { return proto.CompactTextString(m) } +func (*RunOptions_Experimental) ProtoMessage() {} +func (*RunOptions_Experimental) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{7, 0} +} + +func (m *RunOptions_Experimental) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunOptions_Experimental.Unmarshal(m, b) +} +func (m *RunOptions_Experimental) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunOptions_Experimental.Marshal(b, m, deterministic) +} +func (m *RunOptions_Experimental) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunOptions_Experimental.Merge(m, src) +} +func (m *RunOptions_Experimental) XXX_Size() int { + return xxx_messageInfo_RunOptions_Experimental.Size(m) +} +func (m *RunOptions_Experimental) XXX_DiscardUnknown() { + xxx_messageInfo_RunOptions_Experimental.DiscardUnknown(m) +} + +var xxx_messageInfo_RunOptions_Experimental proto.InternalMessageInfo + +func (m *RunOptions_Experimental) GetCollectiveGraphKey() int64 { + if m != nil { + return m.CollectiveGraphKey + } + return 0 +} + +func (m *RunOptions_Experimental) GetUseRunHandlerPool() bool { + if m != nil { + return m.UseRunHandlerPool + } + return false +} + +func (m *RunOptions_Experimental) GetRunHandlerPoolOptions() *RunOptions_Experimental_RunHandlerPoolOptions { + if m != nil { + return m.RunHandlerPoolOptions + } + return nil +} + +// Options for run handler thread pool. +type RunOptions_Experimental_RunHandlerPoolOptions struct { + // Priority of the request. The run handler thread pool will schedule ops + // based on the priority number. The larger number means higher priority. + Priority int64 `protobuf:"varint,1,opt,name=priority,proto3" json:"priority,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunOptions_Experimental_RunHandlerPoolOptions) Reset() { + *m = RunOptions_Experimental_RunHandlerPoolOptions{} +} +func (m *RunOptions_Experimental_RunHandlerPoolOptions) String() string { + return proto.CompactTextString(m) +} +func (*RunOptions_Experimental_RunHandlerPoolOptions) ProtoMessage() {} +func (*RunOptions_Experimental_RunHandlerPoolOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{7, 0, 0} +} + +func (m *RunOptions_Experimental_RunHandlerPoolOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunOptions_Experimental_RunHandlerPoolOptions.Unmarshal(m, b) +} +func (m *RunOptions_Experimental_RunHandlerPoolOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunOptions_Experimental_RunHandlerPoolOptions.Marshal(b, m, deterministic) +} +func (m *RunOptions_Experimental_RunHandlerPoolOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunOptions_Experimental_RunHandlerPoolOptions.Merge(m, src) +} +func (m *RunOptions_Experimental_RunHandlerPoolOptions) XXX_Size() int { + return xxx_messageInfo_RunOptions_Experimental_RunHandlerPoolOptions.Size(m) +} +func (m *RunOptions_Experimental_RunHandlerPoolOptions) XXX_DiscardUnknown() { + xxx_messageInfo_RunOptions_Experimental_RunHandlerPoolOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_RunOptions_Experimental_RunHandlerPoolOptions proto.InternalMessageInfo + +func (m *RunOptions_Experimental_RunHandlerPoolOptions) GetPriority() int64 { + if m != nil { + return m.Priority + } + return 0 +} + +// Metadata output (i.e., non-Tensor) for a single Run() call. +type RunMetadata struct { + // Statistics traced for this step. Populated if tracing is turned on via the + // "RunOptions" proto. + // EXPERIMENTAL: The format and set of events may change in future versions. + StepStats *step_stats_go_proto.StepStats `protobuf:"bytes,1,opt,name=step_stats,json=stepStats,proto3" json:"step_stats,omitempty"` + // The cost graph for the computation defined by the run call. + CostGraph *cost_graph_go_proto.CostGraphDef `protobuf:"bytes,2,opt,name=cost_graph,json=costGraph,proto3" json:"cost_graph,omitempty"` + // Graphs of the partitions executed by executors. + PartitionGraphs []*graph_go_proto.GraphDef `protobuf:"bytes,3,rep,name=partition_graphs,json=partitionGraphs,proto3" json:"partition_graphs,omitempty"` + // This is only populated for graphs that are run as functions in TensorFlow + // V2. There will be an entry below for each function that is traced. + // The main use cases of the post_optimization_graph and the partition_graphs + // is to give the caller insight into the graphs that were actually run by the + // runtime. Additional information (such as those in step_stats) will match + // these graphs. + // We also include the pre_optimization_graph since it is usually easier to + // read, and is helpful in situations where the caller wants to get a high + // level idea of what the built graph looks like (since the various graph + // optimization passes might change the structure of the graph significantly). + FunctionGraphs []*RunMetadata_FunctionGraphs `protobuf:"bytes,4,rep,name=function_graphs,json=functionGraphs,proto3" json:"function_graphs,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunMetadata) Reset() { *m = RunMetadata{} } +func (m *RunMetadata) String() string { return proto.CompactTextString(m) } +func (*RunMetadata) ProtoMessage() {} +func (*RunMetadata) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{8} +} + +func (m *RunMetadata) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunMetadata.Unmarshal(m, b) +} +func (m *RunMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunMetadata.Marshal(b, m, deterministic) +} +func (m *RunMetadata) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunMetadata.Merge(m, src) +} +func (m *RunMetadata) XXX_Size() int { + return xxx_messageInfo_RunMetadata.Size(m) +} +func (m *RunMetadata) XXX_DiscardUnknown() { + xxx_messageInfo_RunMetadata.DiscardUnknown(m) +} + +var xxx_messageInfo_RunMetadata proto.InternalMessageInfo + +func (m *RunMetadata) GetStepStats() *step_stats_go_proto.StepStats { + if m != nil { + return m.StepStats + } + return nil +} + +func (m *RunMetadata) GetCostGraph() *cost_graph_go_proto.CostGraphDef { + if m != nil { + return m.CostGraph + } + return nil +} + +func (m *RunMetadata) GetPartitionGraphs() []*graph_go_proto.GraphDef { + if m != nil { + return m.PartitionGraphs + } + return nil +} + +func (m *RunMetadata) GetFunctionGraphs() []*RunMetadata_FunctionGraphs { + if m != nil { + return m.FunctionGraphs + } + return nil +} + +type RunMetadata_FunctionGraphs struct { + // TODO(nareshmodi): Include some sort of function/cache-key identifier? + PartitionGraphs []*graph_go_proto.GraphDef `protobuf:"bytes,1,rep,name=partition_graphs,json=partitionGraphs,proto3" json:"partition_graphs,omitempty"` + PreOptimizationGraph *graph_go_proto.GraphDef `protobuf:"bytes,2,opt,name=pre_optimization_graph,json=preOptimizationGraph,proto3" json:"pre_optimization_graph,omitempty"` + PostOptimizationGraph *graph_go_proto.GraphDef `protobuf:"bytes,3,opt,name=post_optimization_graph,json=postOptimizationGraph,proto3" json:"post_optimization_graph,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunMetadata_FunctionGraphs) Reset() { *m = RunMetadata_FunctionGraphs{} } +func (m *RunMetadata_FunctionGraphs) String() string { return proto.CompactTextString(m) } +func (*RunMetadata_FunctionGraphs) ProtoMessage() {} +func (*RunMetadata_FunctionGraphs) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{8, 0} +} + +func (m *RunMetadata_FunctionGraphs) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunMetadata_FunctionGraphs.Unmarshal(m, b) +} +func (m *RunMetadata_FunctionGraphs) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunMetadata_FunctionGraphs.Marshal(b, m, deterministic) +} +func (m *RunMetadata_FunctionGraphs) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunMetadata_FunctionGraphs.Merge(m, src) +} +func (m *RunMetadata_FunctionGraphs) XXX_Size() int { + return xxx_messageInfo_RunMetadata_FunctionGraphs.Size(m) +} +func (m *RunMetadata_FunctionGraphs) XXX_DiscardUnknown() { + xxx_messageInfo_RunMetadata_FunctionGraphs.DiscardUnknown(m) +} + +var xxx_messageInfo_RunMetadata_FunctionGraphs proto.InternalMessageInfo + +func (m *RunMetadata_FunctionGraphs) GetPartitionGraphs() []*graph_go_proto.GraphDef { + if m != nil { + return m.PartitionGraphs + } + return nil +} + +func (m *RunMetadata_FunctionGraphs) GetPreOptimizationGraph() *graph_go_proto.GraphDef { + if m != nil { + return m.PreOptimizationGraph + } + return nil +} + +func (m *RunMetadata_FunctionGraphs) GetPostOptimizationGraph() *graph_go_proto.GraphDef { + if m != nil { + return m.PostOptimizationGraph + } + return nil +} + +// Defines a connection between two tensors in a `GraphDef`. +type TensorConnection struct { + // A tensor name. The value of this tensor will be substituted for + // the tensor named in `to_tensor`. + FromTensor string `protobuf:"bytes,1,opt,name=from_tensor,json=fromTensor,proto3" json:"from_tensor,omitempty"` + // A tensor name. The value of this tensor will be bound to the + // value of the tensor named in `from_tensor`. + ToTensor string `protobuf:"bytes,2,opt,name=to_tensor,json=toTensor,proto3" json:"to_tensor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorConnection) Reset() { *m = TensorConnection{} } +func (m *TensorConnection) String() string { return proto.CompactTextString(m) } +func (*TensorConnection) ProtoMessage() {} +func (*TensorConnection) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{9} +} + +func (m *TensorConnection) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorConnection.Unmarshal(m, b) +} +func (m *TensorConnection) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorConnection.Marshal(b, m, deterministic) +} +func (m *TensorConnection) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorConnection.Merge(m, src) +} +func (m *TensorConnection) XXX_Size() int { + return xxx_messageInfo_TensorConnection.Size(m) +} +func (m *TensorConnection) XXX_DiscardUnknown() { + xxx_messageInfo_TensorConnection.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorConnection proto.InternalMessageInfo + +func (m *TensorConnection) GetFromTensor() string { + if m != nil { + return m.FromTensor + } + return "" +} + +func (m *TensorConnection) GetToTensor() string { + if m != nil { + return m.ToTensor + } + return "" +} + +// Defines a subgraph in another `GraphDef` as a set of feed points and nodes +// to be fetched or executed. +// +// Compare with the arguments to `Session::Run()`. +type CallableOptions struct { + // Tensors to be fed in the callable. Each feed is the name of a tensor. + Feed []string `protobuf:"bytes,1,rep,name=feed,proto3" json:"feed,omitempty"` + // Fetches. A list of tensor names. The caller of the callable expects a + // tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The + // order of specified fetches does not change the execution order. + Fetch []string `protobuf:"bytes,2,rep,name=fetch,proto3" json:"fetch,omitempty"` + // Target Nodes. A list of node names. The named nodes will be run by the + // callable but their outputs will not be returned. + Target []string `protobuf:"bytes,3,rep,name=target,proto3" json:"target,omitempty"` + // Options that will be applied to each run. + RunOptions *RunOptions `protobuf:"bytes,4,opt,name=run_options,json=runOptions,proto3" json:"run_options,omitempty"` + // Tensors to be connected in the callable. Each TensorConnection denotes + // a pair of tensors in the graph, between which an edge will be created + // in the callable. + TensorConnection []*TensorConnection `protobuf:"bytes,5,rep,name=tensor_connection,json=tensorConnection,proto3" json:"tensor_connection,omitempty"` + // The Tensor objects fed in the callable and fetched from the callable + // are expected to be backed by host (CPU) memory by default. + // + // The options below allow changing that - feeding tensors backed by + // device memory, or returning tensors that are backed by device memory. + // + // The maps below map the name of a feed/fetch tensor (which appears in + // 'feed' or 'fetch' fields above), to the fully qualified name of the device + // owning the memory backing the contents of the tensor. + // + // For example, creating a callable with the following options: + // + // CallableOptions { + // feed: "a:0" + // feed: "b:0" + // + // fetch: "x:0" + // fetch: "y:0" + // + // feed_devices: { + // "a:0": "/job:localhost/replica:0/task:0/device:GPU:0" + // } + // + // fetch_devices: { + // "y:0": "/job:localhost/replica:0/task:0/device:GPU:0" + // } + // } + // + // means that the Callable expects: + // - The first argument ("a:0") is a Tensor backed by GPU memory. + // - The second argument ("b:0") is a Tensor backed by host memory. + // and of its return values: + // - The first output ("x:0") will be backed by host memory. + // - The second output ("y:0") will be backed by GPU memory. + // + // FEEDS: + // It is the responsibility of the caller to ensure that the memory of the fed + // tensors will be correctly initialized and synchronized before it is + // accessed by operations executed during the call to Session::RunCallable(). + // + // This is typically ensured by using the TensorFlow memory allocators + // (Device::GetAllocator()) to create the Tensor to be fed. + // + // Alternatively, for CUDA-enabled GPU devices, this typically means that the + // operation that produced the contents of the tensor has completed, i.e., the + // CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or + // cuStreamSynchronize()). + FeedDevices map[string]string `protobuf:"bytes,6,rep,name=feed_devices,json=feedDevices,proto3" json:"feed_devices,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + FetchDevices map[string]string `protobuf:"bytes,7,rep,name=fetch_devices,json=fetchDevices,proto3" json:"fetch_devices,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // By default, RunCallable() will synchronize the GPU stream before returning + // fetched tensors on a GPU device, to ensure that the values in those tensors + // have been produced. This simplifies interacting with the tensors, but + // potentially incurs a performance hit. + // + // If this options is set to true, the caller is responsible for ensuring + // that the values in the fetched tensors have been produced before they are + // used. The caller can do this by invoking `Device::Sync()` on the underlying + // device(s), or by feeding the tensors back to the same Session using + // `feed_devices` with the same corresponding device name. + FetchSkipSync bool `protobuf:"varint,8,opt,name=fetch_skip_sync,json=fetchSkipSync,proto3" json:"fetch_skip_sync,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CallableOptions) Reset() { *m = CallableOptions{} } +func (m *CallableOptions) String() string { return proto.CompactTextString(m) } +func (*CallableOptions) ProtoMessage() {} +func (*CallableOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_e2349c44c118036b, []int{10} +} + +func (m *CallableOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CallableOptions.Unmarshal(m, b) +} +func (m *CallableOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CallableOptions.Marshal(b, m, deterministic) +} +func (m *CallableOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_CallableOptions.Merge(m, src) +} +func (m *CallableOptions) XXX_Size() int { + return xxx_messageInfo_CallableOptions.Size(m) +} +func (m *CallableOptions) XXX_DiscardUnknown() { + xxx_messageInfo_CallableOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_CallableOptions proto.InternalMessageInfo + +func (m *CallableOptions) GetFeed() []string { + if m != nil { + return m.Feed + } + return nil +} + +func (m *CallableOptions) GetFetch() []string { + if m != nil { + return m.Fetch + } + return nil +} + +func (m *CallableOptions) GetTarget() []string { + if m != nil { + return m.Target + } + return nil +} + +func (m *CallableOptions) GetRunOptions() *RunOptions { + if m != nil { + return m.RunOptions + } + return nil +} + +func (m *CallableOptions) GetTensorConnection() []*TensorConnection { + if m != nil { + return m.TensorConnection + } + return nil +} + +func (m *CallableOptions) GetFeedDevices() map[string]string { + if m != nil { + return m.FeedDevices + } + return nil +} + +func (m *CallableOptions) GetFetchDevices() map[string]string { + if m != nil { + return m.FetchDevices + } + return nil +} + +func (m *CallableOptions) GetFetchSkipSync() bool { + if m != nil { + return m.FetchSkipSync + } + return false +} + +func init() { + proto.RegisterEnum("tensorflow.OptimizerOptions_Level", OptimizerOptions_Level_name, OptimizerOptions_Level_value) + proto.RegisterEnum("tensorflow.OptimizerOptions_GlobalJitLevel", OptimizerOptions_GlobalJitLevel_name, OptimizerOptions_GlobalJitLevel_value) + proto.RegisterEnum("tensorflow.ConfigProto_Experimental_MlirBridgeRollout", ConfigProto_Experimental_MlirBridgeRollout_name, ConfigProto_Experimental_MlirBridgeRollout_value) + proto.RegisterEnum("tensorflow.RunOptions_TraceLevel", RunOptions_TraceLevel_name, RunOptions_TraceLevel_value) + proto.RegisterType((*GPUOptions)(nil), "tensorflow.GPUOptions") + proto.RegisterType((*GPUOptions_Experimental)(nil), "tensorflow.GPUOptions.Experimental") + proto.RegisterType((*GPUOptions_Experimental_VirtualDevices)(nil), "tensorflow.GPUOptions.Experimental.VirtualDevices") + proto.RegisterType((*OptimizerOptions)(nil), "tensorflow.OptimizerOptions") + proto.RegisterType((*GraphOptions)(nil), "tensorflow.GraphOptions") + proto.RegisterType((*ThreadPoolOptionProto)(nil), "tensorflow.ThreadPoolOptionProto") + proto.RegisterType((*RPCOptions)(nil), "tensorflow.RPCOptions") + proto.RegisterType((*SessionMetadata)(nil), "tensorflow.SessionMetadata") + proto.RegisterType((*ConfigProto)(nil), "tensorflow.ConfigProto") + proto.RegisterMapType((map[string]int32)(nil), "tensorflow.ConfigProto.DeviceCountEntry") + proto.RegisterType((*ConfigProto_Experimental)(nil), "tensorflow.ConfigProto.Experimental") + proto.RegisterType((*RunOptions)(nil), "tensorflow.RunOptions") + proto.RegisterType((*RunOptions_Experimental)(nil), "tensorflow.RunOptions.Experimental") + proto.RegisterType((*RunOptions_Experimental_RunHandlerPoolOptions)(nil), "tensorflow.RunOptions.Experimental.RunHandlerPoolOptions") + proto.RegisterType((*RunMetadata)(nil), "tensorflow.RunMetadata") + proto.RegisterType((*RunMetadata_FunctionGraphs)(nil), "tensorflow.RunMetadata.FunctionGraphs") + proto.RegisterType((*TensorConnection)(nil), "tensorflow.TensorConnection") + proto.RegisterType((*CallableOptions)(nil), "tensorflow.CallableOptions") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.CallableOptions.FeedDevicesEntry") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.CallableOptions.FetchDevicesEntry") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/config.proto", fileDescriptor_e2349c44c118036b) +} + +var fileDescriptor_e2349c44c118036b = []byte{ + // 2998 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x59, 0x4f, 0x73, 0xdb, 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DO NOT EDIT. +// source: tensorflow/core/protobuf/control_flow.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing the values in ControlFlowContext. +type ValuesDef struct { + // Value names that have been seen in this context. + Values []string `protobuf:"bytes,1,rep,name=values,proto3" json:"values,omitempty"` + // Value names referenced by but external to this context. + ExternalValues map[string]string `protobuf:"bytes,2,rep,name=external_values,json=externalValues,proto3" json:"external_values,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ValuesDef) Reset() { *m = ValuesDef{} } +func (m *ValuesDef) String() string { return proto.CompactTextString(m) } +func (*ValuesDef) ProtoMessage() {} +func (*ValuesDef) Descriptor() ([]byte, []int) { + return fileDescriptor_64affc5a646d7df1, []int{0} +} + +func (m *ValuesDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ValuesDef.Unmarshal(m, b) +} +func (m *ValuesDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ValuesDef.Marshal(b, m, deterministic) +} +func (m *ValuesDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_ValuesDef.Merge(m, src) +} +func (m *ValuesDef) XXX_Size() int { + return xxx_messageInfo_ValuesDef.Size(m) +} +func (m *ValuesDef) XXX_DiscardUnknown() { + xxx_messageInfo_ValuesDef.DiscardUnknown(m) +} + +var xxx_messageInfo_ValuesDef proto.InternalMessageInfo + +func (m *ValuesDef) GetValues() []string { + if m != nil { + return m.Values + } + return nil +} + +func (m *ValuesDef) GetExternalValues() map[string]string { + if m != nil { + return m.ExternalValues + } + return nil +} + +// Container for any kind of control flow context. Any other control flow +// contexts that are added below should also be added here. +type ControlFlowContextDef struct { + // Types that are valid to be assigned to Ctxt: + // *ControlFlowContextDef_CondCtxt + // *ControlFlowContextDef_WhileCtxt + Ctxt isControlFlowContextDef_Ctxt `protobuf_oneof:"ctxt"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ControlFlowContextDef) Reset() { *m = ControlFlowContextDef{} } +func (m *ControlFlowContextDef) String() string { return proto.CompactTextString(m) } +func (*ControlFlowContextDef) ProtoMessage() {} +func (*ControlFlowContextDef) Descriptor() ([]byte, []int) { + return fileDescriptor_64affc5a646d7df1, []int{1} +} + +func (m *ControlFlowContextDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ControlFlowContextDef.Unmarshal(m, b) +} +func (m *ControlFlowContextDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ControlFlowContextDef.Marshal(b, m, deterministic) +} +func (m *ControlFlowContextDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_ControlFlowContextDef.Merge(m, src) +} +func (m *ControlFlowContextDef) XXX_Size() int { + return xxx_messageInfo_ControlFlowContextDef.Size(m) +} +func (m *ControlFlowContextDef) XXX_DiscardUnknown() { + xxx_messageInfo_ControlFlowContextDef.DiscardUnknown(m) +} + +var xxx_messageInfo_ControlFlowContextDef proto.InternalMessageInfo + +type isControlFlowContextDef_Ctxt interface { + isControlFlowContextDef_Ctxt() +} + +type ControlFlowContextDef_CondCtxt struct { + CondCtxt *CondContextDef `protobuf:"bytes,1,opt,name=cond_ctxt,json=condCtxt,proto3,oneof"` +} + +type ControlFlowContextDef_WhileCtxt struct { + WhileCtxt *WhileContextDef `protobuf:"bytes,2,opt,name=while_ctxt,json=whileCtxt,proto3,oneof"` +} + +func (*ControlFlowContextDef_CondCtxt) isControlFlowContextDef_Ctxt() {} + +func (*ControlFlowContextDef_WhileCtxt) isControlFlowContextDef_Ctxt() {} + +func (m *ControlFlowContextDef) GetCtxt() isControlFlowContextDef_Ctxt { + if m != nil { + return m.Ctxt + } + return nil +} + +func (m *ControlFlowContextDef) GetCondCtxt() *CondContextDef { + if x, ok := m.GetCtxt().(*ControlFlowContextDef_CondCtxt); ok { + return x.CondCtxt + } + return nil +} + +func (m *ControlFlowContextDef) GetWhileCtxt() *WhileContextDef { + if x, ok := m.GetCtxt().(*ControlFlowContextDef_WhileCtxt); ok { + return x.WhileCtxt + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*ControlFlowContextDef) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*ControlFlowContextDef_CondCtxt)(nil), + (*ControlFlowContextDef_WhileCtxt)(nil), + } +} + +// Protocol buffer representing a CondContext object. +type CondContextDef struct { + // Name of the context. + ContextName string `protobuf:"bytes,1,opt,name=context_name,json=contextName,proto3" json:"context_name,omitempty"` + // Name of the pred tensor. + PredName string `protobuf:"bytes,2,opt,name=pred_name,json=predName,proto3" json:"pred_name,omitempty"` + // Name of the pivot tensor. + PivotName string `protobuf:"bytes,3,opt,name=pivot_name,json=pivotName,proto3" json:"pivot_name,omitempty"` + // Branch prediction. 0 or 1. + Branch int32 `protobuf:"varint,4,opt,name=branch,proto3" json:"branch,omitempty"` + // Values and external values in control flow context. + ValuesDef *ValuesDef `protobuf:"bytes,5,opt,name=values_def,json=valuesDef,proto3" json:"values_def,omitempty"` + // Contexts contained inside this context (e.g. nested conds). + NestedContexts []*ControlFlowContextDef `protobuf:"bytes,6,rep,name=nested_contexts,json=nestedContexts,proto3" json:"nested_contexts,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CondContextDef) Reset() { *m = CondContextDef{} } +func (m *CondContextDef) String() string { return proto.CompactTextString(m) } +func (*CondContextDef) ProtoMessage() {} +func (*CondContextDef) Descriptor() ([]byte, []int) { + return fileDescriptor_64affc5a646d7df1, []int{2} +} + +func (m *CondContextDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CondContextDef.Unmarshal(m, b) +} +func (m *CondContextDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CondContextDef.Marshal(b, m, deterministic) +} +func (m *CondContextDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_CondContextDef.Merge(m, src) +} +func (m *CondContextDef) XXX_Size() int { + return xxx_messageInfo_CondContextDef.Size(m) +} +func (m *CondContextDef) XXX_DiscardUnknown() { + xxx_messageInfo_CondContextDef.DiscardUnknown(m) +} + +var xxx_messageInfo_CondContextDef proto.InternalMessageInfo + +func (m *CondContextDef) GetContextName() string { + if m != nil { + return m.ContextName + } + return "" +} + +func (m *CondContextDef) GetPredName() string { + if m != nil { + return m.PredName + } + return "" +} + +func (m *CondContextDef) GetPivotName() string { + if m != nil { + return m.PivotName + } + return "" +} + +func (m *CondContextDef) GetBranch() int32 { + if m != nil { + return m.Branch + } + return 0 +} + +func (m *CondContextDef) GetValuesDef() *ValuesDef { + if m != nil { + return m.ValuesDef + } + return nil +} + +func (m *CondContextDef) GetNestedContexts() []*ControlFlowContextDef { + if m != nil { + return m.NestedContexts + } + return nil +} + +// Protocol buffer representing a WhileContext object. +type WhileContextDef struct { + // Name of the context. + ContextName string `protobuf:"bytes,1,opt,name=context_name,json=contextName,proto3" json:"context_name,omitempty"` + // The number of iterations allowed to run in parallel. + ParallelIterations int32 `protobuf:"varint,2,opt,name=parallel_iterations,json=parallelIterations,proto3" json:"parallel_iterations,omitempty"` + // Whether backprop is enabled for this while loop. + BackProp bool `protobuf:"varint,3,opt,name=back_prop,json=backProp,proto3" json:"back_prop,omitempty"` + // Whether GPU-CPU memory swap is enabled for this loop. + SwapMemory bool `protobuf:"varint,4,opt,name=swap_memory,json=swapMemory,proto3" json:"swap_memory,omitempty"` + // Name of the pivot tensor. + PivotName string `protobuf:"bytes,5,opt,name=pivot_name,json=pivotName,proto3" json:"pivot_name,omitempty"` + // Name of the pivot_for_pred tensor. + PivotForPredName string `protobuf:"bytes,6,opt,name=pivot_for_pred_name,json=pivotForPredName,proto3" json:"pivot_for_pred_name,omitempty"` + // Name of the pivot_for_body tensor. + PivotForBodyName string `protobuf:"bytes,7,opt,name=pivot_for_body_name,json=pivotForBodyName,proto3" json:"pivot_for_body_name,omitempty"` + // List of names for exit tensors. + LoopExitNames []string `protobuf:"bytes,8,rep,name=loop_exit_names,json=loopExitNames,proto3" json:"loop_exit_names,omitempty"` + // List of names for enter tensors. + LoopEnterNames []string `protobuf:"bytes,10,rep,name=loop_enter_names,json=loopEnterNames,proto3" json:"loop_enter_names,omitempty"` + // Values and external values in control flow context. + ValuesDef *ValuesDef `protobuf:"bytes,9,opt,name=values_def,json=valuesDef,proto3" json:"values_def,omitempty"` + // Optional name of the maximum_iterations tensor. + MaximumIterationsName string `protobuf:"bytes,11,opt,name=maximum_iterations_name,json=maximumIterationsName,proto3" json:"maximum_iterations_name,omitempty"` + // Contexts contained inside this context (e.g. nested whiles). + NestedContexts []*ControlFlowContextDef `protobuf:"bytes,12,rep,name=nested_contexts,json=nestedContexts,proto3" json:"nested_contexts,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *WhileContextDef) Reset() { *m = WhileContextDef{} } +func (m *WhileContextDef) String() string { return proto.CompactTextString(m) } +func (*WhileContextDef) ProtoMessage() {} +func (*WhileContextDef) Descriptor() ([]byte, []int) { + return fileDescriptor_64affc5a646d7df1, []int{3} +} + +func (m *WhileContextDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_WhileContextDef.Unmarshal(m, b) +} +func (m *WhileContextDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_WhileContextDef.Marshal(b, m, deterministic) +} +func (m *WhileContextDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_WhileContextDef.Merge(m, src) +} +func (m *WhileContextDef) XXX_Size() int { + return xxx_messageInfo_WhileContextDef.Size(m) +} +func (m *WhileContextDef) XXX_DiscardUnknown() { + xxx_messageInfo_WhileContextDef.DiscardUnknown(m) +} + +var xxx_messageInfo_WhileContextDef proto.InternalMessageInfo + +func (m *WhileContextDef) GetContextName() string { + if m != nil { + return m.ContextName + } + return "" +} + +func (m *WhileContextDef) GetParallelIterations() int32 { + if m != nil { + return m.ParallelIterations + } + return 0 +} + +func (m *WhileContextDef) GetBackProp() bool { + if m != nil { + return m.BackProp + } + return false +} + +func (m *WhileContextDef) GetSwapMemory() bool { + if m != nil { + return m.SwapMemory + } + return false +} + +func (m *WhileContextDef) GetPivotName() string { + if m != nil { + return m.PivotName + } + return "" +} + +func (m *WhileContextDef) GetPivotForPredName() string { + if m != nil { + return m.PivotForPredName + } + return "" +} + +func (m *WhileContextDef) GetPivotForBodyName() string { + if m != nil { + return m.PivotForBodyName + } + return "" +} + +func (m *WhileContextDef) GetLoopExitNames() []string { + if m != nil { + return m.LoopExitNames + } + return nil +} + +func (m *WhileContextDef) GetLoopEnterNames() []string { + if m != nil { + return m.LoopEnterNames + } + return nil +} + +func (m *WhileContextDef) GetValuesDef() *ValuesDef { + if m != nil { + return m.ValuesDef + } + return nil +} + +func (m *WhileContextDef) GetMaximumIterationsName() string { + if m != nil { + return m.MaximumIterationsName + } + return "" +} + +func (m *WhileContextDef) GetNestedContexts() []*ControlFlowContextDef { + if m != nil { + return m.NestedContexts + } + return nil +} + +func init() { + proto.RegisterType((*ValuesDef)(nil), "tensorflow.ValuesDef") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.ValuesDef.ExternalValuesEntry") + proto.RegisterType((*ControlFlowContextDef)(nil), "tensorflow.ControlFlowContextDef") + proto.RegisterType((*CondContextDef)(nil), "tensorflow.CondContextDef") + proto.RegisterType((*WhileContextDef)(nil), "tensorflow.WhileContextDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/control_flow.proto", fileDescriptor_64affc5a646d7df1) +} + +var fileDescriptor_64affc5a646d7df1 = []byte{ + // 636 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x9c, 0x54, 0xdf, 0x6e, 0xd3, 0x3c, + 0x14, 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0000000..8aa0287 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/conv_autotuning.pb.go @@ -0,0 +1,200 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/conv_autotuning.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + stream_executor "github.com/tensorflow/tensorflow/tensorflow/go/stream_executor" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// A convolution. Currently it's only used for logging. In the future, we may +// want to use it in the API as well. +type ConvolutionProto struct { + Kind stream_executor.ConvolutionKind `protobuf:"varint,1,opt,name=kind,proto3,enum=stream_executor.dnn.ConvolutionKind" json:"kind,omitempty"` + Input *stream_executor.TensorDescriptorProto `protobuf:"bytes,2,opt,name=input,proto3" json:"input,omitempty"` + Filter *stream_executor.TensorDescriptorProto `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"` + Output *stream_executor.TensorDescriptorProto `protobuf:"bytes,4,opt,name=output,proto3" json:"output,omitempty"` + ConvDesc *stream_executor.ConvolutionDescriptorProto `protobuf:"bytes,5,opt,name=conv_desc,json=convDesc,proto3" json:"conv_desc,omitempty"` + // result = conv_scale * conv(...) + side_value_scale * side_value. + // side_value is an arbitrary buffer if activation is not none. Otherwise, it + // has to be the result buffer (using its old values). + ConvScale float64 `protobuf:"fixed64,6,opt,name=conv_scale,json=convScale,proto3" json:"conv_scale,omitempty"` + SideValueScale float64 `protobuf:"fixed64,7,opt,name=side_value_scale,json=sideValueScale,proto3" json:"side_value_scale,omitempty"` + Activation stream_executor.ActivationMode `protobuf:"varint,8,opt,name=activation,proto3,enum=stream_executor.dnn.ActivationMode" json:"activation,omitempty"` + InputAddress int64 `protobuf:"varint,9,opt,name=input_address,json=inputAddress,proto3" json:"input_address,omitempty"` + FilterAddress int64 `protobuf:"varint,10,opt,name=filter_address,json=filterAddress,proto3" json:"filter_address,omitempty"` + OutputAddress int64 `protobuf:"varint,11,opt,name=output_address,json=outputAddress,proto3" json:"output_address,omitempty"` + BiasAddress int64 `protobuf:"varint,12,opt,name=bias_address,json=biasAddress,proto3" json:"bias_address,omitempty"` + SideInputAddress int64 `protobuf:"varint,13,opt,name=side_input_address,json=sideInputAddress,proto3" json:"side_input_address,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ConvolutionProto) Reset() { *m = ConvolutionProto{} } +func (m *ConvolutionProto) String() string { return proto.CompactTextString(m) } +func (*ConvolutionProto) ProtoMessage() {} +func (*ConvolutionProto) Descriptor() ([]byte, []int) { + return fileDescriptor_5d1b55de4e1b5595, []int{0} +} + +func (m *ConvolutionProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ConvolutionProto.Unmarshal(m, b) +} +func (m *ConvolutionProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ConvolutionProto.Marshal(b, m, deterministic) +} +func (m *ConvolutionProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_ConvolutionProto.Merge(m, src) +} +func (m *ConvolutionProto) XXX_Size() int { + return xxx_messageInfo_ConvolutionProto.Size(m) +} +func (m *ConvolutionProto) XXX_DiscardUnknown() { + xxx_messageInfo_ConvolutionProto.DiscardUnknown(m) +} + +var xxx_messageInfo_ConvolutionProto proto.InternalMessageInfo + +func (m *ConvolutionProto) GetKind() stream_executor.ConvolutionKind { + if m != nil { + return m.Kind + } + return stream_executor.ConvolutionKind_INVALID +} + +func (m *ConvolutionProto) GetInput() *stream_executor.TensorDescriptorProto { + if m != nil { + return m.Input + } + return nil +} + +func (m *ConvolutionProto) GetFilter() *stream_executor.TensorDescriptorProto { + if m != nil { + return m.Filter + } + return nil +} + +func (m *ConvolutionProto) GetOutput() *stream_executor.TensorDescriptorProto { + if m != nil { + return m.Output + } + return nil +} + +func (m *ConvolutionProto) GetConvDesc() *stream_executor.ConvolutionDescriptorProto { + if m != nil { + return m.ConvDesc + } + return nil +} + +func (m *ConvolutionProto) GetConvScale() float64 { + if m != nil { + return m.ConvScale + } + return 0 +} + +func (m *ConvolutionProto) GetSideValueScale() float64 { + if m != nil { + return m.SideValueScale + } + return 0 +} + +func (m *ConvolutionProto) GetActivation() stream_executor.ActivationMode { + if m != nil { + return m.Activation + } + return stream_executor.ActivationMode_kNone +} + +func (m *ConvolutionProto) GetInputAddress() int64 { + if m != nil { + return m.InputAddress + } + return 0 +} + +func (m *ConvolutionProto) GetFilterAddress() int64 { + if m != nil { + return m.FilterAddress + } + return 0 +} + +func (m *ConvolutionProto) GetOutputAddress() int64 { + if m != nil { + return m.OutputAddress + } + return 0 +} + +func (m *ConvolutionProto) GetBiasAddress() int64 { + if m != nil { + return m.BiasAddress + } + return 0 +} + +func (m *ConvolutionProto) GetSideInputAddress() int64 { + if m != nil { + return m.SideInputAddress + } + return 0 +} + +func init() { + proto.RegisterType((*ConvolutionProto)(nil), "tensorflow.ConvolutionProto") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/conv_autotuning.proto", fileDescriptor_5d1b55de4e1b5595) +} + +var fileDescriptor_5d1b55de4e1b5595 = []byte{ + // 413 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x93, 0xdb, 0x6b, 0xd4, 0x40, + 0x18, 0xc5, 0x89, 0xed, 0xae, 0xed, 0xb7, 0x17, 0xca, 0x3c, 0x05, 0x41, 0x58, 0x6d, 0x85, 0x20, + 0x92, 0x40, 0x7d, 0xf1, 0xd1, 0xb6, 0xbe, 0x88, 0x0a, 0x12, 0x6f, 0xe0, 0x4b, 0x98, 0x9d, 0x99, + 0x8d, 0x83, 0xe9, 0x7c, 0xcb, 0x5c, 0xa2, 0x7f, 0x99, 0x7f, 0x9f, 0xcc, 0x37, 0x6d, 0x8c, 0xcb, + 0x42, 0xe9, 0xd3, 0x0e, 0x67, 0x7f, 0xe7, 0xcc, 0xe1, 0x24, 0x81, 0xd2, 0x2b, 0xe3, 0xd0, 0x6e, + 0x3a, 0xfc, 0x55, 0x09, 0xb4, 0xaa, 0xda, 0x5a, 0xf4, 0xb8, 0x0e, 0x9b, 0x4a, 0xa0, 0xe9, 0x1b, + 0x1e, 0x3c, 0xfa, 0x60, 0xb4, 0x69, 0x4b, 0xfa, 0x83, 0xc1, 0x3f, 0xfe, 0xd1, 0xd9, 0xc8, 0xeb, + 0xbc, 0x55, 0xfc, 0xba, 0x51, 0xbf, 0x95, 0x08, 0x1e, 0x6d, 0x25, 0x8d, 0x49, 0x8e, 0xa7, 0x7f, + 0x26, 0x70, 0x72, 0x85, 0xa6, 0xc7, 0x2e, 0x78, 0x8d, 0xe6, 0x23, 0xc5, 0xbc, 0x82, 0xc3, 0x9f, + 0xda, 0xc8, 0x3c, 0x5b, 0x65, 0xc5, 0xf2, 0xfc, 0xac, 0xdc, 0xb1, 0x97, 0xd1, 0x3e, 0x32, 0xbd, + 0xd3, 0x46, 0xd6, 0xe4, 0x60, 0xaf, 0x61, 0xa2, 0xcd, 0x36, 0xf8, 0xfc, 0xc1, 0x2a, 0x2b, 0x66, + 0xe7, 0xcf, 0xf7, 0x5a, 0x3f, 0x53, 0xb1, 0x37, 0xca, 0x09, 0xab, 0xb7, 0x1e, 0x2d, 0x5d, 0x5a, + 0x27, 0x23, 0xbb, 0x84, 0xe9, 0x46, 0x77, 0x5e, 0xd9, 0xfc, 0xe0, 0xde, 0x11, 0x37, 0xce, 0x98, + 0x81, 0xc1, 0xc7, 0x1a, 0x87, 0xf7, 0xcf, 0x48, 0x4e, 0xf6, 0x1e, 0x8e, 0x69, 0x63, 0xa9, 0x9c, + 0xc8, 0x27, 0x14, 0x53, 0xdd, 0x35, 0xc4, 0x6e, 0xd6, 0x51, 0x4c, 0x88, 0x22, 0x7b, 0x0c, 0x40, + 0x69, 0x4e, 0xf0, 0x4e, 0xe5, 0xd3, 0x55, 0x56, 0x64, 0x35, 0xe5, 0x7f, 0x8a, 0x02, 0x2b, 0xe0, + 0xc4, 0x69, 0xa9, 0x9a, 0x9e, 0x77, 0x41, 0xdd, 0x40, 0x0f, 0x09, 0x5a, 0x46, 0xfd, 0x6b, 0x94, + 0x13, 0x79, 0x05, 0xc0, 0x85, 0xd7, 0x3d, 0x8f, 0xf7, 0xe5, 0x47, 0xf4, 0x80, 0x4e, 0xf7, 0xf6, + 0xba, 0x18, 0xb0, 0x0f, 0x28, 0x55, 0x3d, 0xb2, 0xb1, 0x53, 0x58, 0xd0, 0xd8, 0x0d, 0x97, 0xd2, + 0x2a, 0xe7, 0xf2, 0xe3, 0x55, 0x56, 0x1c, 0xd4, 0x73, 0x12, 0x2f, 0x92, 0xc6, 0x9e, 0xc1, 0x32, + 0xcd, 0x39, 0x50, 0x40, 0xd4, 0x22, 0xa9, 0x23, 0x2c, 0x2d, 0x36, 0x60, 0xb3, 0x84, 0x25, 0xf5, + 0x16, 0x7b, 0x02, 0xf3, 0xb5, 0xe6, 0x6e, 0x80, 0xe6, 0x04, 0xcd, 0xa2, 0x76, 0x8b, 0xbc, 0x00, + 0x46, 0x23, 0xfc, 0x5f, 0x6d, 0x41, 0x20, 0xcd, 0xf3, 0x76, 0x54, 0xef, 0xf2, 0xdb, 0xf7, 0x2f, + 0xad, 0xf6, 0x3f, 0xc2, 0xba, 0x14, 0x78, 0x5d, 0x8d, 0xde, 0xf5, 0xfd, 0xc7, 0x16, 0x77, 0x3e, + 0xa0, 0x0d, 0xda, 0x26, 0x2a, 0x0d, 0x29, 0xae, 0x69, 0x31, 0x9d, 0xd6, 0x53, 0xfa, 0x79, 0xf9, + 0x37, 0x00, 0x00, 0xff, 0xff, 0xc3, 0x55, 0x26, 0xfc, 0x7c, 0x03, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/critical_section.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/critical_section.pb.go new file mode 100644 index 0000000..12dc500 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/critical_section.pb.go @@ -0,0 +1,143 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/critical_section.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing a CriticalSection. +type CriticalSectionDef struct { + // Name of the critical section handle. + CriticalSectionName string `protobuf:"bytes,1,opt,name=critical_section_name,json=criticalSectionName,proto3" json:"critical_section_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CriticalSectionDef) Reset() { *m = CriticalSectionDef{} } +func (m *CriticalSectionDef) String() string { return proto.CompactTextString(m) } +func (*CriticalSectionDef) ProtoMessage() {} +func (*CriticalSectionDef) Descriptor() ([]byte, []int) { + return fileDescriptor_d30d8be90fd098b9, []int{0} +} + +func (m *CriticalSectionDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CriticalSectionDef.Unmarshal(m, b) +} +func (m *CriticalSectionDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CriticalSectionDef.Marshal(b, m, deterministic) +} +func (m *CriticalSectionDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_CriticalSectionDef.Merge(m, src) +} +func (m *CriticalSectionDef) XXX_Size() int { + return xxx_messageInfo_CriticalSectionDef.Size(m) +} +func (m *CriticalSectionDef) XXX_DiscardUnknown() { + xxx_messageInfo_CriticalSectionDef.DiscardUnknown(m) +} + +var xxx_messageInfo_CriticalSectionDef proto.InternalMessageInfo + +func (m *CriticalSectionDef) GetCriticalSectionName() string { + if m != nil { + return m.CriticalSectionName + } + return "" +} + +// Protocol buffer representing a CriticalSection execution. +type CriticalSectionExecutionDef struct { + // Name of the critical section handle. + ExecuteInCriticalSectionName string `protobuf:"bytes,1,opt,name=execute_in_critical_section_name,json=executeInCriticalSectionName,proto3" json:"execute_in_critical_section_name,omitempty"` + // Whether this operation requires exclusive access to its resources, + // (i.e., no other CriticalSections may request the same resources). + ExclusiveResourceAccess bool `protobuf:"varint,2,opt,name=exclusive_resource_access,json=exclusiveResourceAccess,proto3" json:"exclusive_resource_access,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CriticalSectionExecutionDef) Reset() { *m = CriticalSectionExecutionDef{} } +func (m *CriticalSectionExecutionDef) String() string { return proto.CompactTextString(m) } +func (*CriticalSectionExecutionDef) ProtoMessage() {} +func (*CriticalSectionExecutionDef) Descriptor() ([]byte, []int) { + return fileDescriptor_d30d8be90fd098b9, []int{1} +} + +func (m *CriticalSectionExecutionDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CriticalSectionExecutionDef.Unmarshal(m, b) +} +func (m *CriticalSectionExecutionDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CriticalSectionExecutionDef.Marshal(b, m, deterministic) +} +func (m *CriticalSectionExecutionDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_CriticalSectionExecutionDef.Merge(m, src) +} +func (m *CriticalSectionExecutionDef) XXX_Size() int { + return xxx_messageInfo_CriticalSectionExecutionDef.Size(m) +} +func (m *CriticalSectionExecutionDef) XXX_DiscardUnknown() { + xxx_messageInfo_CriticalSectionExecutionDef.DiscardUnknown(m) +} + +var xxx_messageInfo_CriticalSectionExecutionDef proto.InternalMessageInfo + +func (m *CriticalSectionExecutionDef) GetExecuteInCriticalSectionName() string { + if m != nil { + return m.ExecuteInCriticalSectionName + } + return "" +} + +func (m *CriticalSectionExecutionDef) GetExclusiveResourceAccess() bool { + if m != nil { + return m.ExclusiveResourceAccess + } + return false +} + +func init() { + proto.RegisterType((*CriticalSectionDef)(nil), "tensorflow.CriticalSectionDef") + proto.RegisterType((*CriticalSectionExecutionDef)(nil), "tensorflow.CriticalSectionExecutionDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/critical_section.proto", fileDescriptor_d30d8be90fd098b9) +} + +var fileDescriptor_d30d8be90fd098b9 = []byte{ + // 260 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x51, 0xbd, 0x4e, 0xc3, 0x30, + 0x10, 0x96, 0x19, 0x10, 0x78, 0x0c, 0xaa, 0x08, 0x82, 0x21, 0xea, 0xd4, 0x29, 0x91, 0x60, 0x63, + 0xa3, 0x05, 0x04, 0x0b, 0xaa, 0x82, 0x58, 0x58, 0xac, 0xe4, 0x74, 0x0e, 0x16, 0x89, 0x0f, 0x9d, + 0x6d, 0xda, 0x97, 0xe0, 0x21, 0x78, 0x4b, 0x46, 0x54, 0x37, 0x50, 0x08, 0xa8, 0xdb, 0xe7, 0xfb, + 0xfe, 0xa4, 0xcf, 0xb2, 0xf0, 0x68, 0x1d, 0xb1, 0x6e, 0x69, 0x51, 0x00, 0x31, 0x16, 0x2f, 0x4c, + 0x9e, 0xea, 0xa0, 0x0b, 0x60, 0xe3, 0x0d, 0x54, 0xad, 0x72, 0x08, 0xde, 0x90, 0xcd, 0x23, 0x93, + 0xc8, 0x8d, 0x61, 0x7c, 0x23, 0x93, 0x59, 0xaf, 0xba, 0x5f, 0x8b, 0x2e, 0x51, 0x27, 0xa7, 0x72, + 0x34, 0xf4, 0x2a, 0x5b, 0x75, 0x98, 0x8a, 0x4c, 0x4c, 0xf6, 0xcb, 0x03, 0xf8, 0x6d, 0xb9, 0xab, + 0x3a, 0x1c, 0xbf, 0x0b, 0x79, 0x3c, 0x88, 0xba, 0x5a, 0x22, 0x84, 0xaf, 0xcc, 0x6b, 0x99, 0x61, + 0x7c, 0xa3, 0x32, 0x56, 0x6d, 0x8b, 0x3f, 0xe9, 0x75, 0xb7, 0x76, 0xf6, 0xb7, 0x27, 0x39, 0x97, + 0x47, 0xb8, 0x84, 0x36, 0x38, 0xf3, 0x8a, 0x8a, 0xd1, 0x51, 0x60, 0x40, 0x55, 0x01, 0xa0, 0x73, + 0xe9, 0x4e, 0x26, 0x26, 0x7b, 0xe5, 0xe1, 0xb7, 0xa0, 0xec, 0xf9, 0x8b, 0x48, 0x4f, 0xdf, 0x84, + 0x4c, 0x89, 0x9b, 0x7c, 0x33, 0x40, 0xae, 0xb9, 0xea, 0x70, 0x41, 0xfc, 0x3c, 0x1d, 0x0d, 0xda, + 0xe6, 0xab, 0xb1, 0xdc, 0x5c, 0x3c, 0x3e, 0x34, 0xc6, 0x3f, 0x85, 0x3a, 0x07, 0xea, 0x7e, 0x6e, + 0xfd, 0x3f, 0x6c, 0x68, 0xf0, 0x09, 0x9a, 0x58, 0xad, 0x2e, 0x2a, 0x5e, 0x9c, 0x6a, 0x68, 0x8d, + 0x3e, 0x84, 0xa8, 0x77, 0x23, 0x3a, 0xfb, 0x0c, 0x00, 0x00, 0xff, 0xff, 0xfa, 0xdf, 0x90, 0x13, + 0xc3, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/debug.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/debug.pb.go new file mode 100644 index 0000000..701e7a0 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/debug.pb.go @@ -0,0 +1,352 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/debug.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Option for watching a node in TensorFlow Debugger (tfdbg). +type DebugTensorWatch struct { + // Name of the node to watch. + // Use "*" for wildcard. But note: currently, regex is not supported in + // general. + NodeName string `protobuf:"bytes,1,opt,name=node_name,json=nodeName,proto3" json:"node_name,omitempty"` + // Output slot to watch. + // The semantics of output_slot == -1 is that all outputs of the node + // will be watched (i.e., a wildcard). + // Other negative values of output_slot are invalid and will lead to + // errors currently. + OutputSlot int32 `protobuf:"varint,2,opt,name=output_slot,json=outputSlot,proto3" json:"output_slot,omitempty"` + // Name(s) of the debugging op(s). + // One or more than one probes on a tensor. + // e.g., {"DebugIdentity", "DebugNanCount"} + DebugOps []string `protobuf:"bytes,3,rep,name=debug_ops,json=debugOps,proto3" json:"debug_ops,omitempty"` + // URL(s) for debug targets(s). + // + // Supported URL formats are: + // - file:///foo/tfdbg_dump: Writes out Event content to file + // /foo/tfdbg_dump. Assumes all directories can be created if they don't + // already exist. + // - grpc://localhost:11011: Sends an RPC request to an EventListener + // service running at localhost:11011 with the event. + // - memcbk:///event_key: Routes tensors to clients using the + // callback registered with the DebugCallbackRegistry for event_key. + // + // Each debug op listed in debug_ops will publish its output tensor (debug + // signal) to all URLs in debug_urls. + // + // N.B. Session::Run() supports concurrent invocations of the same inputs + // (feed keys), outputs and target nodes. If such concurrent invocations + // are to be debugged, the callers of Session::Run() must use distinct + // debug_urls to make sure that the streamed or dumped events do not overlap + // among the invocations. + // TODO(cais): More visible documentation of this in g3docs. + DebugUrls []string `protobuf:"bytes,4,rep,name=debug_urls,json=debugUrls,proto3" json:"debug_urls,omitempty"` + // Do not error out if debug op creation fails (e.g., due to dtype + // incompatibility). Instead, just log the failure. + TolerateDebugOpCreationFailures bool `protobuf:"varint,5,opt,name=tolerate_debug_op_creation_failures,json=tolerateDebugOpCreationFailures,proto3" json:"tolerate_debug_op_creation_failures,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebugTensorWatch) Reset() { *m = DebugTensorWatch{} } +func (m *DebugTensorWatch) String() string { return proto.CompactTextString(m) } +func (*DebugTensorWatch) ProtoMessage() {} +func (*DebugTensorWatch) Descriptor() ([]byte, []int) { + return fileDescriptor_4fbf764b7c91eef6, []int{0} +} + +func (m *DebugTensorWatch) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebugTensorWatch.Unmarshal(m, b) +} +func (m *DebugTensorWatch) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebugTensorWatch.Marshal(b, m, deterministic) +} +func (m *DebugTensorWatch) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebugTensorWatch.Merge(m, src) +} +func (m *DebugTensorWatch) XXX_Size() int { + return xxx_messageInfo_DebugTensorWatch.Size(m) +} +func (m *DebugTensorWatch) XXX_DiscardUnknown() { + xxx_messageInfo_DebugTensorWatch.DiscardUnknown(m) +} + +var xxx_messageInfo_DebugTensorWatch proto.InternalMessageInfo + +func (m *DebugTensorWatch) GetNodeName() string { + if m != nil { + return m.NodeName + } + return "" +} + +func (m *DebugTensorWatch) GetOutputSlot() int32 { + if m != nil { + return m.OutputSlot + } + return 0 +} + +func (m *DebugTensorWatch) GetDebugOps() []string { + if m != nil { + return m.DebugOps + } + return nil +} + +func (m *DebugTensorWatch) GetDebugUrls() []string { + if m != nil { + return m.DebugUrls + } + return nil +} + +func (m *DebugTensorWatch) GetTolerateDebugOpCreationFailures() bool { + if m != nil { + return m.TolerateDebugOpCreationFailures + } + return false +} + +// Options for initializing DebuggerState in TensorFlow Debugger (tfdbg). +type DebugOptions struct { + // Debugging options + DebugTensorWatchOpts []*DebugTensorWatch `protobuf:"bytes,4,rep,name=debug_tensor_watch_opts,json=debugTensorWatchOpts,proto3" json:"debug_tensor_watch_opts,omitempty"` + // Caller-specified global step count. + // Note that this is distinct from the session run count and the executor + // step count. + GlobalStep int64 `protobuf:"varint,10,opt,name=global_step,json=globalStep,proto3" json:"global_step,omitempty"` + // Whether the total disk usage of tfdbg is to be reset to zero + // in this Session.run call. This is used by wrappers and hooks + // such as the local CLI ones to indicate that the dumped tensors + // are cleaned up from the disk after each Session.run. + ResetDiskByteUsage bool `protobuf:"varint,11,opt,name=reset_disk_byte_usage,json=resetDiskByteUsage,proto3" json:"reset_disk_byte_usage,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebugOptions) Reset() { *m = DebugOptions{} } +func (m *DebugOptions) String() string { return proto.CompactTextString(m) } +func (*DebugOptions) ProtoMessage() {} +func (*DebugOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_4fbf764b7c91eef6, []int{1} +} + +func (m *DebugOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebugOptions.Unmarshal(m, b) +} +func (m *DebugOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebugOptions.Marshal(b, m, deterministic) +} +func (m *DebugOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebugOptions.Merge(m, src) +} +func (m *DebugOptions) XXX_Size() int { + return xxx_messageInfo_DebugOptions.Size(m) +} +func (m *DebugOptions) XXX_DiscardUnknown() { + xxx_messageInfo_DebugOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_DebugOptions proto.InternalMessageInfo + +func (m *DebugOptions) GetDebugTensorWatchOpts() []*DebugTensorWatch { + if m != nil { + return m.DebugTensorWatchOpts + } + return nil +} + +func (m *DebugOptions) GetGlobalStep() int64 { + if m != nil { + return m.GlobalStep + } + return 0 +} + +func (m *DebugOptions) GetResetDiskByteUsage() bool { + if m != nil { + return m.ResetDiskByteUsage + } + return false +} + +type DebuggedSourceFile struct { + // The host name on which a source code file is located. + Host string `protobuf:"bytes,1,opt,name=host,proto3" json:"host,omitempty"` + // Path to the source code file. + FilePath string `protobuf:"bytes,2,opt,name=file_path,json=filePath,proto3" json:"file_path,omitempty"` + // The timestamp at which the source code file is last modified. + LastModified int64 `protobuf:"varint,3,opt,name=last_modified,json=lastModified,proto3" json:"last_modified,omitempty"` + // Byte size of the file. + Bytes int64 `protobuf:"varint,4,opt,name=bytes,proto3" json:"bytes,omitempty"` + // Line-by-line content of the source code file. + Lines []string `protobuf:"bytes,5,rep,name=lines,proto3" json:"lines,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebuggedSourceFile) Reset() { *m = DebuggedSourceFile{} } +func (m *DebuggedSourceFile) String() string { return proto.CompactTextString(m) } +func (*DebuggedSourceFile) ProtoMessage() {} +func (*DebuggedSourceFile) Descriptor() ([]byte, []int) { + return fileDescriptor_4fbf764b7c91eef6, []int{2} +} + +func (m *DebuggedSourceFile) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebuggedSourceFile.Unmarshal(m, b) +} +func (m *DebuggedSourceFile) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebuggedSourceFile.Marshal(b, m, deterministic) +} +func (m *DebuggedSourceFile) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebuggedSourceFile.Merge(m, src) +} +func (m *DebuggedSourceFile) XXX_Size() int { + return xxx_messageInfo_DebuggedSourceFile.Size(m) +} +func (m *DebuggedSourceFile) XXX_DiscardUnknown() { + xxx_messageInfo_DebuggedSourceFile.DiscardUnknown(m) +} + +var xxx_messageInfo_DebuggedSourceFile proto.InternalMessageInfo + +func (m *DebuggedSourceFile) GetHost() string { + if m != nil { + return m.Host + } + return "" +} + +func (m *DebuggedSourceFile) GetFilePath() string { + if m != nil { + return m.FilePath + } + return "" +} + +func (m *DebuggedSourceFile) GetLastModified() int64 { + if m != nil { + return m.LastModified + } + return 0 +} + +func (m *DebuggedSourceFile) GetBytes() int64 { + if m != nil { + return m.Bytes + } + return 0 +} + +func (m *DebuggedSourceFile) GetLines() []string { + if m != nil { + return m.Lines + } + return nil +} + +type DebuggedSourceFiles struct { + // A collection of source code files. + SourceFiles []*DebuggedSourceFile `protobuf:"bytes,1,rep,name=source_files,json=sourceFiles,proto3" json:"source_files,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebuggedSourceFiles) Reset() { *m = DebuggedSourceFiles{} } +func (m *DebuggedSourceFiles) String() string { return proto.CompactTextString(m) } +func (*DebuggedSourceFiles) ProtoMessage() {} +func (*DebuggedSourceFiles) Descriptor() ([]byte, []int) { + return fileDescriptor_4fbf764b7c91eef6, []int{3} +} + +func (m *DebuggedSourceFiles) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebuggedSourceFiles.Unmarshal(m, b) +} +func (m *DebuggedSourceFiles) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebuggedSourceFiles.Marshal(b, m, deterministic) +} +func (m *DebuggedSourceFiles) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebuggedSourceFiles.Merge(m, src) +} +func (m *DebuggedSourceFiles) XXX_Size() int { + return xxx_messageInfo_DebuggedSourceFiles.Size(m) +} +func (m *DebuggedSourceFiles) XXX_DiscardUnknown() { + xxx_messageInfo_DebuggedSourceFiles.DiscardUnknown(m) +} + +var xxx_messageInfo_DebuggedSourceFiles proto.InternalMessageInfo + +func (m *DebuggedSourceFiles) GetSourceFiles() []*DebuggedSourceFile { + if m != nil { + return m.SourceFiles + } + return nil +} + +func init() { + proto.RegisterType((*DebugTensorWatch)(nil), "tensorflow.DebugTensorWatch") + proto.RegisterType((*DebugOptions)(nil), "tensorflow.DebugOptions") + proto.RegisterType((*DebuggedSourceFile)(nil), "tensorflow.DebuggedSourceFile") + proto.RegisterType((*DebuggedSourceFiles)(nil), "tensorflow.DebuggedSourceFiles") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/debug.proto", fileDescriptor_4fbf764b7c91eef6) +} + +var fileDescriptor_4fbf764b7c91eef6 = []byte{ + // 493 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x6c, 0x93, 0xcf, 0x8e, 0x12, 0x41, + 0x10, 0xc6, 0xd3, 0xb2, 0x18, 0x68, 0x30, 0x31, 0xed, 0x1a, 0x27, 0xf1, 0xcf, 0x12, 0xd6, 0x03, + 0x27, 0x88, 0xfa, 0x04, 0x22, 0xd9, 0x93, 0xba, 0x64, 0x90, 0x68, 0xbc, 0x74, 0x7a, 0x98, 0x9a, + 0x61, 0x42, 0x43, 0x4d, 0xba, 0x6a, 0x42, 0xf6, 0xec, 0x3b, 0xf8, 0x2a, 0xbe, 0x8b, 0x4f, 0xe2, + 0xd1, 0x74, 0x37, 0x1b, 0x56, 0xd6, 0x5b, 0xd5, 0xef, 0xab, 0xaa, 0xae, 0xf9, 0xba, 0x47, 0xbe, + 0x66, 0xd8, 0x11, 0xba, 0xc2, 0xe2, 0x7e, 0xb2, 0x42, 0x07, 0x93, 0xda, 0x21, 0x63, 0xd6, 0x14, + 0x93, 0x1c, 0xb2, 0xa6, 0x1c, 0x87, 0x54, 0xc9, 0x63, 0xd5, 0xf0, 0xb7, 0x90, 0x8f, 0x67, 0x5e, + 0xfb, 0x12, 0xd8, 0x57, 0xc3, 0xab, 0xb5, 0x7a, 0x2e, 0xbb, 0x3b, 0xcc, 0x41, 0xef, 0xcc, 0x16, + 0x12, 0x31, 0x10, 0xa3, 0x6e, 0xda, 0xf1, 0xe0, 0xb3, 0xd9, 0x82, 0xba, 0x90, 0x3d, 0x6c, 0xb8, + 0x6e, 0x58, 0x93, 0x45, 0x4e, 0x1e, 0x0c, 0xc4, 0xa8, 0x9d, 0xca, 0x88, 0x16, 0x16, 0xd9, 0x77, + 0x87, 0xd3, 0x34, 0xd6, 0x94, 0xb4, 0x06, 0x2d, 0xdf, 0x1d, 0xc0, 0x75, 0x4d, 0xea, 0xa5, 0x94, + 0x51, 0x6c, 0x9c, 0xa5, 0xe4, 0x2c, 0xa8, 0xb1, 0x7c, 0xe9, 0x2c, 0xa9, 0x8f, 0xf2, 0x92, 0xd1, + 0x82, 0x33, 0x0c, 0xfa, 0x76, 0x88, 0x5e, 0x39, 0x30, 0x5c, 0xe1, 0x4e, 0x17, 0xa6, 0xb2, 0x8d, + 0x03, 0x4a, 0xda, 0x03, 0x31, 0xea, 0xa4, 0x17, 0xb7, 0xa5, 0xb3, 0x38, 0xfd, 0xc3, 0xa1, 0xee, + 0xea, 0x50, 0x36, 0xfc, 0x25, 0x64, 0xff, 0xa0, 0x79, 0x4e, 0x6a, 0x21, 0x9f, 0xc5, 0xa9, 0xd1, + 0x01, 0xbd, 0xf7, 0x9f, 0xab, 0xb1, 0xe6, 0xb8, 0x4a, 0xef, 0xed, 0x8b, 0xf1, 0xd1, 0x9b, 0xf1, + 0xa9, 0x2f, 0xe9, 0x79, 0x7e, 0x42, 0xae, 0x6b, 0x26, 0x6f, 0x48, 0x69, 0x31, 0x33, 0x56, 0x13, + 0x43, 0x9d, 0xc8, 0x81, 0x18, 0xb5, 0x52, 0x19, 0xd1, 0x82, 0xa1, 0x56, 0x6f, 0xe4, 0x53, 0x07, + 0x04, 0xac, 0xf3, 0x8a, 0x36, 0x3a, 0xbb, 0x61, 0xd0, 0x0d, 0x99, 0x12, 0x92, 0x5e, 0xf8, 0x0c, + 0x15, 0xc4, 0x59, 0x45, 0x9b, 0xe9, 0x0d, 0xc3, 0xd2, 0x2b, 0xc3, 0x9f, 0x42, 0xaa, 0x70, 0x7c, + 0x09, 0xf9, 0x02, 0x1b, 0xb7, 0x82, 0xab, 0xca, 0x82, 0x52, 0xf2, 0x6c, 0x8d, 0xc4, 0x87, 0x3b, + 0x09, 0xb1, 0xb7, 0xbb, 0xa8, 0x2c, 0xe8, 0xda, 0xf0, 0x3a, 0xdc, 0x46, 0x37, 0xed, 0x78, 0x30, + 0x37, 0xbc, 0x56, 0x97, 0xf2, 0x91, 0x35, 0xc4, 0x7a, 0x8b, 0x79, 0x55, 0x54, 0x90, 0x27, 0xad, + 0xb0, 0x5d, 0xdf, 0xc3, 0x4f, 0x07, 0xa6, 0xce, 0x65, 0xdb, 0x2f, 0xe5, 0x3d, 0xf0, 0x62, 0x4c, + 0x3c, 0xb5, 0xd5, 0x2e, 0x98, 0xed, 0x2f, 0x29, 0x26, 0xc3, 0x6f, 0xf2, 0xc9, 0xfd, 0xbd, 0x48, + 0xbd, 0x97, 0x7d, 0x0a, 0xa9, 0xf6, 0x47, 0x53, 0x22, 0x82, 0x9b, 0xaf, 0xee, 0xb9, 0xf9, 0x4f, + 0x5b, 0xda, 0xa3, 0xe3, 0x88, 0xe9, 0x0f, 0x21, 0x13, 0x74, 0xe5, 0xdd, 0x96, 0xc2, 0x99, 0x2d, + 0xec, 0xd1, 0x6d, 0xa6, 0xbd, 0xd0, 0x3d, 0xf7, 0xcf, 0x97, 0xe6, 0xe2, 0xfb, 0xb2, 0xac, 0x78, + 0xdd, 0x64, 0xe3, 0x15, 0x6e, 0x27, 0x77, 0x9e, 0xfc, 0xff, 0xc3, 0x12, 0x4f, 0xfe, 0x85, 0x02, + 0x9d, 0xf6, 0x44, 0x07, 0x42, 0xba, 0xc4, 0x18, 0xfd, 0x11, 0x22, 0x7b, 0x18, 0xa2, 0x77, 0x7f, + 0x03, 0x00, 0x00, 0xff, 0xff, 0xc1, 0x4b, 0xd6, 0xe5, 0x4a, 0x03, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/debug_event.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/debug_event.pb.go new file mode 100644 index 0000000..d82bf37 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/debug_event.pb.go @@ -0,0 +1,1109 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/debug_event.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Available modes for extracting debugging information from a Tensor. +// TODO(cais): Document the detailed column names and semantics in a separate +// markdown file once the implementation settles. +type TensorDebugMode int32 + +const ( + TensorDebugMode_UNSPECIFIED TensorDebugMode = 0 + // Only records what tensors are computed, eagerly or in graphs. + // No information regarding the value of the tensor is available. + TensorDebugMode_NO_TENSOR TensorDebugMode = 1 + // A minimalist health summary for float-type tensors. + // Contains information only about the presence/absence of pathological + // values including Infinity and NaN. + // Applicable only to float dtypes. + TensorDebugMode_CURT_HEALTH TensorDebugMode = 2 + // A concise health summary for float-type tensors. + // Contains more information that CURT_HEALTH. + // Infinity and NaN are treated differently. + // Applicable only to float and integer dtypes. + TensorDebugMode_CONCISE_HEALTH TensorDebugMode = 3 + // A detailed health summary. + // Contains further detailed information than `CONCISE_HEALTH`. + // Information about device, dtype and shape are included. + // Counts for various types of values (Infinity, NaN, negative, zero, + // positive) are included. + // Applicable to float, integer and boolean dtypes. + TensorDebugMode_FULL_HEALTH TensorDebugMode = 4 + // Provides full runtime shape information, up to a maximum rank, beyond + // which the dimension sizes are truncated. + TensorDebugMode_SHAPE TensorDebugMode = 5 + // Full numeric summary. + // Including device, dtype, shape, counts of various types of values + // (Infinity, NaN, negative, zero, positive), and summary statistics + // (minimum, maximum, mean and variance). + // Applicable to float, integer and boolean dtypes. + TensorDebugMode_FULL_NUMERICS TensorDebugMode = 6 + // Full tensor value. + TensorDebugMode_FULL_TENSOR TensorDebugMode = 7 + // Reduce the elements of a tensor to a rank-1 tensor of shape [3], in which + // - the 1st element is -inf if any element of the tensor is -inf, + // or zero otherwise. + // - the 2nd element is +inf if any element of the tensor is +inf, + // or zero otherwise. + // - the 3rd element is nan if any element of the tensor is nan, or zero + // otherwise. + TensorDebugMode_REDUCE_INF_NAN_THREE_SLOTS TensorDebugMode = 8 +) + +var TensorDebugMode_name = map[int32]string{ + 0: "UNSPECIFIED", + 1: "NO_TENSOR", + 2: "CURT_HEALTH", + 3: "CONCISE_HEALTH", + 4: "FULL_HEALTH", + 5: "SHAPE", + 6: "FULL_NUMERICS", + 7: "FULL_TENSOR", + 8: "REDUCE_INF_NAN_THREE_SLOTS", +} + +var TensorDebugMode_value = map[string]int32{ + "UNSPECIFIED": 0, + "NO_TENSOR": 1, + "CURT_HEALTH": 2, + "CONCISE_HEALTH": 3, + "FULL_HEALTH": 4, + "SHAPE": 5, + "FULL_NUMERICS": 6, + "FULL_TENSOR": 7, + "REDUCE_INF_NAN_THREE_SLOTS": 8, +} + +func (x TensorDebugMode) String() string { + return proto.EnumName(TensorDebugMode_name, int32(x)) +} + +func (TensorDebugMode) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{0} +} + +// An Event related to the debugging of a TensorFlow program. +type DebugEvent struct { + // Timestamp in seconds (with microsecond precision). + WallTime float64 `protobuf:"fixed64,1,opt,name=wall_time,json=wallTime,proto3" json:"wall_time,omitempty"` + // Step of training (if available). + Step int64 `protobuf:"varint,2,opt,name=step,proto3" json:"step,omitempty"` + // Types that are valid to be assigned to What: + // *DebugEvent_DebugMetadata + // *DebugEvent_SourceFile + // *DebugEvent_StackFrameWithId + // *DebugEvent_GraphOpCreation + // *DebugEvent_DebuggedGraph + // *DebugEvent_Execution + // *DebugEvent_GraphExecutionTrace + // *DebugEvent_GraphId + // *DebugEvent_DebuggedDevice + What isDebugEvent_What `protobuf_oneof:"what"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebugEvent) Reset() { *m = DebugEvent{} } +func (m *DebugEvent) String() string { return proto.CompactTextString(m) } +func (*DebugEvent) ProtoMessage() {} +func (*DebugEvent) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{0} +} + +func (m *DebugEvent) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebugEvent.Unmarshal(m, b) +} +func (m *DebugEvent) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebugEvent.Marshal(b, m, deterministic) +} +func (m *DebugEvent) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebugEvent.Merge(m, src) +} +func (m *DebugEvent) XXX_Size() int { + return xxx_messageInfo_DebugEvent.Size(m) +} +func (m *DebugEvent) XXX_DiscardUnknown() { + xxx_messageInfo_DebugEvent.DiscardUnknown(m) +} + +var xxx_messageInfo_DebugEvent proto.InternalMessageInfo + +func (m *DebugEvent) GetWallTime() float64 { + if m != nil { + return m.WallTime + } + return 0 +} + +func (m *DebugEvent) GetStep() int64 { + if m != nil { + return m.Step + } + return 0 +} + +type isDebugEvent_What interface { + isDebugEvent_What() +} + +type DebugEvent_DebugMetadata struct { + DebugMetadata *DebugMetadata `protobuf:"bytes,3,opt,name=debug_metadata,json=debugMetadata,proto3,oneof"` +} + +type DebugEvent_SourceFile struct { + SourceFile *SourceFile `protobuf:"bytes,4,opt,name=source_file,json=sourceFile,proto3,oneof"` +} + +type DebugEvent_StackFrameWithId struct { + StackFrameWithId *StackFrameWithId `protobuf:"bytes,6,opt,name=stack_frame_with_id,json=stackFrameWithId,proto3,oneof"` +} + +type DebugEvent_GraphOpCreation struct { + GraphOpCreation *GraphOpCreation `protobuf:"bytes,7,opt,name=graph_op_creation,json=graphOpCreation,proto3,oneof"` +} + +type DebugEvent_DebuggedGraph struct { + DebuggedGraph *DebuggedGraph `protobuf:"bytes,8,opt,name=debugged_graph,json=debuggedGraph,proto3,oneof"` +} + +type DebugEvent_Execution struct { + Execution *Execution `protobuf:"bytes,9,opt,name=execution,proto3,oneof"` +} + +type DebugEvent_GraphExecutionTrace struct { + GraphExecutionTrace *GraphExecutionTrace `protobuf:"bytes,10,opt,name=graph_execution_trace,json=graphExecutionTrace,proto3,oneof"` +} + +type DebugEvent_GraphId struct { + GraphId string `protobuf:"bytes,11,opt,name=graph_id,json=graphId,proto3,oneof"` +} + +type DebugEvent_DebuggedDevice struct { + DebuggedDevice *DebuggedDevice `protobuf:"bytes,12,opt,name=debugged_device,json=debuggedDevice,proto3,oneof"` +} + +func (*DebugEvent_DebugMetadata) isDebugEvent_What() {} + +func (*DebugEvent_SourceFile) isDebugEvent_What() {} + +func (*DebugEvent_StackFrameWithId) isDebugEvent_What() {} + +func (*DebugEvent_GraphOpCreation) isDebugEvent_What() {} + +func (*DebugEvent_DebuggedGraph) isDebugEvent_What() {} + +func (*DebugEvent_Execution) isDebugEvent_What() {} + +func (*DebugEvent_GraphExecutionTrace) isDebugEvent_What() {} + +func (*DebugEvent_GraphId) isDebugEvent_What() {} + +func (*DebugEvent_DebuggedDevice) isDebugEvent_What() {} + +func (m *DebugEvent) GetWhat() isDebugEvent_What { + if m != nil { + return m.What + } + return nil +} + +func (m *DebugEvent) GetDebugMetadata() *DebugMetadata { + if x, ok := m.GetWhat().(*DebugEvent_DebugMetadata); ok { + return x.DebugMetadata + } + return nil +} + +func (m *DebugEvent) GetSourceFile() *SourceFile { + if x, ok := m.GetWhat().(*DebugEvent_SourceFile); ok { + return x.SourceFile + } + return nil +} + +func (m *DebugEvent) GetStackFrameWithId() *StackFrameWithId { + if x, ok := m.GetWhat().(*DebugEvent_StackFrameWithId); ok { + return x.StackFrameWithId + } + return nil +} + +func (m *DebugEvent) GetGraphOpCreation() *GraphOpCreation { + if x, ok := m.GetWhat().(*DebugEvent_GraphOpCreation); ok { + return x.GraphOpCreation + } + return nil +} + +func (m *DebugEvent) GetDebuggedGraph() *DebuggedGraph { + if x, ok := m.GetWhat().(*DebugEvent_DebuggedGraph); ok { + return x.DebuggedGraph + } + return nil +} + +func (m *DebugEvent) GetExecution() *Execution { + if x, ok := m.GetWhat().(*DebugEvent_Execution); ok { + return x.Execution + } + return nil +} + +func (m *DebugEvent) GetGraphExecutionTrace() *GraphExecutionTrace { + if x, ok := m.GetWhat().(*DebugEvent_GraphExecutionTrace); ok { + return x.GraphExecutionTrace + } + return nil +} + +func (m *DebugEvent) GetGraphId() string { + if x, ok := m.GetWhat().(*DebugEvent_GraphId); ok { + return x.GraphId + } + return "" +} + +func (m *DebugEvent) GetDebuggedDevice() *DebuggedDevice { + if x, ok := m.GetWhat().(*DebugEvent_DebuggedDevice); ok { + return x.DebuggedDevice + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*DebugEvent) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*DebugEvent_DebugMetadata)(nil), + (*DebugEvent_SourceFile)(nil), + (*DebugEvent_StackFrameWithId)(nil), + (*DebugEvent_GraphOpCreation)(nil), + (*DebugEvent_DebuggedGraph)(nil), + (*DebugEvent_Execution)(nil), + (*DebugEvent_GraphExecutionTrace)(nil), + (*DebugEvent_GraphId)(nil), + (*DebugEvent_DebuggedDevice)(nil), + } +} + +// Metadata about the debugger and the debugged TensorFlow program. +type DebugMetadata struct { + // Version of TensorFlow. + TensorflowVersion string `protobuf:"bytes,1,opt,name=tensorflow_version,json=tensorflowVersion,proto3" json:"tensorflow_version,omitempty"` + // Version of the DebugEvent file format. + // Has a format of "debug.Event:", e.g., "debug.Event:1". + FileVersion string `protobuf:"bytes,2,opt,name=file_version,json=fileVersion,proto3" json:"file_version,omitempty"` + // A unique ID for the current run of tfdbg. + // A run of tfdbg is defined as a TensorFlow job instrumented by tfdbg. + // Multiple hosts in a distributed TensorFlow job instrumented by tfdbg + // have the same ID. + TfdbgRunId string `protobuf:"bytes,3,opt,name=tfdbg_run_id,json=tfdbgRunId,proto3" json:"tfdbg_run_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebugMetadata) Reset() { *m = DebugMetadata{} } +func (m *DebugMetadata) String() string { return proto.CompactTextString(m) } +func (*DebugMetadata) ProtoMessage() {} +func (*DebugMetadata) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{1} +} + +func (m *DebugMetadata) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebugMetadata.Unmarshal(m, b) +} +func (m *DebugMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebugMetadata.Marshal(b, m, deterministic) +} +func (m *DebugMetadata) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebugMetadata.Merge(m, src) +} +func (m *DebugMetadata) XXX_Size() int { + return xxx_messageInfo_DebugMetadata.Size(m) +} +func (m *DebugMetadata) XXX_DiscardUnknown() { + xxx_messageInfo_DebugMetadata.DiscardUnknown(m) +} + +var xxx_messageInfo_DebugMetadata proto.InternalMessageInfo + +func (m *DebugMetadata) GetTensorflowVersion() string { + if m != nil { + return m.TensorflowVersion + } + return "" +} + +func (m *DebugMetadata) GetFileVersion() string { + if m != nil { + return m.FileVersion + } + return "" +} + +func (m *DebugMetadata) GetTfdbgRunId() string { + if m != nil { + return m.TfdbgRunId + } + return "" +} + +// Content of a source file involved in the execution of the debugged TensorFlow +// program. +type SourceFile struct { + // Path to the file. + FilePath string `protobuf:"bytes,1,opt,name=file_path,json=filePath,proto3" json:"file_path,omitempty"` + // Name of the host on which the file is located. + HostName string `protobuf:"bytes,2,opt,name=host_name,json=hostName,proto3" json:"host_name,omitempty"` + // Line-by-line content of the file. + Lines []string `protobuf:"bytes,3,rep,name=lines,proto3" json:"lines,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SourceFile) Reset() { *m = SourceFile{} } +func (m *SourceFile) String() string { return proto.CompactTextString(m) } +func (*SourceFile) ProtoMessage() {} +func (*SourceFile) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{2} +} + +func (m *SourceFile) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SourceFile.Unmarshal(m, b) +} +func (m *SourceFile) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SourceFile.Marshal(b, m, deterministic) +} +func (m *SourceFile) XXX_Merge(src proto.Message) { + xxx_messageInfo_SourceFile.Merge(m, src) +} +func (m *SourceFile) XXX_Size() int { + return xxx_messageInfo_SourceFile.Size(m) +} +func (m *SourceFile) XXX_DiscardUnknown() { + xxx_messageInfo_SourceFile.DiscardUnknown(m) +} + +var xxx_messageInfo_SourceFile proto.InternalMessageInfo + +func (m *SourceFile) GetFilePath() string { + if m != nil { + return m.FilePath + } + return "" +} + +func (m *SourceFile) GetHostName() string { + if m != nil { + return m.HostName + } + return "" +} + +func (m *SourceFile) GetLines() []string { + if m != nil { + return m.Lines + } + return nil +} + +// A stack frame with ID. +type StackFrameWithId struct { + // A unique ID for the stack frame: A UUID-like string. + Id string `protobuf:"bytes,1,opt,name=id,proto3" json:"id,omitempty"` + // Stack frame, i.e., a frame of a stack trace, containing information + // regarding the file name, line number, function name, code content + // of the line, and column number (if available). + FileLineCol *GraphDebugInfo_FileLineCol `protobuf:"bytes,2,opt,name=file_line_col,json=fileLineCol,proto3" json:"file_line_col,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *StackFrameWithId) Reset() { *m = StackFrameWithId{} } +func (m *StackFrameWithId) String() string { return proto.CompactTextString(m) } +func (*StackFrameWithId) ProtoMessage() {} +func (*StackFrameWithId) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{3} +} + +func (m *StackFrameWithId) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_StackFrameWithId.Unmarshal(m, b) +} +func (m *StackFrameWithId) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_StackFrameWithId.Marshal(b, m, deterministic) +} +func (m *StackFrameWithId) XXX_Merge(src proto.Message) { + xxx_messageInfo_StackFrameWithId.Merge(m, src) +} +func (m *StackFrameWithId) XXX_Size() int { + return xxx_messageInfo_StackFrameWithId.Size(m) +} +func (m *StackFrameWithId) XXX_DiscardUnknown() { + xxx_messageInfo_StackFrameWithId.DiscardUnknown(m) +} + +var xxx_messageInfo_StackFrameWithId proto.InternalMessageInfo + +func (m *StackFrameWithId) GetId() string { + if m != nil { + return m.Id + } + return "" +} + +func (m *StackFrameWithId) GetFileLineCol() *GraphDebugInfo_FileLineCol { + if m != nil { + return m.FileLineCol + } + return nil +} + +// Code location information: A stack trace with host-name information. +// Instead of encoding the detailed stack trace, this proto refers to IDs of +// stack frames stored as `StackFrameWithId` protos. +type CodeLocation struct { + // Host name on which the source files are located. + HostName string `protobuf:"bytes,1,opt,name=host_name,json=hostName,proto3" json:"host_name,omitempty"` + // ID to a stack frame, each of which is pointed to + // by a unique ID. The ordering of the frames is consistent with Python's + // `traceback.extract_tb()`. + StackFrameIds []string `protobuf:"bytes,2,rep,name=stack_frame_ids,json=stackFrameIds,proto3" json:"stack_frame_ids,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CodeLocation) Reset() { *m = CodeLocation{} } +func (m *CodeLocation) String() string { return proto.CompactTextString(m) } +func (*CodeLocation) ProtoMessage() {} +func (*CodeLocation) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{4} +} + +func (m *CodeLocation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CodeLocation.Unmarshal(m, b) +} +func (m *CodeLocation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CodeLocation.Marshal(b, m, deterministic) +} +func (m *CodeLocation) XXX_Merge(src proto.Message) { + xxx_messageInfo_CodeLocation.Merge(m, src) +} +func (m *CodeLocation) XXX_Size() int { + return xxx_messageInfo_CodeLocation.Size(m) +} +func (m *CodeLocation) XXX_DiscardUnknown() { + xxx_messageInfo_CodeLocation.DiscardUnknown(m) +} + +var xxx_messageInfo_CodeLocation proto.InternalMessageInfo + +func (m *CodeLocation) GetHostName() string { + if m != nil { + return m.HostName + } + return "" +} + +func (m *CodeLocation) GetStackFrameIds() []string { + if m != nil { + return m.StackFrameIds + } + return nil +} + +// The creation of an op in a TensorFlow Graph (e.g., FuncGraph in TF2). +type GraphOpCreation struct { + // Type of the op (e.g., "MatMul"). + OpType string `protobuf:"bytes,1,opt,name=op_type,json=opType,proto3" json:"op_type,omitempty"` + // Name of the op (e.g., "Dense/MatMul_1"). + OpName string `protobuf:"bytes,2,opt,name=op_name,json=opName,proto3" json:"op_name,omitempty"` + // Name of the graph that the op is a part of (if available). + GraphName string `protobuf:"bytes,3,opt,name=graph_name,json=graphName,proto3" json:"graph_name,omitempty"` + // Unique ID of the graph (generated by debugger). + // This is the ID of the immediately-enclosing graph. + GraphId string `protobuf:"bytes,4,opt,name=graph_id,json=graphId,proto3" json:"graph_id,omitempty"` + // Name of the device that the op is assigned to (if available). + DeviceName string `protobuf:"bytes,5,opt,name=device_name,json=deviceName,proto3" json:"device_name,omitempty"` + // Names of the input tensors to the op. + InputNames []string `protobuf:"bytes,6,rep,name=input_names,json=inputNames,proto3" json:"input_names,omitempty"` + // Number of output tensors emitted by the op. + NumOutputs int32 `protobuf:"varint,7,opt,name=num_outputs,json=numOutputs,proto3" json:"num_outputs,omitempty"` + // The unique ID for code location (stack trace) of the op's creation. + CodeLocation *CodeLocation `protobuf:"bytes,8,opt,name=code_location,json=codeLocation,proto3" json:"code_location,omitempty"` + // Unique IDs for the output tensors of this op. + OutputTensorIds []int32 `protobuf:"varint,9,rep,packed,name=output_tensor_ids,json=outputTensorIds,proto3" json:"output_tensor_ids,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphOpCreation) Reset() { *m = GraphOpCreation{} } +func (m *GraphOpCreation) String() string { return proto.CompactTextString(m) } +func (*GraphOpCreation) ProtoMessage() {} +func (*GraphOpCreation) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{5} +} + +func (m *GraphOpCreation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphOpCreation.Unmarshal(m, b) +} +func (m *GraphOpCreation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphOpCreation.Marshal(b, m, deterministic) +} +func (m *GraphOpCreation) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphOpCreation.Merge(m, src) +} +func (m *GraphOpCreation) XXX_Size() int { + return xxx_messageInfo_GraphOpCreation.Size(m) +} +func (m *GraphOpCreation) XXX_DiscardUnknown() { + xxx_messageInfo_GraphOpCreation.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphOpCreation proto.InternalMessageInfo + +func (m *GraphOpCreation) GetOpType() string { + if m != nil { + return m.OpType + } + return "" +} + +func (m *GraphOpCreation) GetOpName() string { + if m != nil { + return m.OpName + } + return "" +} + +func (m *GraphOpCreation) GetGraphName() string { + if m != nil { + return m.GraphName + } + return "" +} + +func (m *GraphOpCreation) GetGraphId() string { + if m != nil { + return m.GraphId + } + return "" +} + +func (m *GraphOpCreation) GetDeviceName() string { + if m != nil { + return m.DeviceName + } + return "" +} + +func (m *GraphOpCreation) GetInputNames() []string { + if m != nil { + return m.InputNames + } + return nil +} + +func (m *GraphOpCreation) GetNumOutputs() int32 { + if m != nil { + return m.NumOutputs + } + return 0 +} + +func (m *GraphOpCreation) GetCodeLocation() *CodeLocation { + if m != nil { + return m.CodeLocation + } + return nil +} + +func (m *GraphOpCreation) GetOutputTensorIds() []int32 { + if m != nil { + return m.OutputTensorIds + } + return nil +} + +// A debugger-instrumented graph. +type DebuggedGraph struct { + // An ID for the graph. + // This can be used up to look up graph names. Generated by the debugger. + GraphId string `protobuf:"bytes,1,opt,name=graph_id,json=graphId,proto3" json:"graph_id,omitempty"` + // Name of the graph (if available). + GraphName string `protobuf:"bytes,2,opt,name=graph_name,json=graphName,proto3" json:"graph_name,omitempty"` + // Names of the instrumented ops. This can be used to look up op name + // based on the numeric-summary tensors (2nd column). + InstrumentedOps []string `protobuf:"bytes,3,rep,name=instrumented_ops,json=instrumentedOps,proto3" json:"instrumented_ops,omitempty"` + // Original (uninstrumented) GraphDef (if available). + OriginalGraphDef []byte `protobuf:"bytes,4,opt,name=original_graph_def,json=originalGraphDef,proto3" json:"original_graph_def,omitempty"` + // An encoded version of a GraphDef. + // This graph may include the debugger-inserted ops. + InstrumentedGraphDef []byte `protobuf:"bytes,5,opt,name=instrumented_graph_def,json=instrumentedGraphDef,proto3" json:"instrumented_graph_def,omitempty"` + // IDs of the immediate enclosing context (graph), if any. + OuterContextId string `protobuf:"bytes,6,opt,name=outer_context_id,json=outerContextId,proto3" json:"outer_context_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebuggedGraph) Reset() { *m = DebuggedGraph{} } +func (m *DebuggedGraph) String() string { return proto.CompactTextString(m) } +func (*DebuggedGraph) ProtoMessage() {} +func (*DebuggedGraph) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{6} +} + +func (m *DebuggedGraph) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebuggedGraph.Unmarshal(m, b) +} +func (m *DebuggedGraph) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebuggedGraph.Marshal(b, m, deterministic) +} +func (m *DebuggedGraph) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebuggedGraph.Merge(m, src) +} +func (m *DebuggedGraph) XXX_Size() int { + return xxx_messageInfo_DebuggedGraph.Size(m) +} +func (m *DebuggedGraph) XXX_DiscardUnknown() { + xxx_messageInfo_DebuggedGraph.DiscardUnknown(m) +} + +var xxx_messageInfo_DebuggedGraph proto.InternalMessageInfo + +func (m *DebuggedGraph) GetGraphId() string { + if m != nil { + return m.GraphId + } + return "" +} + +func (m *DebuggedGraph) GetGraphName() string { + if m != nil { + return m.GraphName + } + return "" +} + +func (m *DebuggedGraph) GetInstrumentedOps() []string { + if m != nil { + return m.InstrumentedOps + } + return nil +} + +func (m *DebuggedGraph) GetOriginalGraphDef() []byte { + if m != nil { + return m.OriginalGraphDef + } + return nil +} + +func (m *DebuggedGraph) GetInstrumentedGraphDef() []byte { + if m != nil { + return m.InstrumentedGraphDef + } + return nil +} + +func (m *DebuggedGraph) GetOuterContextId() string { + if m != nil { + return m.OuterContextId + } + return "" +} + +// A device on which ops and/or tensors are instrumented by the debugger. +type DebuggedDevice struct { + // Name of the device. + DeviceName string `protobuf:"bytes,1,opt,name=device_name,json=deviceName,proto3" json:"device_name,omitempty"` + // A debugger-generated ID for the device. Guaranteed to be unique within + // the scope of the debugged TensorFlow program, including single-host and + // multi-host settings. + // TODO(cais): Test the uniqueness guarantee in multi-host settings. + DeviceId int32 `protobuf:"varint,2,opt,name=device_id,json=deviceId,proto3" json:"device_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DebuggedDevice) Reset() { *m = DebuggedDevice{} } +func (m *DebuggedDevice) String() string { return proto.CompactTextString(m) } +func (*DebuggedDevice) ProtoMessage() {} +func (*DebuggedDevice) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{7} +} + +func (m *DebuggedDevice) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DebuggedDevice.Unmarshal(m, b) +} +func (m *DebuggedDevice) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DebuggedDevice.Marshal(b, m, deterministic) +} +func (m *DebuggedDevice) XXX_Merge(src proto.Message) { + xxx_messageInfo_DebuggedDevice.Merge(m, src) +} +func (m *DebuggedDevice) XXX_Size() int { + return xxx_messageInfo_DebuggedDevice.Size(m) +} +func (m *DebuggedDevice) XXX_DiscardUnknown() { + xxx_messageInfo_DebuggedDevice.DiscardUnknown(m) +} + +var xxx_messageInfo_DebuggedDevice proto.InternalMessageInfo + +func (m *DebuggedDevice) GetDeviceName() string { + if m != nil { + return m.DeviceName + } + return "" +} + +func (m *DebuggedDevice) GetDeviceId() int32 { + if m != nil { + return m.DeviceId + } + return 0 +} + +// Data relating to the eager execution of an op or a Graph. +// For a op that generates N output tensors (N >= 0), only one +// Execution proto will be used to describe the execution event. +type Execution struct { + // Op type (e.g., "MatMul"). + // In the case of a Graph, this is the name of the Graph. + OpType string `protobuf:"bytes,1,opt,name=op_type,json=opType,proto3" json:"op_type,omitempty"` + // Number of output tensors. + NumOutputs int32 `protobuf:"varint,2,opt,name=num_outputs,json=numOutputs,proto3" json:"num_outputs,omitempty"` + // The graph that's executed: applicable only to the eager + // execution of a FuncGraph. + GraphId string `protobuf:"bytes,3,opt,name=graph_id,json=graphId,proto3" json:"graph_id,omitempty"` + // IDs of the input tensors (if available). + InputTensorIds []int64 `protobuf:"varint,4,rep,packed,name=input_tensor_ids,json=inputTensorIds,proto3" json:"input_tensor_ids,omitempty"` + // IDs of the output tensors (if availbable). + // If specified, must have the same length as tensor_protos. + OutputTensorIds []int64 `protobuf:"varint,5,rep,packed,name=output_tensor_ids,json=outputTensorIds,proto3" json:"output_tensor_ids,omitempty"` + // Type of the tensor value encapsulated in this proto. + TensorDebugMode TensorDebugMode `protobuf:"varint,6,opt,name=tensor_debug_mode,json=tensorDebugMode,proto3,enum=tensorflow.TensorDebugMode" json:"tensor_debug_mode,omitempty"` + // Output Tensor values in the type described by `tensor_value_type`. + // The length of this should match `num_outputs`. + TensorProtos []*tensor_go_proto.TensorProto `protobuf:"bytes,7,rep,name=tensor_protos,json=tensorProtos,proto3" json:"tensor_protos,omitempty"` + // Stack trace of the eager execution. + CodeLocation *CodeLocation `protobuf:"bytes,8,opt,name=code_location,json=codeLocation,proto3" json:"code_location,omitempty"` + // Debugged-generated IDs of the devices on which the output tensors reside. + // To look up details about the device (e.g., name), cross-reference this + // field with the DebuggedDevice messages. + OutputTensorDeviceIds []int32 `protobuf:"varint,9,rep,packed,name=output_tensor_device_ids,json=outputTensorDeviceIds,proto3" json:"output_tensor_device_ids,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Execution) Reset() { *m = Execution{} } +func (m *Execution) String() string { return proto.CompactTextString(m) } +func (*Execution) ProtoMessage() {} +func (*Execution) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{8} +} + +func (m *Execution) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Execution.Unmarshal(m, b) +} +func (m *Execution) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Execution.Marshal(b, m, deterministic) +} +func (m *Execution) XXX_Merge(src proto.Message) { + xxx_messageInfo_Execution.Merge(m, src) +} +func (m *Execution) XXX_Size() int { + return xxx_messageInfo_Execution.Size(m) +} +func (m *Execution) XXX_DiscardUnknown() { + xxx_messageInfo_Execution.DiscardUnknown(m) +} + +var xxx_messageInfo_Execution proto.InternalMessageInfo + +func (m *Execution) GetOpType() string { + if m != nil { + return m.OpType + } + return "" +} + +func (m *Execution) GetNumOutputs() int32 { + if m != nil { + return m.NumOutputs + } + return 0 +} + +func (m *Execution) GetGraphId() string { + if m != nil { + return m.GraphId + } + return "" +} + +func (m *Execution) GetInputTensorIds() []int64 { + if m != nil { + return m.InputTensorIds + } + return nil +} + +func (m *Execution) GetOutputTensorIds() []int64 { + if m != nil { + return m.OutputTensorIds + } + return nil +} + +func (m *Execution) GetTensorDebugMode() TensorDebugMode { + if m != nil { + return m.TensorDebugMode + } + return TensorDebugMode_UNSPECIFIED +} + +func (m *Execution) GetTensorProtos() []*tensor_go_proto.TensorProto { + if m != nil { + return m.TensorProtos + } + return nil +} + +func (m *Execution) GetCodeLocation() *CodeLocation { + if m != nil { + return m.CodeLocation + } + return nil +} + +func (m *Execution) GetOutputTensorDeviceIds() []int32 { + if m != nil { + return m.OutputTensorDeviceIds + } + return nil +} + +// Data relating to an execution of a Graph (e.g., an eager execution of a +// FuncGraph). +// The values of the intermediate tensors computed in the graph are recorded +// in this proto. A graph execution may correspond to one or more pieces of +// `GraphExecutionTrace`, depending on whether the instrumented tensor values +// are summarized in an aggregated or separate fashion. +type GraphExecutionTrace struct { + // Unique ID of the context that the executed op(s) belong to (e.g., a + // compiled concrete tf.function). + TfdbgContextId string `protobuf:"bytes,1,opt,name=tfdbg_context_id,json=tfdbgContextId,proto3" json:"tfdbg_context_id,omitempty"` + // Name of the op (applicable only in the case of the `FULL_TENSOR` trace + // level). + OpName string `protobuf:"bytes,2,opt,name=op_name,json=opName,proto3" json:"op_name,omitempty"` + // Output slot of the tensor (applicable only in the case of the `FULL_TENSOR` + // trace level). + OutputSlot int32 `protobuf:"varint,3,opt,name=output_slot,json=outputSlot,proto3" json:"output_slot,omitempty"` + // Type of the tensor value encapsulated in this proto. + TensorDebugMode TensorDebugMode `protobuf:"varint,4,opt,name=tensor_debug_mode,json=tensorDebugMode,proto3,enum=tensorflow.TensorDebugMode" json:"tensor_debug_mode,omitempty"` + // Tensor value in the type described by `tensor_value_type`. + // This tensor may summarize the value of a single intermediate op of the + // graph, or those of multiple intermediate tensors. + TensorProto *tensor_go_proto.TensorProto `protobuf:"bytes,5,opt,name=tensor_proto,json=tensorProto,proto3" json:"tensor_proto,omitempty"` + // Name of the device that the op belongs to. + DeviceName string `protobuf:"bytes,6,opt,name=device_name,json=deviceName,proto3" json:"device_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphExecutionTrace) Reset() { *m = GraphExecutionTrace{} } +func (m *GraphExecutionTrace) String() string { return proto.CompactTextString(m) } +func (*GraphExecutionTrace) ProtoMessage() {} +func (*GraphExecutionTrace) Descriptor() ([]byte, []int) { + return fileDescriptor_ff023fe0e3927cdd, []int{9} +} + +func (m *GraphExecutionTrace) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphExecutionTrace.Unmarshal(m, b) +} +func (m *GraphExecutionTrace) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphExecutionTrace.Marshal(b, m, deterministic) +} +func (m *GraphExecutionTrace) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphExecutionTrace.Merge(m, src) +} +func (m *GraphExecutionTrace) XXX_Size() int { + return xxx_messageInfo_GraphExecutionTrace.Size(m) +} +func (m *GraphExecutionTrace) XXX_DiscardUnknown() { + xxx_messageInfo_GraphExecutionTrace.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphExecutionTrace proto.InternalMessageInfo + +func (m *GraphExecutionTrace) GetTfdbgContextId() string { + if m != nil { + return m.TfdbgContextId + } + return "" +} + +func (m *GraphExecutionTrace) GetOpName() string { + if m != nil { + return m.OpName + } + return "" +} + +func (m *GraphExecutionTrace) GetOutputSlot() int32 { + if m != nil { + return m.OutputSlot + } + return 0 +} + +func (m *GraphExecutionTrace) GetTensorDebugMode() TensorDebugMode { + if m != nil { + return m.TensorDebugMode + } + return TensorDebugMode_UNSPECIFIED +} + +func (m *GraphExecutionTrace) GetTensorProto() *tensor_go_proto.TensorProto { + if m != nil { + return m.TensorProto + } + return nil +} + +func (m *GraphExecutionTrace) GetDeviceName() string { + if m != nil { + return m.DeviceName + } + return "" +} + +func init() { + proto.RegisterEnum("tensorflow.TensorDebugMode", TensorDebugMode_name, TensorDebugMode_value) + proto.RegisterType((*DebugEvent)(nil), "tensorflow.DebugEvent") + proto.RegisterType((*DebugMetadata)(nil), "tensorflow.DebugMetadata") + proto.RegisterType((*SourceFile)(nil), "tensorflow.SourceFile") + proto.RegisterType((*StackFrameWithId)(nil), "tensorflow.StackFrameWithId") + proto.RegisterType((*CodeLocation)(nil), "tensorflow.CodeLocation") + proto.RegisterType((*GraphOpCreation)(nil), "tensorflow.GraphOpCreation") + proto.RegisterType((*DebuggedGraph)(nil), "tensorflow.DebuggedGraph") + proto.RegisterType((*DebuggedDevice)(nil), "tensorflow.DebuggedDevice") + proto.RegisterType((*Execution)(nil), "tensorflow.Execution") + proto.RegisterType((*GraphExecutionTrace)(nil), "tensorflow.GraphExecutionTrace") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/debug_event.proto", fileDescriptor_ff023fe0e3927cdd) +} + +var fileDescriptor_ff023fe0e3927cdd = []byte{ + // 1251 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xa4, 0x56, 0x5d, 0x6f, 0xe3, 0x44, + 0x17, 0x8e, 0xf3, 0xd5, 0xe4, 0xe4, 0xb3, 0xd3, 0xfd, 0xf0, 0xb6, 0xef, 0x4b, 0x43, 0x2e, 0x56, + 0x61, 0x05, 0xad, 0xb4, 0x80, 0x10, 0x08, 0x2e, 0xb6, 0xa9, 0xbb, 0x31, 0xea, 0x26, 0xd5, 0x24, + 0x01, 0x09, 0x21, 0x8d, 0x5c, 0x7b, 0x92, 0x58, 0xeb, 0x78, 0x2c, 0x7b, 0xdc, 0xee, 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DO NOT EDIT. +// source: tensorflow/core/protobuf/device_filters.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Defines the device filters for a remote task. +type TaskDeviceFilters struct { + DeviceFilters []string `protobuf:"bytes,1,rep,name=device_filters,json=deviceFilters,proto3" json:"device_filters,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TaskDeviceFilters) Reset() { *m = TaskDeviceFilters{} } +func (m *TaskDeviceFilters) String() string { return proto.CompactTextString(m) } +func (*TaskDeviceFilters) ProtoMessage() {} +func (*TaskDeviceFilters) Descriptor() ([]byte, []int) { + return fileDescriptor_48fe7fa73e9da3a6, []int{0} +} + +func (m *TaskDeviceFilters) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TaskDeviceFilters.Unmarshal(m, b) +} +func (m *TaskDeviceFilters) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TaskDeviceFilters.Marshal(b, m, deterministic) +} +func (m *TaskDeviceFilters) XXX_Merge(src proto.Message) { + xxx_messageInfo_TaskDeviceFilters.Merge(m, src) +} +func (m *TaskDeviceFilters) XXX_Size() int { + return xxx_messageInfo_TaskDeviceFilters.Size(m) +} +func (m *TaskDeviceFilters) XXX_DiscardUnknown() { + xxx_messageInfo_TaskDeviceFilters.DiscardUnknown(m) +} + +var xxx_messageInfo_TaskDeviceFilters proto.InternalMessageInfo + +func (m *TaskDeviceFilters) GetDeviceFilters() []string { + if m != nil { + return m.DeviceFilters + } + return nil +} + +// Defines the device filters for tasks in a job. +type JobDeviceFilters struct { + // The name of this job. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // Mapping from task ID to task device filters. + Tasks map[int32]*TaskDeviceFilters `protobuf:"bytes,2,rep,name=tasks,proto3" json:"tasks,omitempty" protobuf_key:"varint,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *JobDeviceFilters) Reset() { *m = JobDeviceFilters{} } +func (m *JobDeviceFilters) String() string { return proto.CompactTextString(m) } +func (*JobDeviceFilters) ProtoMessage() {} +func (*JobDeviceFilters) Descriptor() ([]byte, []int) { + return fileDescriptor_48fe7fa73e9da3a6, []int{1} +} + +func (m *JobDeviceFilters) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_JobDeviceFilters.Unmarshal(m, b) +} +func (m *JobDeviceFilters) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_JobDeviceFilters.Marshal(b, m, deterministic) +} +func (m *JobDeviceFilters) XXX_Merge(src proto.Message) { + xxx_messageInfo_JobDeviceFilters.Merge(m, src) +} +func (m *JobDeviceFilters) XXX_Size() int { + return xxx_messageInfo_JobDeviceFilters.Size(m) +} +func (m *JobDeviceFilters) XXX_DiscardUnknown() { + xxx_messageInfo_JobDeviceFilters.DiscardUnknown(m) +} + +var xxx_messageInfo_JobDeviceFilters proto.InternalMessageInfo + +func (m *JobDeviceFilters) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *JobDeviceFilters) GetTasks() map[int32]*TaskDeviceFilters { + if m != nil { + return m.Tasks + } + return nil +} + +// Defines the device filters for jobs in a cluster. +type ClusterDeviceFilters struct { + Jobs []*JobDeviceFilters `protobuf:"bytes,1,rep,name=jobs,proto3" json:"jobs,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ClusterDeviceFilters) Reset() { *m = ClusterDeviceFilters{} } +func (m *ClusterDeviceFilters) String() string { return proto.CompactTextString(m) } +func (*ClusterDeviceFilters) ProtoMessage() {} +func (*ClusterDeviceFilters) Descriptor() ([]byte, []int) { + return fileDescriptor_48fe7fa73e9da3a6, []int{2} +} + +func (m *ClusterDeviceFilters) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ClusterDeviceFilters.Unmarshal(m, b) +} +func (m *ClusterDeviceFilters) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ClusterDeviceFilters.Marshal(b, m, deterministic) +} +func (m *ClusterDeviceFilters) XXX_Merge(src proto.Message) { + xxx_messageInfo_ClusterDeviceFilters.Merge(m, src) +} +func (m *ClusterDeviceFilters) XXX_Size() int { + return xxx_messageInfo_ClusterDeviceFilters.Size(m) +} +func (m *ClusterDeviceFilters) XXX_DiscardUnknown() { + xxx_messageInfo_ClusterDeviceFilters.DiscardUnknown(m) +} + +var xxx_messageInfo_ClusterDeviceFilters proto.InternalMessageInfo + +func (m *ClusterDeviceFilters) GetJobs() []*JobDeviceFilters { + if m != nil { + return m.Jobs + } + return nil +} + +func init() { + proto.RegisterType((*TaskDeviceFilters)(nil), "tensorflow.TaskDeviceFilters") + proto.RegisterType((*JobDeviceFilters)(nil), "tensorflow.JobDeviceFilters") + proto.RegisterMapType((map[int32]*TaskDeviceFilters)(nil), "tensorflow.JobDeviceFilters.TasksEntry") + proto.RegisterType((*ClusterDeviceFilters)(nil), "tensorflow.ClusterDeviceFilters") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/device_filters.proto", fileDescriptor_48fe7fa73e9da3a6) +} + +var fileDescriptor_48fe7fa73e9da3a6 = []byte{ + // 312 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 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0x6f, 0x0a, 0x25, + 0xb6, 0xac, 0x4e, 0x4d, 0x9f, 0x00, 0x1a, 0x93, 0x4e, 0xa0, 0x9f, 0xf2, 0xad, 0xbe, 0x3f, 0x64, + 0x95, 0xa4, 0x0b, 0x18, 0x6e, 0xbc, 0xac, 0xe4, 0x46, 0xcf, 0x24, 0xd6, 0x78, 0x7e, 0xdc, 0x3e, + 0xff, 0xa3, 0x1c, 0xab, 0xd9, 0x65, 0xef, 0x82, 0xcc, 0xee, 0xe0, 0xf0, 0x2a, 0x2b, 0xa5, 0xe2, + 0xa2, 0xdb, 0xe1, 0x1c, 0x06, 0xaf, 0xe8, 0xd7, 0xad, 0xc7, 0xf3, 0xa3, 0xbf, 0xde, 0x65, 0x9a, + 0x5c, 0x7d, 0x10, 0x98, 0xa2, 0x88, 0xdb, 0x64, 0x98, 0x48, 0x25, 0xca, 0x42, 0x25, 0x39, 0x5f, + 0x1d, 0x74, 0x32, 0xeb, 0x6a, 0x05, 0xb9, 0x26, 0xcf, 0x8f, 0x71, 0xa2, 0x5e, 0x4a, 0xdf, 0x0e, + 0x30, 0x77, 0x5a, 0x13, 0xfe, 0x2e, 0x63, 0xdc, 0xd9, 0x36, 0x42, 0xe1, 0x56, 0x8e, 0xab, 0x1d, + 0xe9, 0xc6, 0x58, 0xab, 0x2f, 0x42, 0xfc, 0x7f, 0x5a, 0x2d, 0xbe, 0x03, 0x00, 0x00, 0xff, 0xff, + 0x10, 0x5d, 0xdb, 0x8d, 0x1a, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/device_properties.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/device_properties.pb.go new file mode 100644 index 0000000..7664c51 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/device_properties.pb.go @@ -0,0 +1,261 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/device_properties.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type DeviceProperties struct { + // Device type (CPU, GPU, ...) + Type string `protobuf:"bytes,1,opt,name=type,proto3" json:"type,omitempty"` + // Vendor (Intel, nvidia, ...) + Vendor string `protobuf:"bytes,2,opt,name=vendor,proto3" json:"vendor,omitempty"` + // Model (Haswell, K40, ...) + Model string `protobuf:"bytes,3,opt,name=model,proto3" json:"model,omitempty"` + // Core Frequency in Mhz + Frequency int64 `protobuf:"varint,4,opt,name=frequency,proto3" json:"frequency,omitempty"` + // Number of cores + NumCores int64 `protobuf:"varint,5,opt,name=num_cores,json=numCores,proto3" json:"num_cores,omitempty"` + // Version of the tools and libraries used with this device (e.g. gcc 4.9, + // cudnn 5.1) + Environment map[string]string `protobuf:"bytes,6,rep,name=environment,proto3" json:"environment,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // Number of registers per core. + NumRegisters int64 `protobuf:"varint,7,opt,name=num_registers,json=numRegisters,proto3" json:"num_registers,omitempty"` + // L1 cache size in bytes + L1CacheSize int64 `protobuf:"varint,8,opt,name=l1_cache_size,json=l1CacheSize,proto3" json:"l1_cache_size,omitempty"` + // L2 cache size in bytes + L2CacheSize int64 `protobuf:"varint,9,opt,name=l2_cache_size,json=l2CacheSize,proto3" json:"l2_cache_size,omitempty"` + // L3 cache size in bytes + L3CacheSize int64 `protobuf:"varint,10,opt,name=l3_cache_size,json=l3CacheSize,proto3" json:"l3_cache_size,omitempty"` + // Shared memory size per multiprocessor in bytes. This field is + // applicable to GPUs only. + SharedMemorySizePerMultiprocessor int64 `protobuf:"varint,11,opt,name=shared_memory_size_per_multiprocessor,json=sharedMemorySizePerMultiprocessor,proto3" json:"shared_memory_size_per_multiprocessor,omitempty"` + // Memory size in bytes + MemorySize int64 `protobuf:"varint,12,opt,name=memory_size,json=memorySize,proto3" json:"memory_size,omitempty"` + // Memory bandwidth in KB/s + Bandwidth int64 `protobuf:"varint,13,opt,name=bandwidth,proto3" json:"bandwidth,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeviceProperties) Reset() { *m = DeviceProperties{} } +func (m *DeviceProperties) String() string { return proto.CompactTextString(m) } +func (*DeviceProperties) ProtoMessage() {} +func (*DeviceProperties) Descriptor() ([]byte, []int) { + return fileDescriptor_07c4fdb3c62f9732, []int{0} +} + +func (m *DeviceProperties) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeviceProperties.Unmarshal(m, b) +} +func (m *DeviceProperties) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeviceProperties.Marshal(b, m, deterministic) +} +func (m *DeviceProperties) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeviceProperties.Merge(m, src) +} +func (m *DeviceProperties) XXX_Size() int { + return xxx_messageInfo_DeviceProperties.Size(m) +} +func (m *DeviceProperties) XXX_DiscardUnknown() { + xxx_messageInfo_DeviceProperties.DiscardUnknown(m) +} + +var xxx_messageInfo_DeviceProperties proto.InternalMessageInfo + +func (m *DeviceProperties) GetType() string { + if m != nil { + return m.Type + } + return "" +} + +func (m *DeviceProperties) GetVendor() string { + if m != nil { + return m.Vendor + } + return "" +} + +func (m *DeviceProperties) GetModel() string { + if m != nil { + return m.Model + } + return "" +} + +func (m *DeviceProperties) GetFrequency() int64 { + if m != nil { + return m.Frequency + } + return 0 +} + +func (m *DeviceProperties) GetNumCores() int64 { + if m != nil { + return m.NumCores + } + return 0 +} + +func (m *DeviceProperties) GetEnvironment() map[string]string { + if m != nil { + return m.Environment + } + return nil +} + +func (m *DeviceProperties) GetNumRegisters() int64 { + if m != nil { + return m.NumRegisters + } + return 0 +} + +func (m *DeviceProperties) GetL1CacheSize() int64 { + if m != nil { + return m.L1CacheSize + } + return 0 +} + +func (m *DeviceProperties) GetL2CacheSize() int64 { + if m != nil { + return m.L2CacheSize + } + return 0 +} + +func (m *DeviceProperties) GetL3CacheSize() int64 { + if m != nil { + return m.L3CacheSize + } + return 0 +} + +func (m *DeviceProperties) GetSharedMemorySizePerMultiprocessor() int64 { + if m != nil { + return m.SharedMemorySizePerMultiprocessor + } + return 0 +} + +func (m *DeviceProperties) GetMemorySize() int64 { + if m != nil { + return m.MemorySize + } + return 0 +} + +func (m *DeviceProperties) GetBandwidth() int64 { + if m != nil { + return m.Bandwidth + } + return 0 +} + +type NamedDevice struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Properties *DeviceProperties `protobuf:"bytes,2,opt,name=properties,proto3" json:"properties,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NamedDevice) Reset() { *m = NamedDevice{} } +func (m *NamedDevice) String() string { return proto.CompactTextString(m) } +func (*NamedDevice) ProtoMessage() {} +func (*NamedDevice) Descriptor() ([]byte, []int) { + return fileDescriptor_07c4fdb3c62f9732, []int{1} +} + +func (m *NamedDevice) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NamedDevice.Unmarshal(m, b) +} +func (m *NamedDevice) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NamedDevice.Marshal(b, m, deterministic) +} +func (m *NamedDevice) XXX_Merge(src proto.Message) { + xxx_messageInfo_NamedDevice.Merge(m, src) +} +func (m *NamedDevice) XXX_Size() int { + return xxx_messageInfo_NamedDevice.Size(m) +} +func (m *NamedDevice) XXX_DiscardUnknown() { + xxx_messageInfo_NamedDevice.DiscardUnknown(m) +} + +var xxx_messageInfo_NamedDevice proto.InternalMessageInfo + +func (m *NamedDevice) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *NamedDevice) GetProperties() *DeviceProperties { + if m != nil { + return m.Properties + } + return nil +} + +func init() { + proto.RegisterType((*DeviceProperties)(nil), "tensorflow.DeviceProperties") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.DeviceProperties.EnvironmentEntry") + proto.RegisterType((*NamedDevice)(nil), "tensorflow.NamedDevice") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/device_properties.proto", fileDescriptor_07c4fdb3c62f9732) +} + +var fileDescriptor_07c4fdb3c62f9732 = []byte{ + // 464 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x93, 0xc1, 0x6f, 0xd3, 0x30, + 0x14, 0xc6, 0x95, 0xb5, 0x2b, 0xeb, 0xcb, 0x2a, 0x55, 0x16, 0x9a, 0x2c, 0x98, 0x44, 0x29, 0x42, + 0xea, 0x85, 0x96, 0xb5, 0x17, 0x84, 0x10, 0x87, 0x8d, 0x1d, 0x07, 0x55, 0x11, 0x17, 0x2e, 0x56, + 0x9a, 0xbc, 0xb6, 0x11, 0xb1, 0x1d, 0x9e, 0x9d, 0x4e, 0xd9, 0x9f, 0xc7, 0x5f, 0xc5, 0x11, 0xd9, + 0x09, 0x4d, 0x56, 0x21, 0x6e, 0xdf, 0xfb, 0xfc, 0x7b, 0x2f, 0x56, 0xde, 0x67, 0x78, 0x6b, 0x51, + 0x19, 0x4d, 0x9b, 0x4c, 0xdf, 0xcf, 0x62, 0x4d, 0x38, 0xcb, 0x49, 0x5b, 0xbd, 0x2e, 0x36, 0xb3, + 0x04, 0xf7, 0x69, 0x8c, 0x22, 0x27, 0x9d, 0x23, 0xd9, 0x14, 0xcd, 0xd4, 0x1f, 0x31, 0x68, 0x3a, + 0xc6, 0xbf, 0xba, 0x30, 0xfc, 0xe4, 0xb9, 0xe5, 0x01, 0x63, 0x0c, 0xba, 0xb6, 0xcc, 0x91, 0x07, + 0xa3, 0x60, 0xd2, 0x5f, 0x79, 0xcd, 0x2e, 0xa0, 0xb7, 0x47, 0x95, 0x68, 0xe2, 0x27, 0xde, 0xad, + 0x2b, 0xf6, 0x14, 0x4e, 0xa5, 0x4e, 0x30, 0xe3, 0x1d, 0x6f, 0x57, 0x05, 0xbb, 0x84, 0xfe, 0x86, + 0xf0, 0x67, 0x81, 0x2a, 0x2e, 0x79, 0x77, 0x14, 0x4c, 0x3a, 0xab, 0xc6, 0x60, 0xcf, 0xa1, 0xaf, + 0x0a, 0x29, 0xdc, 0x6d, 0x0d, 0x3f, 0xf5, 0xa7, 0x67, 0xaa, 0x90, 0x37, 0xae, 0x66, 0x5f, 0x20, + 0x44, 0xb5, 0x4f, 0x49, 0x2b, 0x89, 0xca, 0xf2, 0xde, 0xa8, 0x33, 0x09, 0xe7, 0x6f, 0xa6, 0xcd, + 0x9d, 0xa7, 0xc7, 0xf7, 0x9d, 0xde, 0x36, 0xfc, 0xad, 0xb2, 0x54, 0xae, 0xda, 0x13, 0xd8, 0x2b, + 0x18, 0xb8, 0xaf, 0x11, 0x6e, 0x53, 0x63, 0x91, 0x0c, 0x7f, 0xe2, 0xbf, 0x78, 0xae, 0x0a, 0xb9, + 0xfa, 0xeb, 0xb1, 0x31, 0x0c, 0xb2, 0x2b, 0x11, 0x47, 0xf1, 0x0e, 0x85, 0x49, 0x1f, 0x90, 0x9f, + 0x79, 0x28, 0xcc, 0xae, 0x6e, 0x9c, 0xf7, 0x35, 0x7d, 0x40, 0xcf, 0xcc, 0xdb, 0x4c, 0xbf, 0x66, + 0xe6, 0x8f, 0x99, 0x45, 0x9b, 0x81, 0x9a, 0x59, 0x34, 0xcc, 0x12, 0x5e, 0x9b, 0x5d, 0x44, 0x98, + 0x08, 0x89, 0x52, 0x53, 0xe9, 0x41, 0x91, 0x23, 0x09, 0x59, 0x64, 0x36, 0xcd, 0x49, 0xc7, 0x68, + 0x8c, 0x26, 0x1e, 0xfa, 0xde, 0x97, 0x15, 0x7c, 0xe7, 0x59, 0x37, 0x60, 0x89, 0x74, 0xf7, 0x08, + 0x64, 0x2f, 0x20, 0x6c, 0x8d, 0xe2, 0xe7, 0xbe, 0x0f, 0xe4, 0xa1, 0xc3, 0xed, 0x63, 0x1d, 0xa9, + 0xe4, 0x3e, 0x4d, 0xec, 0x8e, 0x0f, 0xaa, 0x7d, 0x1c, 0x8c, 0x67, 0x1f, 0x61, 0x78, 0xfc, 0x0b, + 0xd9, 0x10, 0x3a, 0x3f, 0xb0, 0xac, 0x23, 0xe0, 0xa4, 0xdb, 0xf4, 0x3e, 0xca, 0x0a, 0xac, 0x03, + 0x50, 0x15, 0xef, 0x4f, 0xde, 0x05, 0x63, 0x01, 0xe1, 0xe7, 0x48, 0x62, 0x52, 0x2d, 0xc6, 0xc5, + 0x47, 0x45, 0xf2, 0x10, 0x1f, 0xa7, 0xd9, 0x07, 0x80, 0x26, 0x87, 0x7e, 0x42, 0x38, 0xbf, 0xfc, + 0xdf, 0x52, 0x57, 0x2d, 0xfe, 0x9a, 0xae, 0x2f, 0x8e, 0xcf, 0x97, 0x2e, 0xca, 0xe6, 0xfb, 0xb7, + 0x6d, 0x6a, 0x77, 0xc5, 0x7a, 0x1a, 0x6b, 0x39, 0x6b, 0x3d, 0x84, 0x7f, 0xcb, 0xad, 0x3e, 0x7a, + 0x21, 0x1b, 0x4d, 0x3e, 0x85, 0xc2, 0x3b, 0x46, 0x6c, 0x75, 0xa5, 0x7e, 0x07, 0xc1, 0xba, 0xe7, + 0xd5, 0xe2, 0x4f, 0x00, 0x00, 0x00, 0xff, 0xff, 0xcd, 0xa5, 0x99, 0xef, 0x60, 0x03, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/eager_service.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/eager_service.pb.go new file mode 100644 index 0000000..b2a748e --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/eager_service.pb.go @@ -0,0 +1,1654 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/eager_service.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + device_attributes_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/device_attributes_go_proto" + function_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/function_go_proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + versions_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// A proto representation of an eager operation. +type Operation struct { + // A unique identifier for the operation. Set by the client so that the client + // can uniquely identify the outputs of the scheduled operation. + // + // In the initial implementation, sending duplicate IDs has undefined + // behaviour, but additional constraints may be placed upon this in the + // future. + Id int64 `protobuf:"varint,1,opt,name=id,proto3" json:"id,omitempty"` + Name string `protobuf:"bytes,2,opt,name=name,proto3" json:"name,omitempty"` + OpInputs []*Operation_Input `protobuf:"bytes,10,rep,name=op_inputs,json=opInputs,proto3" json:"op_inputs,omitempty"` + // Control Operation IDs that will be respected when ops are re-ordered by + // async execution. If async execution (+ op re-ordering) is not enabled, this + // should have no effect. + ControlOpIds []int64 `protobuf:"varint,4,rep,packed,name=control_op_ids,json=controlOpIds,proto3" json:"control_op_ids,omitempty"` + Attrs map[string]*attr_value_go_proto.AttrValue `protobuf:"bytes,5,rep,name=attrs,proto3" json:"attrs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + Device string `protobuf:"bytes,6,opt,name=device,proto3" json:"device,omitempty"` + // Indicates whether the op is a component of a multi-device function. + IsComponentFunction bool `protobuf:"varint,7,opt,name=is_component_function,json=isComponentFunction,proto3" json:"is_component_function,omitempty"` + // Set when is_component_function is true. It's initially generated + // when we create an FunctionLibraryRuntime::Options (negative value) and used + // to create Rendezvous for function execution. All components of a + // multi-device function should use the same step id to make sure that they + // can communicate through Send/Recv ops. + FuncStepId int64 `protobuf:"varint,8,opt,name=func_step_id,json=funcStepId,proto3" json:"func_step_id,omitempty"` + // Indicates whether the op is a function. + IsFunction bool `protobuf:"varint,9,opt,name=is_function,json=isFunction,proto3" json:"is_function,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Operation) Reset() { *m = Operation{} } +func (m *Operation) String() string { return proto.CompactTextString(m) } +func (*Operation) ProtoMessage() {} +func (*Operation) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{0} +} + +func (m *Operation) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Operation.Unmarshal(m, b) +} +func (m *Operation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Operation.Marshal(b, m, deterministic) +} +func (m *Operation) XXX_Merge(src proto.Message) { + xxx_messageInfo_Operation.Merge(m, src) +} +func (m *Operation) XXX_Size() int { + return xxx_messageInfo_Operation.Size(m) +} +func (m *Operation) XXX_DiscardUnknown() { + xxx_messageInfo_Operation.DiscardUnknown(m) +} + +var xxx_messageInfo_Operation proto.InternalMessageInfo + +func (m *Operation) GetId() int64 { + if m != nil { + return m.Id + } + return 0 +} + +func (m *Operation) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *Operation) GetOpInputs() []*Operation_Input { + if m != nil { + return m.OpInputs + } + return nil +} + +func (m *Operation) GetControlOpIds() []int64 { + if m != nil { + return m.ControlOpIds + } + return nil +} + +func (m *Operation) GetAttrs() map[string]*attr_value_go_proto.AttrValue { + if m != nil { + return m.Attrs + } + return nil +} + +func (m *Operation) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *Operation) GetIsComponentFunction() bool { + if m != nil { + return m.IsComponentFunction + } + return false +} + +func (m *Operation) GetFuncStepId() int64 { + if m != nil { + return m.FuncStepId + } + return 0 +} + +func (m *Operation) GetIsFunction() bool { + if m != nil { + return m.IsFunction + } + return false +} + +type Operation_Input struct { + // Types that are valid to be assigned to Item: + // *Operation_Input_RemoteHandle + // *Operation_Input_Tensor + Item isOperation_Input_Item `protobuf_oneof:"item"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *Operation_Input) Reset() { *m = Operation_Input{} } +func (m *Operation_Input) String() string { return proto.CompactTextString(m) } +func (*Operation_Input) ProtoMessage() {} +func (*Operation_Input) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{0, 0} +} + +func (m *Operation_Input) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_Operation_Input.Unmarshal(m, b) +} +func (m *Operation_Input) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_Operation_Input.Marshal(b, m, deterministic) +} +func (m *Operation_Input) XXX_Merge(src proto.Message) { + xxx_messageInfo_Operation_Input.Merge(m, src) +} +func (m *Operation_Input) XXX_Size() int { + return xxx_messageInfo_Operation_Input.Size(m) +} +func (m *Operation_Input) XXX_DiscardUnknown() { + xxx_messageInfo_Operation_Input.DiscardUnknown(m) +} + +var xxx_messageInfo_Operation_Input proto.InternalMessageInfo + +type isOperation_Input_Item interface { + isOperation_Input_Item() +} + +type Operation_Input_RemoteHandle struct { + RemoteHandle *RemoteTensorHandle `protobuf:"bytes,1,opt,name=remote_handle,json=remoteHandle,proto3,oneof"` +} + +type Operation_Input_Tensor struct { + Tensor *tensor_go_proto.TensorProto `protobuf:"bytes,2,opt,name=tensor,proto3,oneof"` +} + +func (*Operation_Input_RemoteHandle) isOperation_Input_Item() {} + +func (*Operation_Input_Tensor) isOperation_Input_Item() {} + +func (m *Operation_Input) GetItem() isOperation_Input_Item { + if m != nil { + return m.Item + } + return nil +} + +func (m *Operation_Input) GetRemoteHandle() *RemoteTensorHandle { + if x, ok := m.GetItem().(*Operation_Input_RemoteHandle); ok { + return x.RemoteHandle + } + return nil +} + +func (m *Operation_Input) GetTensor() *tensor_go_proto.TensorProto { + if x, ok := m.GetItem().(*Operation_Input_Tensor); ok { + return x.Tensor + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*Operation_Input) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*Operation_Input_RemoteHandle)(nil), + (*Operation_Input_Tensor)(nil), + } +} + +type QueueItem struct { + // The remote executor should be able to handle either executing ops directly, + // or releasing any unused tensor handles, since the tensor lifetime is + // maintained by the client. + // + // Types that are valid to be assigned to Item: + // *QueueItem_HandleToDecref + // *QueueItem_Operation + // *QueueItem_SendTensor + // *QueueItem_RegisterFunction + // *QueueItem_CleanupFunction + // *QueueItem_SyncRemoteExecutorForStream + // *QueueItem_SendPackedHandle + Item isQueueItem_Item `protobuf_oneof:"item"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *QueueItem) Reset() { *m = QueueItem{} } +func (m *QueueItem) String() string { return proto.CompactTextString(m) } +func (*QueueItem) ProtoMessage() {} +func (*QueueItem) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{1} +} + +func (m *QueueItem) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_QueueItem.Unmarshal(m, b) +} +func (m *QueueItem) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_QueueItem.Marshal(b, m, deterministic) +} +func (m *QueueItem) XXX_Merge(src proto.Message) { + xxx_messageInfo_QueueItem.Merge(m, src) +} +func (m *QueueItem) XXX_Size() int { + return xxx_messageInfo_QueueItem.Size(m) +} +func (m *QueueItem) XXX_DiscardUnknown() { + xxx_messageInfo_QueueItem.DiscardUnknown(m) +} + +var xxx_messageInfo_QueueItem proto.InternalMessageInfo + +type isQueueItem_Item interface { + isQueueItem_Item() +} + +type QueueItem_HandleToDecref struct { + HandleToDecref *RemoteTensorHandle `protobuf:"bytes,1,opt,name=handle_to_decref,json=handleToDecref,proto3,oneof"` +} + +type QueueItem_Operation struct { + Operation *Operation `protobuf:"bytes,2,opt,name=operation,proto3,oneof"` +} + +type QueueItem_SendTensor struct { + SendTensor *SendTensorOp `protobuf:"bytes,3,opt,name=send_tensor,json=sendTensor,proto3,oneof"` +} + +type QueueItem_RegisterFunction struct { + RegisterFunction *RegisterFunctionOp `protobuf:"bytes,4,opt,name=register_function,json=registerFunction,proto3,oneof"` +} + +type QueueItem_CleanupFunction struct { + CleanupFunction *CleanupFunctionOp `protobuf:"bytes,5,opt,name=cleanup_function,json=cleanupFunction,proto3,oneof"` +} + +type QueueItem_SyncRemoteExecutorForStream struct { + SyncRemoteExecutorForStream *SyncRemoteExecutorForStream `protobuf:"bytes,6,opt,name=sync_remote_executor_for_stream,json=syncRemoteExecutorForStream,proto3,oneof"` +} + +type QueueItem_SendPackedHandle struct { + SendPackedHandle *SendPackedHandleOp `protobuf:"bytes,7,opt,name=send_packed_handle,json=sendPackedHandle,proto3,oneof"` +} + +func (*QueueItem_HandleToDecref) isQueueItem_Item() {} + +func (*QueueItem_Operation) isQueueItem_Item() {} + +func (*QueueItem_SendTensor) isQueueItem_Item() {} + +func (*QueueItem_RegisterFunction) isQueueItem_Item() {} + +func (*QueueItem_CleanupFunction) isQueueItem_Item() {} + +func (*QueueItem_SyncRemoteExecutorForStream) isQueueItem_Item() {} + +func (*QueueItem_SendPackedHandle) isQueueItem_Item() {} + +func (m *QueueItem) GetItem() isQueueItem_Item { + if m != nil { + return m.Item + } + return nil +} + +func (m *QueueItem) GetHandleToDecref() *RemoteTensorHandle { + if x, ok := m.GetItem().(*QueueItem_HandleToDecref); ok { + return x.HandleToDecref + } + return nil +} + +func (m *QueueItem) GetOperation() *Operation { + if x, ok := m.GetItem().(*QueueItem_Operation); ok { + return x.Operation + } + return nil +} + +func (m *QueueItem) GetSendTensor() *SendTensorOp { + if x, ok := m.GetItem().(*QueueItem_SendTensor); ok { + return x.SendTensor + } + return nil +} + +func (m *QueueItem) GetRegisterFunction() *RegisterFunctionOp { + if x, ok := m.GetItem().(*QueueItem_RegisterFunction); ok { + return x.RegisterFunction + } + return nil +} + +func (m *QueueItem) GetCleanupFunction() *CleanupFunctionOp { + if x, ok := m.GetItem().(*QueueItem_CleanupFunction); ok { + return x.CleanupFunction + } + return nil +} + +func (m *QueueItem) GetSyncRemoteExecutorForStream() *SyncRemoteExecutorForStream { + if x, ok := m.GetItem().(*QueueItem_SyncRemoteExecutorForStream); ok { + return x.SyncRemoteExecutorForStream + } + return nil +} + +func (m *QueueItem) GetSendPackedHandle() *SendPackedHandleOp { + if x, ok := m.GetItem().(*QueueItem_SendPackedHandle); ok { + return x.SendPackedHandle + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*QueueItem) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*QueueItem_HandleToDecref)(nil), + (*QueueItem_Operation)(nil), + (*QueueItem_SendTensor)(nil), + (*QueueItem_RegisterFunction)(nil), + (*QueueItem_CleanupFunction)(nil), + (*QueueItem_SyncRemoteExecutorForStream)(nil), + (*QueueItem_SendPackedHandle)(nil), + } +} + +type QueueResponse struct { + // `shape` and `tensor` cannot be set in the same response. + // Shapes of output tensors for creating remote TensorHandles. + Shape []*tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,1,rep,name=shape,proto3" json:"shape,omitempty"` + // Optional. If set, represents the output devices of a function. + Device []string `protobuf:"bytes,3,rep,name=device,proto3" json:"device,omitempty"` + // Output tensors of a remote function. Set when Operation.id is invalid. + Tensor []*tensor_go_proto.TensorProto `protobuf:"bytes,2,rep,name=tensor,proto3" json:"tensor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *QueueResponse) Reset() { *m = QueueResponse{} } +func (m *QueueResponse) String() string { return proto.CompactTextString(m) } +func (*QueueResponse) ProtoMessage() {} +func (*QueueResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{2} +} + +func (m *QueueResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_QueueResponse.Unmarshal(m, b) +} +func (m *QueueResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_QueueResponse.Marshal(b, m, deterministic) +} +func (m *QueueResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_QueueResponse.Merge(m, src) +} +func (m *QueueResponse) XXX_Size() int { + return xxx_messageInfo_QueueResponse.Size(m) +} +func (m *QueueResponse) XXX_DiscardUnknown() { + xxx_messageInfo_QueueResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_QueueResponse proto.InternalMessageInfo + +func (m *QueueResponse) GetShape() []*tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *QueueResponse) GetDevice() []string { + if m != nil { + return m.Device + } + return nil +} + +func (m *QueueResponse) GetTensor() []*tensor_go_proto.TensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +type CreateContextRequest struct { + // Identifies the full cluster, and this particular worker's position within. + ServerDef *ServerDef `protobuf:"bytes,1,opt,name=server_def,json=serverDef,proto3" json:"server_def,omitempty"` + // Whether the ops on the worker should be executed synchronously or + // asynchronously. By default, ops are executed synchronously. + Async bool `protobuf:"varint,2,opt,name=async,proto3" json:"async,omitempty"` + // Number of seconds to keep the context alive. If more than keep_alive_secs + // has passed since a particular context has been communicated with, it will + // be garbage collected. + KeepAliveSecs int64 `protobuf:"varint,3,opt,name=keep_alive_secs,json=keepAliveSecs,proto3" json:"keep_alive_secs,omitempty"` + // This is the version for all the ops that will be enqueued by the client. + VersionDef *versions_go_proto.VersionDef `protobuf:"bytes,4,opt,name=version_def,json=versionDef,proto3" json:"version_def,omitempty"` + // Device attributes in the cluster + ClusterDeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,6,rep,name=cluster_device_attributes,json=clusterDeviceAttributes,proto3" json:"cluster_device_attributes,omitempty"` + // The ID of the created context. This is usually a randomly generated number, + // that will be used to identify the context in future requests to the + // service. Contexts are not persisted through server restarts. + // This ID will be used for all future communications as well. It is essential + // that both ends use this ID for selecting a rendezvous to get everything to + // match. + ContextId uint64 `protobuf:"fixed64,7,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + // The view ID of the context. + ContextViewId uint64 `protobuf:"fixed64,8,opt,name=context_view_id,json=contextViewId,proto3" json:"context_view_id,omitempty"` + // For a multi device function, if false, eagerly copy all remote inputs to + // the default function device; if true, lazily copy remote inputs to their + // target devices after function instantiation to avoid redundant copies. + LazyCopyRemoteFunctionInputs bool `protobuf:"varint,9,opt,name=lazy_copy_remote_function_inputs,json=lazyCopyRemoteFunctionInputs,proto3" json:"lazy_copy_remote_function_inputs,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CreateContextRequest) Reset() { *m = CreateContextRequest{} } +func (m *CreateContextRequest) String() string { return proto.CompactTextString(m) } +func (*CreateContextRequest) ProtoMessage() {} +func (*CreateContextRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{3} +} + +func (m *CreateContextRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CreateContextRequest.Unmarshal(m, b) +} +func (m *CreateContextRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CreateContextRequest.Marshal(b, m, deterministic) +} +func (m *CreateContextRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CreateContextRequest.Merge(m, src) +} +func (m *CreateContextRequest) XXX_Size() int { + return xxx_messageInfo_CreateContextRequest.Size(m) +} +func (m *CreateContextRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CreateContextRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CreateContextRequest proto.InternalMessageInfo + +func (m *CreateContextRequest) GetServerDef() *ServerDef { + if m != nil { + return m.ServerDef + } + return nil +} + +func (m *CreateContextRequest) GetAsync() bool { + if m != nil { + return m.Async + } + return false +} + +func (m *CreateContextRequest) GetKeepAliveSecs() int64 { + if m != nil { + return m.KeepAliveSecs + } + return 0 +} + +func (m *CreateContextRequest) GetVersionDef() *versions_go_proto.VersionDef { + if m != nil { + return m.VersionDef + } + return nil +} + +func (m *CreateContextRequest) GetClusterDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.ClusterDeviceAttributes + } + return nil +} + +func (m *CreateContextRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +func (m *CreateContextRequest) GetContextViewId() uint64 { + if m != nil { + return m.ContextViewId + } + return 0 +} + +func (m *CreateContextRequest) GetLazyCopyRemoteFunctionInputs() bool { + if m != nil { + return m.LazyCopyRemoteFunctionInputs + } + return false +} + +type CreateContextResponse struct { + // List of devices that are locally accessible to the worker. + DeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,2,rep,name=device_attributes,json=deviceAttributes,proto3" json:"device_attributes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CreateContextResponse) Reset() { *m = CreateContextResponse{} } +func (m *CreateContextResponse) String() string { return proto.CompactTextString(m) } +func (*CreateContextResponse) ProtoMessage() {} +func (*CreateContextResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{4} +} + +func (m *CreateContextResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CreateContextResponse.Unmarshal(m, b) +} +func (m *CreateContextResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CreateContextResponse.Marshal(b, m, deterministic) +} +func (m *CreateContextResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CreateContextResponse.Merge(m, src) +} +func (m *CreateContextResponse) XXX_Size() int { + return xxx_messageInfo_CreateContextResponse.Size(m) +} +func (m *CreateContextResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CreateContextResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CreateContextResponse proto.InternalMessageInfo + +func (m *CreateContextResponse) GetDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.DeviceAttributes + } + return nil +} + +type UpdateContextRequest struct { + // Identifies the full cluster, and this particular worker's position within. + ServerDef *ServerDef `protobuf:"bytes,1,opt,name=server_def,json=serverDef,proto3" json:"server_def,omitempty"` + // Device attributes in the cluster. + // If this field is empty, it indicates that this is a simple update request + // that only increments the cluster view ID and does not require changes to + // the workers it connects to. + ClusterDeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,2,rep,name=cluster_device_attributes,json=clusterDeviceAttributes,proto3" json:"cluster_device_attributes,omitempty"` + // The ID of the context to be updated. A context with the specified ID must + // already exist on the recepient server of this request. + ContextId uint64 `protobuf:"fixed64,3,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + // The view ID of the context, which should be contiguously incremented when + // updating the same context. + ContextViewId uint64 `protobuf:"fixed64,4,opt,name=context_view_id,json=contextViewId,proto3" json:"context_view_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *UpdateContextRequest) Reset() { *m = UpdateContextRequest{} } +func (m *UpdateContextRequest) String() string { return proto.CompactTextString(m) } +func (*UpdateContextRequest) ProtoMessage() {} +func (*UpdateContextRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{5} +} + +func (m *UpdateContextRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_UpdateContextRequest.Unmarshal(m, b) +} +func (m *UpdateContextRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_UpdateContextRequest.Marshal(b, m, deterministic) +} +func (m *UpdateContextRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_UpdateContextRequest.Merge(m, src) +} +func (m *UpdateContextRequest) XXX_Size() int { + return xxx_messageInfo_UpdateContextRequest.Size(m) +} +func (m *UpdateContextRequest) XXX_DiscardUnknown() { + xxx_messageInfo_UpdateContextRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_UpdateContextRequest proto.InternalMessageInfo + +func (m *UpdateContextRequest) GetServerDef() *ServerDef { + if m != nil { + return m.ServerDef + } + return nil +} + +func (m *UpdateContextRequest) GetClusterDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.ClusterDeviceAttributes + } + return nil +} + +func (m *UpdateContextRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +func (m *UpdateContextRequest) GetContextViewId() uint64 { + if m != nil { + return m.ContextViewId + } + return 0 +} + +type UpdateContextResponse struct { + // List of devices that are locally accessible to the worker. + DeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,1,rep,name=device_attributes,json=deviceAttributes,proto3" json:"device_attributes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *UpdateContextResponse) Reset() { *m = UpdateContextResponse{} } +func (m *UpdateContextResponse) String() string { return proto.CompactTextString(m) } +func (*UpdateContextResponse) ProtoMessage() {} +func (*UpdateContextResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{6} +} + +func (m *UpdateContextResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_UpdateContextResponse.Unmarshal(m, b) +} +func (m *UpdateContextResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_UpdateContextResponse.Marshal(b, m, deterministic) +} +func (m *UpdateContextResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_UpdateContextResponse.Merge(m, src) +} +func (m *UpdateContextResponse) XXX_Size() int { + return xxx_messageInfo_UpdateContextResponse.Size(m) +} +func (m *UpdateContextResponse) XXX_DiscardUnknown() { + xxx_messageInfo_UpdateContextResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_UpdateContextResponse proto.InternalMessageInfo + +func (m *UpdateContextResponse) GetDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.DeviceAttributes + } + return nil +} + +type EnqueueRequest struct { + ContextId uint64 `protobuf:"fixed64,1,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + Queue []*QueueItem `protobuf:"bytes,3,rep,name=queue,proto3" json:"queue,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *EnqueueRequest) Reset() { *m = EnqueueRequest{} } +func (m *EnqueueRequest) String() string { return proto.CompactTextString(m) } +func (*EnqueueRequest) ProtoMessage() {} +func (*EnqueueRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{7} +} + +func (m *EnqueueRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_EnqueueRequest.Unmarshal(m, b) +} +func (m *EnqueueRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_EnqueueRequest.Marshal(b, m, deterministic) +} +func (m *EnqueueRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_EnqueueRequest.Merge(m, src) +} +func (m *EnqueueRequest) XXX_Size() int { + return xxx_messageInfo_EnqueueRequest.Size(m) +} +func (m *EnqueueRequest) XXX_DiscardUnknown() { + xxx_messageInfo_EnqueueRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_EnqueueRequest proto.InternalMessageInfo + +func (m *EnqueueRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +func (m *EnqueueRequest) GetQueue() []*QueueItem { + if m != nil { + return m.Queue + } + return nil +} + +type EnqueueResponse struct { + // A single operation response for every item in the request. + QueueResponse []*QueueResponse `protobuf:"bytes,1,rep,name=queue_response,json=queueResponse,proto3" json:"queue_response,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *EnqueueResponse) Reset() { *m = EnqueueResponse{} } +func (m *EnqueueResponse) String() string { return proto.CompactTextString(m) } +func (*EnqueueResponse) ProtoMessage() {} +func (*EnqueueResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{8} +} + +func (m *EnqueueResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_EnqueueResponse.Unmarshal(m, b) +} +func (m *EnqueueResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_EnqueueResponse.Marshal(b, m, deterministic) +} +func (m *EnqueueResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_EnqueueResponse.Merge(m, src) +} +func (m *EnqueueResponse) XXX_Size() int { + return xxx_messageInfo_EnqueueResponse.Size(m) +} +func (m *EnqueueResponse) XXX_DiscardUnknown() { + xxx_messageInfo_EnqueueResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_EnqueueResponse proto.InternalMessageInfo + +func (m *EnqueueResponse) GetQueueResponse() []*QueueResponse { + if m != nil { + return m.QueueResponse + } + return nil +} + +type WaitQueueDoneRequest struct { + ContextId uint64 `protobuf:"fixed64,1,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + // Ids to wait on. If empty, wait on everything currently pending. + OpId []int64 `protobuf:"varint,2,rep,packed,name=op_id,json=opId,proto3" json:"op_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *WaitQueueDoneRequest) Reset() { *m = WaitQueueDoneRequest{} } +func (m *WaitQueueDoneRequest) String() string { return proto.CompactTextString(m) } +func (*WaitQueueDoneRequest) ProtoMessage() {} +func (*WaitQueueDoneRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{9} +} + +func (m *WaitQueueDoneRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_WaitQueueDoneRequest.Unmarshal(m, b) +} +func (m *WaitQueueDoneRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_WaitQueueDoneRequest.Marshal(b, m, deterministic) +} +func (m *WaitQueueDoneRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_WaitQueueDoneRequest.Merge(m, src) +} +func (m *WaitQueueDoneRequest) XXX_Size() int { + return xxx_messageInfo_WaitQueueDoneRequest.Size(m) +} +func (m *WaitQueueDoneRequest) XXX_DiscardUnknown() { + xxx_messageInfo_WaitQueueDoneRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_WaitQueueDoneRequest proto.InternalMessageInfo + +func (m *WaitQueueDoneRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +func (m *WaitQueueDoneRequest) GetOpId() []int64 { + if m != nil { + return m.OpId + } + return nil +} + +type WaitQueueDoneResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *WaitQueueDoneResponse) Reset() { *m = WaitQueueDoneResponse{} } +func (m *WaitQueueDoneResponse) String() string { return proto.CompactTextString(m) } +func (*WaitQueueDoneResponse) ProtoMessage() {} +func (*WaitQueueDoneResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{10} +} + +func (m *WaitQueueDoneResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_WaitQueueDoneResponse.Unmarshal(m, b) +} +func (m *WaitQueueDoneResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_WaitQueueDoneResponse.Marshal(b, m, deterministic) +} +func (m *WaitQueueDoneResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_WaitQueueDoneResponse.Merge(m, src) +} +func (m *WaitQueueDoneResponse) XXX_Size() int { + return xxx_messageInfo_WaitQueueDoneResponse.Size(m) +} +func (m *WaitQueueDoneResponse) XXX_DiscardUnknown() { + xxx_messageInfo_WaitQueueDoneResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_WaitQueueDoneResponse proto.InternalMessageInfo + +type RunComponentFunctionRequest struct { + ContextId uint64 `protobuf:"fixed64,1,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + Operation *Operation `protobuf:"bytes,2,opt,name=operation,proto3" json:"operation,omitempty"` + // The output indices of its parent function. + OutputNum []int32 `protobuf:"varint,3,rep,packed,name=output_num,json=outputNum,proto3" json:"output_num,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunComponentFunctionRequest) Reset() { *m = RunComponentFunctionRequest{} } +func (m *RunComponentFunctionRequest) String() string { return proto.CompactTextString(m) } +func (*RunComponentFunctionRequest) ProtoMessage() {} +func (*RunComponentFunctionRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{11} +} + +func (m *RunComponentFunctionRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunComponentFunctionRequest.Unmarshal(m, b) +} +func (m *RunComponentFunctionRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunComponentFunctionRequest.Marshal(b, m, deterministic) +} +func (m *RunComponentFunctionRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunComponentFunctionRequest.Merge(m, src) +} +func (m *RunComponentFunctionRequest) XXX_Size() int { + return xxx_messageInfo_RunComponentFunctionRequest.Size(m) +} +func (m *RunComponentFunctionRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RunComponentFunctionRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RunComponentFunctionRequest proto.InternalMessageInfo + +func (m *RunComponentFunctionRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +func (m *RunComponentFunctionRequest) GetOperation() *Operation { + if m != nil { + return m.Operation + } + return nil +} + +func (m *RunComponentFunctionRequest) GetOutputNum() []int32 { + if m != nil { + return m.OutputNum + } + return nil +} + +type RunComponentFunctionResponse struct { + Shape []*tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,1,rep,name=shape,proto3" json:"shape,omitempty"` + Tensor []*tensor_go_proto.TensorProto `protobuf:"bytes,2,rep,name=tensor,proto3" json:"tensor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunComponentFunctionResponse) Reset() { *m = RunComponentFunctionResponse{} } +func (m *RunComponentFunctionResponse) String() string { return proto.CompactTextString(m) } +func (*RunComponentFunctionResponse) ProtoMessage() {} +func (*RunComponentFunctionResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{12} +} + +func (m *RunComponentFunctionResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunComponentFunctionResponse.Unmarshal(m, b) +} +func (m *RunComponentFunctionResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunComponentFunctionResponse.Marshal(b, m, deterministic) +} +func (m *RunComponentFunctionResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunComponentFunctionResponse.Merge(m, src) +} +func (m *RunComponentFunctionResponse) XXX_Size() int { + return xxx_messageInfo_RunComponentFunctionResponse.Size(m) +} +func (m *RunComponentFunctionResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RunComponentFunctionResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RunComponentFunctionResponse proto.InternalMessageInfo + +func (m *RunComponentFunctionResponse) GetShape() []*tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *RunComponentFunctionResponse) GetTensor() []*tensor_go_proto.TensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +type KeepAliveRequest struct { + ContextId uint64 `protobuf:"fixed64,1,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *KeepAliveRequest) Reset() { *m = KeepAliveRequest{} } +func (m *KeepAliveRequest) String() string { return proto.CompactTextString(m) } +func (*KeepAliveRequest) ProtoMessage() {} +func (*KeepAliveRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{13} +} + +func (m *KeepAliveRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_KeepAliveRequest.Unmarshal(m, b) +} +func (m *KeepAliveRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_KeepAliveRequest.Marshal(b, m, deterministic) +} +func (m *KeepAliveRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_KeepAliveRequest.Merge(m, src) +} +func (m *KeepAliveRequest) XXX_Size() int { + return xxx_messageInfo_KeepAliveRequest.Size(m) +} +func (m *KeepAliveRequest) XXX_DiscardUnknown() { + xxx_messageInfo_KeepAliveRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_KeepAliveRequest proto.InternalMessageInfo + +func (m *KeepAliveRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +type KeepAliveResponse struct { + // If the requested context_id is on the remote host, set the context view ID. + ContextViewId uint64 `protobuf:"fixed64,1,opt,name=context_view_id,json=contextViewId,proto3" json:"context_view_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *KeepAliveResponse) Reset() { *m = KeepAliveResponse{} } +func (m *KeepAliveResponse) String() string { return proto.CompactTextString(m) } +func (*KeepAliveResponse) ProtoMessage() {} +func (*KeepAliveResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{14} +} + +func (m *KeepAliveResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_KeepAliveResponse.Unmarshal(m, b) +} +func (m *KeepAliveResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_KeepAliveResponse.Marshal(b, m, deterministic) +} +func (m *KeepAliveResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_KeepAliveResponse.Merge(m, src) +} +func (m *KeepAliveResponse) XXX_Size() int { + return xxx_messageInfo_KeepAliveResponse.Size(m) +} +func (m *KeepAliveResponse) XXX_DiscardUnknown() { + xxx_messageInfo_KeepAliveResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_KeepAliveResponse proto.InternalMessageInfo + +func (m *KeepAliveResponse) GetContextViewId() uint64 { + if m != nil { + return m.ContextViewId + } + return 0 +} + +type CloseContextRequest struct { + ContextId uint64 `protobuf:"fixed64,1,opt,name=context_id,json=contextId,proto3" json:"context_id,omitempty"` + ContextViewId uint64 `protobuf:"fixed64,2,opt,name=context_view_id,json=contextViewId,proto3" json:"context_view_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CloseContextRequest) Reset() { *m = CloseContextRequest{} } +func (m *CloseContextRequest) String() string { return proto.CompactTextString(m) } +func (*CloseContextRequest) ProtoMessage() {} +func (*CloseContextRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{15} +} + +func (m *CloseContextRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CloseContextRequest.Unmarshal(m, b) +} +func (m *CloseContextRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CloseContextRequest.Marshal(b, m, deterministic) +} +func (m *CloseContextRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CloseContextRequest.Merge(m, src) +} +func (m *CloseContextRequest) XXX_Size() int { + return xxx_messageInfo_CloseContextRequest.Size(m) +} +func (m *CloseContextRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CloseContextRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CloseContextRequest proto.InternalMessageInfo + +func (m *CloseContextRequest) GetContextId() uint64 { + if m != nil { + return m.ContextId + } + return 0 +} + +func (m *CloseContextRequest) GetContextViewId() uint64 { + if m != nil { + return m.ContextViewId + } + return 0 +} + +type CloseContextResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CloseContextResponse) Reset() { *m = CloseContextResponse{} } +func (m *CloseContextResponse) String() string { return proto.CompactTextString(m) } +func (*CloseContextResponse) ProtoMessage() {} +func (*CloseContextResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{16} +} + +func (m *CloseContextResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CloseContextResponse.Unmarshal(m, b) +} +func (m *CloseContextResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CloseContextResponse.Marshal(b, m, deterministic) +} +func (m *CloseContextResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CloseContextResponse.Merge(m, src) +} +func (m *CloseContextResponse) XXX_Size() int { + return xxx_messageInfo_CloseContextResponse.Size(m) +} +func (m *CloseContextResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CloseContextResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CloseContextResponse proto.InternalMessageInfo + +type RegisterFunctionOp struct { + FunctionDef *function_go_proto.FunctionDef `protobuf:"bytes,1,opt,name=function_def,json=functionDef,proto3" json:"function_def,omitempty"` + // If true, it means that function_def is produced by graph partition during + // multi-device function instantiation. + IsComponentFunction bool `protobuf:"varint,2,opt,name=is_component_function,json=isComponentFunction,proto3" json:"is_component_function,omitempty"` + // All necessary FunctionDefs and GradientDefs to expand `function_def`. + // When is_component_function is true, `function_def` could be a nested + // function, since some nodes in its parent's function body could be + // replaced with a new function by the graph optimization passes. No need to + // add FunctionDefs here to the function cache in EagerContext since they + // won't be executed as KernelAndDevices. + Library *function_go_proto.FunctionDefLibrary `protobuf:"bytes,3,opt,name=library,proto3" json:"library,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RegisterFunctionOp) Reset() { *m = RegisterFunctionOp{} } +func (m *RegisterFunctionOp) String() string { return proto.CompactTextString(m) } +func (*RegisterFunctionOp) ProtoMessage() {} +func (*RegisterFunctionOp) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{17} +} + +func (m *RegisterFunctionOp) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RegisterFunctionOp.Unmarshal(m, b) +} +func (m *RegisterFunctionOp) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RegisterFunctionOp.Marshal(b, m, deterministic) +} +func (m *RegisterFunctionOp) XXX_Merge(src proto.Message) { + xxx_messageInfo_RegisterFunctionOp.Merge(m, src) +} +func (m *RegisterFunctionOp) XXX_Size() int { + return xxx_messageInfo_RegisterFunctionOp.Size(m) +} +func (m *RegisterFunctionOp) XXX_DiscardUnknown() { + xxx_messageInfo_RegisterFunctionOp.DiscardUnknown(m) +} + +var xxx_messageInfo_RegisterFunctionOp proto.InternalMessageInfo + +func (m *RegisterFunctionOp) GetFunctionDef() *function_go_proto.FunctionDef { + if m != nil { + return m.FunctionDef + } + return nil +} + +func (m *RegisterFunctionOp) GetIsComponentFunction() bool { + if m != nil { + return m.IsComponentFunction + } + return false +} + +func (m *RegisterFunctionOp) GetLibrary() *function_go_proto.FunctionDefLibrary { + if m != nil { + return m.Library + } + return nil +} + +// Cleanup the step state of a multi-device function (e.g. tensors buffered by +// a `Send` op but not picked up by its corresponding `Recv` op). +type CleanupFunctionOp struct { + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CleanupFunctionOp) Reset() { *m = CleanupFunctionOp{} } +func (m *CleanupFunctionOp) String() string { return proto.CompactTextString(m) } +func (*CleanupFunctionOp) ProtoMessage() {} +func (*CleanupFunctionOp) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{18} +} + +func (m *CleanupFunctionOp) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CleanupFunctionOp.Unmarshal(m, b) +} +func (m *CleanupFunctionOp) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CleanupFunctionOp.Marshal(b, m, deterministic) +} +func (m *CleanupFunctionOp) XXX_Merge(src proto.Message) { + xxx_messageInfo_CleanupFunctionOp.Merge(m, src) +} +func (m *CleanupFunctionOp) XXX_Size() int { + return xxx_messageInfo_CleanupFunctionOp.Size(m) +} +func (m *CleanupFunctionOp) XXX_DiscardUnknown() { + xxx_messageInfo_CleanupFunctionOp.DiscardUnknown(m) +} + +var xxx_messageInfo_CleanupFunctionOp proto.InternalMessageInfo + +func (m *CleanupFunctionOp) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +type SyncRemoteExecutorForStream struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SyncRemoteExecutorForStream) Reset() { *m = SyncRemoteExecutorForStream{} } +func (m *SyncRemoteExecutorForStream) String() string { return proto.CompactTextString(m) } +func (*SyncRemoteExecutorForStream) ProtoMessage() {} +func (*SyncRemoteExecutorForStream) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{19} +} + +func (m *SyncRemoteExecutorForStream) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SyncRemoteExecutorForStream.Unmarshal(m, b) +} +func (m *SyncRemoteExecutorForStream) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SyncRemoteExecutorForStream.Marshal(b, m, deterministic) +} +func (m *SyncRemoteExecutorForStream) XXX_Merge(src proto.Message) { + xxx_messageInfo_SyncRemoteExecutorForStream.Merge(m, src) +} +func (m *SyncRemoteExecutorForStream) XXX_Size() int { + return xxx_messageInfo_SyncRemoteExecutorForStream.Size(m) +} +func (m *SyncRemoteExecutorForStream) XXX_DiscardUnknown() { + xxx_messageInfo_SyncRemoteExecutorForStream.DiscardUnknown(m) +} + +var xxx_messageInfo_SyncRemoteExecutorForStream proto.InternalMessageInfo + +type SendTensorOp struct { + // All remote tensors are identified by . To mimic this + // situation when directly sending tensors, we include an "artificial" op ID + // (which would have corresponded to the _Recv op when not using SendTensor). + OpId int64 `protobuf:"varint,1,opt,name=op_id,json=opId,proto3" json:"op_id,omitempty"` + // The index within the repeated field is the output number that will help + // uniquely identify (along with the above op_id) the particular tensor. + Tensors []*tensor_go_proto.TensorProto `protobuf:"bytes,2,rep,name=tensors,proto3" json:"tensors,omitempty"` + // The device on which the tensors should be resident. + DeviceName string `protobuf:"bytes,3,opt,name=device_name,json=deviceName,proto3" json:"device_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SendTensorOp) Reset() { *m = SendTensorOp{} } +func (m *SendTensorOp) String() string { return proto.CompactTextString(m) } +func (*SendTensorOp) ProtoMessage() {} +func (*SendTensorOp) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{20} +} + +func (m *SendTensorOp) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SendTensorOp.Unmarshal(m, b) +} +func (m *SendTensorOp) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SendTensorOp.Marshal(b, m, deterministic) +} +func (m *SendTensorOp) XXX_Merge(src proto.Message) { + xxx_messageInfo_SendTensorOp.Merge(m, src) +} +func (m *SendTensorOp) XXX_Size() int { + return xxx_messageInfo_SendTensorOp.Size(m) +} +func (m *SendTensorOp) XXX_DiscardUnknown() { + xxx_messageInfo_SendTensorOp.DiscardUnknown(m) +} + +var xxx_messageInfo_SendTensorOp proto.InternalMessageInfo + +func (m *SendTensorOp) GetOpId() int64 { + if m != nil { + return m.OpId + } + return 0 +} + +func (m *SendTensorOp) GetTensors() []*tensor_go_proto.TensorProto { + if m != nil { + return m.Tensors + } + return nil +} + +func (m *SendTensorOp) GetDeviceName() string { + if m != nil { + return m.DeviceName + } + return "" +} + +// Send a packed TensorHandle to a remote worker. +type SendPackedHandleOp struct { + // Op id of the remote packed TensorHandle. + OpId int64 `protobuf:"varint,1,opt,name=op_id,json=opId,proto3" json:"op_id,omitempty"` + Handles []*SendPackedHandleOp_Handle `protobuf:"bytes,2,rep,name=handles,proto3" json:"handles,omitempty"` + DeviceName string `protobuf:"bytes,3,opt,name=device_name,json=deviceName,proto3" json:"device_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SendPackedHandleOp) Reset() { *m = SendPackedHandleOp{} } +func (m *SendPackedHandleOp) String() string { return proto.CompactTextString(m) } +func (*SendPackedHandleOp) ProtoMessage() {} +func (*SendPackedHandleOp) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{21} +} + +func (m *SendPackedHandleOp) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SendPackedHandleOp.Unmarshal(m, b) +} +func (m *SendPackedHandleOp) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SendPackedHandleOp.Marshal(b, m, deterministic) +} +func (m *SendPackedHandleOp) XXX_Merge(src proto.Message) { + xxx_messageInfo_SendPackedHandleOp.Merge(m, src) +} +func (m *SendPackedHandleOp) XXX_Size() int { + return xxx_messageInfo_SendPackedHandleOp.Size(m) +} +func (m *SendPackedHandleOp) XXX_DiscardUnknown() { + xxx_messageInfo_SendPackedHandleOp.DiscardUnknown(m) +} + +var xxx_messageInfo_SendPackedHandleOp proto.InternalMessageInfo + +func (m *SendPackedHandleOp) GetOpId() int64 { + if m != nil { + return m.OpId + } + return 0 +} + +func (m *SendPackedHandleOp) GetHandles() []*SendPackedHandleOp_Handle { + if m != nil { + return m.Handles + } + return nil +} + +func (m *SendPackedHandleOp) GetDeviceName() string { + if m != nil { + return m.DeviceName + } + return "" +} + +type SendPackedHandleOp_LocalTensorHandle struct { + Tensor *tensor_go_proto.TensorProto `protobuf:"bytes,1,opt,name=tensor,proto3" json:"tensor,omitempty"` + // Device where the tensor is produced. + Device string `protobuf:"bytes,2,opt,name=device,proto3" json:"device,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SendPackedHandleOp_LocalTensorHandle) Reset() { *m = SendPackedHandleOp_LocalTensorHandle{} } +func (m *SendPackedHandleOp_LocalTensorHandle) String() string { return proto.CompactTextString(m) } +func (*SendPackedHandleOp_LocalTensorHandle) ProtoMessage() {} +func (*SendPackedHandleOp_LocalTensorHandle) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{21, 0} +} + +func (m *SendPackedHandleOp_LocalTensorHandle) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SendPackedHandleOp_LocalTensorHandle.Unmarshal(m, b) +} +func (m *SendPackedHandleOp_LocalTensorHandle) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SendPackedHandleOp_LocalTensorHandle.Marshal(b, m, deterministic) +} +func (m *SendPackedHandleOp_LocalTensorHandle) XXX_Merge(src proto.Message) { + xxx_messageInfo_SendPackedHandleOp_LocalTensorHandle.Merge(m, src) +} +func (m *SendPackedHandleOp_LocalTensorHandle) XXX_Size() int { + return xxx_messageInfo_SendPackedHandleOp_LocalTensorHandle.Size(m) +} +func (m *SendPackedHandleOp_LocalTensorHandle) XXX_DiscardUnknown() { + xxx_messageInfo_SendPackedHandleOp_LocalTensorHandle.DiscardUnknown(m) +} + +var xxx_messageInfo_SendPackedHandleOp_LocalTensorHandle proto.InternalMessageInfo + +func (m *SendPackedHandleOp_LocalTensorHandle) GetTensor() *tensor_go_proto.TensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +func (m *SendPackedHandleOp_LocalTensorHandle) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +type SendPackedHandleOp_Handle struct { + // Types that are valid to be assigned to Item: + // *SendPackedHandleOp_Handle_LocalHandle + // *SendPackedHandleOp_Handle_RemoteHandle + Item isSendPackedHandleOp_Handle_Item `protobuf_oneof:"item"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SendPackedHandleOp_Handle) Reset() { *m = SendPackedHandleOp_Handle{} } +func (m *SendPackedHandleOp_Handle) String() string { return proto.CompactTextString(m) } +func (*SendPackedHandleOp_Handle) ProtoMessage() {} +func (*SendPackedHandleOp_Handle) Descriptor() ([]byte, []int) { + return fileDescriptor_7f63cfa0a7bc4510, []int{21, 1} +} + +func (m *SendPackedHandleOp_Handle) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SendPackedHandleOp_Handle.Unmarshal(m, b) +} +func (m *SendPackedHandleOp_Handle) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SendPackedHandleOp_Handle.Marshal(b, m, deterministic) +} +func (m *SendPackedHandleOp_Handle) XXX_Merge(src proto.Message) { + xxx_messageInfo_SendPackedHandleOp_Handle.Merge(m, src) +} +func (m *SendPackedHandleOp_Handle) XXX_Size() int { + return xxx_messageInfo_SendPackedHandleOp_Handle.Size(m) +} +func (m *SendPackedHandleOp_Handle) XXX_DiscardUnknown() { + xxx_messageInfo_SendPackedHandleOp_Handle.DiscardUnknown(m) +} + +var xxx_messageInfo_SendPackedHandleOp_Handle proto.InternalMessageInfo + +type isSendPackedHandleOp_Handle_Item interface { + isSendPackedHandleOp_Handle_Item() +} + +type SendPackedHandleOp_Handle_LocalHandle struct { + LocalHandle *SendPackedHandleOp_LocalTensorHandle `protobuf:"bytes,1,opt,name=local_handle,json=localHandle,proto3,oneof"` +} + +type SendPackedHandleOp_Handle_RemoteHandle struct { + RemoteHandle *RemoteTensorHandle `protobuf:"bytes,2,opt,name=remote_handle,json=remoteHandle,proto3,oneof"` +} + +func (*SendPackedHandleOp_Handle_LocalHandle) isSendPackedHandleOp_Handle_Item() {} + +func (*SendPackedHandleOp_Handle_RemoteHandle) isSendPackedHandleOp_Handle_Item() {} + +func (m *SendPackedHandleOp_Handle) GetItem() isSendPackedHandleOp_Handle_Item { + if m != nil { + return m.Item + } + return nil +} + +func (m *SendPackedHandleOp_Handle) GetLocalHandle() *SendPackedHandleOp_LocalTensorHandle { + if x, ok := m.GetItem().(*SendPackedHandleOp_Handle_LocalHandle); ok { + return x.LocalHandle + } + return nil +} + +func (m *SendPackedHandleOp_Handle) GetRemoteHandle() *RemoteTensorHandle { + if x, ok := m.GetItem().(*SendPackedHandleOp_Handle_RemoteHandle); ok { + return x.RemoteHandle + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*SendPackedHandleOp_Handle) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*SendPackedHandleOp_Handle_LocalHandle)(nil), + (*SendPackedHandleOp_Handle_RemoteHandle)(nil), + } +} + +func init() { + proto.RegisterType((*Operation)(nil), "tensorflow.eager.Operation") + proto.RegisterMapType((map[string]*attr_value_go_proto.AttrValue)(nil), "tensorflow.eager.Operation.AttrsEntry") + proto.RegisterType((*Operation_Input)(nil), "tensorflow.eager.Operation.Input") + proto.RegisterType((*QueueItem)(nil), "tensorflow.eager.QueueItem") + proto.RegisterType((*QueueResponse)(nil), "tensorflow.eager.QueueResponse") + proto.RegisterType((*CreateContextRequest)(nil), "tensorflow.eager.CreateContextRequest") + proto.RegisterType((*CreateContextResponse)(nil), "tensorflow.eager.CreateContextResponse") + proto.RegisterType((*UpdateContextRequest)(nil), "tensorflow.eager.UpdateContextRequest") + proto.RegisterType((*UpdateContextResponse)(nil), "tensorflow.eager.UpdateContextResponse") + proto.RegisterType((*EnqueueRequest)(nil), "tensorflow.eager.EnqueueRequest") + proto.RegisterType((*EnqueueResponse)(nil), "tensorflow.eager.EnqueueResponse") + proto.RegisterType((*WaitQueueDoneRequest)(nil), "tensorflow.eager.WaitQueueDoneRequest") + proto.RegisterType((*WaitQueueDoneResponse)(nil), "tensorflow.eager.WaitQueueDoneResponse") + proto.RegisterType((*RunComponentFunctionRequest)(nil), "tensorflow.eager.RunComponentFunctionRequest") + proto.RegisterType((*RunComponentFunctionResponse)(nil), "tensorflow.eager.RunComponentFunctionResponse") + proto.RegisterType((*KeepAliveRequest)(nil), "tensorflow.eager.KeepAliveRequest") + proto.RegisterType((*KeepAliveResponse)(nil), "tensorflow.eager.KeepAliveResponse") + proto.RegisterType((*CloseContextRequest)(nil), "tensorflow.eager.CloseContextRequest") + proto.RegisterType((*CloseContextResponse)(nil), "tensorflow.eager.CloseContextResponse") + proto.RegisterType((*RegisterFunctionOp)(nil), "tensorflow.eager.RegisterFunctionOp") + proto.RegisterType((*CleanupFunctionOp)(nil), "tensorflow.eager.CleanupFunctionOp") + proto.RegisterType((*SyncRemoteExecutorForStream)(nil), "tensorflow.eager.SyncRemoteExecutorForStream") + proto.RegisterType((*SendTensorOp)(nil), "tensorflow.eager.SendTensorOp") + proto.RegisterType((*SendPackedHandleOp)(nil), "tensorflow.eager.SendPackedHandleOp") + proto.RegisterType((*SendPackedHandleOp_LocalTensorHandle)(nil), "tensorflow.eager.SendPackedHandleOp.LocalTensorHandle") + proto.RegisterType((*SendPackedHandleOp_Handle)(nil), "tensorflow.eager.SendPackedHandleOp.Handle") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/eager_service.proto", fileDescriptor_7f63cfa0a7bc4510) +} + +var fileDescriptor_7f63cfa0a7bc4510 = []byte{ + // 1598 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xac, 0x58, 0xdd, 0x72, 0xdb, 0x44, + 0x14, 0x8e, 0x2c, 0xdb, 0x89, 0x8f, 0x93, 0xd4, 0xd9, 0x26, 0x8d, 0x71, 0x5a, 0xea, 0xaa, 0x25, + 0x64, 0x68, 0x71, 0x9a, 0x94, 0x81, 0xd2, 0x32, 0xcc, 0xa4, 0xf9, 0x99, 0x38, 0xed, 0x34, 0x45, + 0x4e, 0x5b, 0xa0, 0x65, 0x54, 0x45, 0x5a, 0x27, 0x9a, 0xd8, 0x5a, 0x55, 0x2b, 0x25, 0x35, 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0xbc, 0xfc, 0xa3, 0x8f, 0xd2, 0x9e, + 0x8b, 0x89, 0xe6, 0x53, 0x59, 0x3e, 0x0f, 0x26, 0xc4, 0x3f, 0x78, 0xfe, 0xfd, 0xd3, 0x23, 0x27, + 0x38, 0x0e, 0x0f, 0x6b, 0x16, 0x69, 0xc7, 0xfe, 0x40, 0x1a, 0xb2, 0x3c, 0x22, 0x03, 0x7f, 0x39, + 0xb1, 0xd7, 0x26, 0xa3, 0x18, 0x9c, 0x42, 0x8d, 0x23, 0x22, 0x56, 0x87, 0x79, 0xfe, 0x73, 0xe7, + 0xbf, 0x00, 0x00, 0x00, 0xff, 0xff, 0x03, 0x4c, 0x16, 0x91, 0x2c, 0x14, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/error_codes.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/error_codes.pb.go new file mode 100644 index 0000000..7852779 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/error_codes.pb.go @@ -0,0 +1,239 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/error_codes.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// The canonical error codes for TensorFlow APIs. +// +// Warnings: +// +// - Do not change any numeric assignments. +// - Changes to this list should only be made if there is a compelling +// need that can't be satisfied in another way. Such changes +// must be approved by at least two OWNERS. +// - These error codes must match gRPC and protobuf error codes (except for +// DO_NOT_USE_RESERVED_FOR_FUTURE_EXPANSION_USE_DEFAULT_IN_SWITCH_INSTEAD_). +// +// Sometimes multiple error codes may apply. Services should return +// the most specific error code that applies. For example, prefer +// OUT_OF_RANGE over FAILED_PRECONDITION if both codes apply. +// Similarly prefer NOT_FOUND or ALREADY_EXISTS over FAILED_PRECONDITION. +type Code int32 + +const ( + // Not an error; returned on success + Code_OK Code = 0 + // The operation was cancelled (typically by the caller). + Code_CANCELLED Code = 1 + // Unknown error. An example of where this error may be returned is + // if a Status value received from another address space belongs to + // an error-space that is not known in this address space. Also + // errors raised by APIs that do not return enough error information + // may be converted to this error. + Code_UNKNOWN Code = 2 + // Client specified an invalid argument. Note that this differs + // from FAILED_PRECONDITION. INVALID_ARGUMENT indicates arguments + // that are problematic regardless of the state of the system + // (e.g., a malformed file name). + Code_INVALID_ARGUMENT Code = 3 + // Deadline expired before operation could complete. For operations + // that change the state of the system, this error may be returned + // even if the operation has completed successfully. For example, a + // successful response from a server could have been delayed long + // enough for the deadline to expire. + Code_DEADLINE_EXCEEDED Code = 4 + // Some requested entity (e.g., file or directory) was not found. + // For privacy reasons, this code *may* be returned when the client + // does not have the access right to the entity. + Code_NOT_FOUND Code = 5 + // Some entity that we attempted to create (e.g., file or directory) + // already exists. + Code_ALREADY_EXISTS Code = 6 + // The caller does not have permission to execute the specified + // operation. PERMISSION_DENIED must not be used for rejections + // caused by exhausting some resource (use RESOURCE_EXHAUSTED + // instead for those errors). PERMISSION_DENIED must not be + // used if the caller can not be identified (use UNAUTHENTICATED + // instead for those errors). + Code_PERMISSION_DENIED Code = 7 + // The request does not have valid authentication credentials for the + // operation. + Code_UNAUTHENTICATED Code = 16 + // Some resource has been exhausted, perhaps a per-user quota, or + // perhaps the entire file system is out of space. + Code_RESOURCE_EXHAUSTED Code = 8 + // Operation was rejected because the system is not in a state + // required for the operation's execution. For example, directory + // to be deleted may be non-empty, an rmdir operation is applied to + // a non-directory, etc. + // + // A litmus test that may help a service implementor in deciding + // between FAILED_PRECONDITION, ABORTED, and UNAVAILABLE: + // (a) Use UNAVAILABLE if the client can retry just the failing call. + // (b) Use ABORTED if the client should retry at a higher-level + // (e.g., restarting a read-modify-write sequence). + // (c) Use FAILED_PRECONDITION if the client should not retry until + // the system state has been explicitly fixed. E.g., if an "rmdir" + // fails because the directory is non-empty, FAILED_PRECONDITION + // should be returned since the client should not retry unless + // they have first fixed up the directory by deleting files from it. + // (d) Use FAILED_PRECONDITION if the client performs conditional + // REST Get/Update/Delete on a resource and the resource on the + // server does not match the condition. E.g., conflicting + // read-modify-write on the same resource. + Code_FAILED_PRECONDITION Code = 9 + // The operation was aborted, typically due to a concurrency issue + // like sequencer check failures, transaction aborts, etc. + // + // See litmus test above for deciding between FAILED_PRECONDITION, + // ABORTED, and UNAVAILABLE. + Code_ABORTED Code = 10 + // Operation tried to iterate past the valid input range. E.g., seeking or + // reading past end of file. + // + // Unlike INVALID_ARGUMENT, this error indicates a problem that may + // be fixed if the system state changes. For example, a 32-bit file + // system will generate INVALID_ARGUMENT if asked to read at an + // offset that is not in the range [0,2^32-1], but it will generate + // OUT_OF_RANGE if asked to read from an offset past the current + // file size. + // + // There is a fair bit of overlap between FAILED_PRECONDITION and + // OUT_OF_RANGE. We recommend using OUT_OF_RANGE (the more specific + // error) when it applies so that callers who are iterating through + // a space can easily look for an OUT_OF_RANGE error to detect when + // they are done. + Code_OUT_OF_RANGE Code = 11 + // Operation is not implemented or not supported/enabled in this service. + Code_UNIMPLEMENTED Code = 12 + // Internal errors. Means some invariant expected by the underlying + // system has been broken. If you see one of these errors, + // something is very broken. + Code_INTERNAL Code = 13 + // The service is currently unavailable. This is a most likely a + // transient condition and may be corrected by retrying with + // a backoff. + // + // See litmus test above for deciding between FAILED_PRECONDITION, + // ABORTED, and UNAVAILABLE. + Code_UNAVAILABLE Code = 14 + // Unrecoverable data loss or corruption. + Code_DATA_LOSS Code = 15 + // An extra enum entry to prevent people from writing code that + // fails to compile when a new code is added. + // + // Nobody should ever reference this enumeration entry. In particular, + // if you write C++ code that switches on this enumeration, add a default: + // case instead of a case that mentions this enumeration entry. + // + // Nobody should rely on the value (currently 20) listed here. It + // may change in the future. + Code_DO_NOT_USE_RESERVED_FOR_FUTURE_EXPANSION_USE_DEFAULT_IN_SWITCH_INSTEAD_ Code = 20 +) + +var Code_name = map[int32]string{ + 0: "OK", + 1: "CANCELLED", + 2: "UNKNOWN", + 3: "INVALID_ARGUMENT", + 4: "DEADLINE_EXCEEDED", + 5: "NOT_FOUND", + 6: "ALREADY_EXISTS", + 7: "PERMISSION_DENIED", + 16: "UNAUTHENTICATED", + 8: "RESOURCE_EXHAUSTED", + 9: "FAILED_PRECONDITION", + 10: "ABORTED", + 11: "OUT_OF_RANGE", + 12: "UNIMPLEMENTED", + 13: "INTERNAL", + 14: "UNAVAILABLE", + 15: "DATA_LOSS", + 20: "DO_NOT_USE_RESERVED_FOR_FUTURE_EXPANSION_USE_DEFAULT_IN_SWITCH_INSTEAD_", +} + +var Code_value = map[string]int32{ + "OK": 0, + "CANCELLED": 1, + "UNKNOWN": 2, + "INVALID_ARGUMENT": 3, + "DEADLINE_EXCEEDED": 4, + "NOT_FOUND": 5, + "ALREADY_EXISTS": 6, + "PERMISSION_DENIED": 7, + "UNAUTHENTICATED": 16, + "RESOURCE_EXHAUSTED": 8, + "FAILED_PRECONDITION": 9, + "ABORTED": 10, + "OUT_OF_RANGE": 11, + "UNIMPLEMENTED": 12, + "INTERNAL": 13, + "UNAVAILABLE": 14, + "DATA_LOSS": 15, + "DO_NOT_USE_RESERVED_FOR_FUTURE_EXPANSION_USE_DEFAULT_IN_SWITCH_INSTEAD_": 20, +} + +func (x Code) String() string { + return proto.EnumName(Code_name, int32(x)) +} + +func (Code) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_fe19ebdfbdec439e, []int{0} +} + +func init() { + proto.RegisterEnum("tensorflow.error.Code", Code_name, Code_value) +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/error_codes.proto", fileDescriptor_fe19ebdfbdec439e) +} + +var fileDescriptor_fe19ebdfbdec439e = []byte{ + // 440 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x6c, 0x52, 0xcd, 0x6e, 0x13, 0x31, + 0x18, 0x24, 0x6d, 0x49, 0x5b, 0x27, 0x69, 0x5c, 0xb7, 0xfc, 0x3c, 0x43, 0x0f, 0xc9, 0x81, 0x27, + 0xf8, 0xb2, 0xfe, 0x36, 0xb1, 0xe2, 0x7c, 0x5e, 0xf9, 0x27, 0x2d, 0x5c, 0x2c, 0x92, 0x6e, 0x02, + 0x82, 0x62, 0xb4, 0x9b, 0xaa, 0x2f, 0xc0, 0x81, 0x47, 0xe6, 0x88, 0xbc, 0x1c, 0xa8, 0x10, 0xb7, + 0xd1, 0x78, 0x6c, 0xcf, 0x68, 0x86, 0xdd, 0x1c, 0xea, 0x6f, 0x6d, 0x6a, 0x76, 0x5f, 0xd3, 0xd3, + 0x74, 0x9b, 0x9a, 0x7a, 0xfa, 0xbd, 0x49, 0x87, 0xb4, 0x79, 0xdc, 0x4d, 0xeb, 0xa6, 0x49, 0x4d, + 0xdc, 0xa6, 0xfb, 0xba, 0x9d, 0x74, 0xa4, 0xe0, 0x7f, 0xb5, 0x93, 0xee, 0xf4, 0xe6, 0xc7, 0x31, + 0x3b, 0x29, 0xd2, 0x7d, 0x2d, 0xfa, 0xec, 0xc8, 0x2c, 0xf9, 0x0b, 0x31, 0x62, 0xe7, 0x05, 0x50, + 0x81, 0x5a, 0xa3, 0xe4, 0x3d, 0x31, 0x60, 0xa7, 0x81, 0x96, 0x64, 0x6e, 0x89, 0x1f, 0x89, 0x6b, + 0xc6, 0x15, 0xad, 0x41, 0x2b, 0x19, 0xc1, 0xce, 0xc3, 0x0a, 0xc9, 0xf3, 0x63, 0xf1, 0x8a, 0x5d, + 0x4a, 0x04, 0xa9, 0x15, 0x61, 0xc4, 0xbb, 0x02, 0x51, 0xa2, 0xe4, 0x27, 0xf9, 0x21, 0x32, 0x3e, + 0x96, 0x26, 0x90, 0xe4, 0x2f, 0x85, 0x60, 0x17, 0xa0, 0x2d, 0x82, 0x7c, 0x1f, 0xf1, 0x4e, 0x39, + 0xef, 0x78, 0x3f, 0xdf, 0xac, 0xd0, 0xae, 0x94, 0x73, 0xca, 0x50, 0x94, 0x48, 0x0a, 0x25, 0x3f, + 0x15, 0x57, 0x6c, 0x1c, 0x08, 0x82, 0x5f, 0x20, 0x79, 0x55, 0x80, 0x47, 0xc9, 0xb9, 0x78, 0xcd, + 0x84, 0x45, 0x67, 0x82, 0x2d, 0xf2, 0x2f, 0x0b, 0x08, 0x2e, 0xf3, 0x67, 0xe2, 0x0d, 0xbb, 0x2a, + 0x41, 0x69, 0x94, 0xb1, 0xb2, 0x58, 0x18, 0x92, 0xca, 0x2b, 0x43, 0xfc, 0x3c, 0x3b, 0x87, 0x99, + 0xb1, 0x59, 0xc5, 0x04, 0x67, 0x43, 0x13, 0x7c, 0x34, 0x65, 0xb4, 0x40, 0x73, 0xe4, 0x03, 0x71, + 0xc9, 0x46, 0x81, 0xd4, 0xaa, 0xd2, 0x98, 0x63, 0xa0, 0xe4, 0x43, 0x31, 0x64, 0x67, 0x8a, 0x3c, + 0x5a, 0x02, 0xcd, 0x47, 0x62, 0xcc, 0x06, 0x81, 0x60, 0x0d, 0x4a, 0xc3, 0x4c, 0x23, 0xbf, 0xc8, + 0x81, 0x24, 0x78, 0x88, 0xda, 0x38, 0xc7, 0xc7, 0x62, 0xc9, 0xe6, 0xd2, 0xc4, 0x1c, 0x31, 0x38, + 0x8c, 0x16, 0x1d, 0xda, 0x35, 0xca, 0x58, 0x1a, 0x1b, 0xcb, 0xe0, 0x83, 0xcd, 0x36, 0x2b, 0xa0, + 0x2e, 0x5a, 0x56, 0x48, 0x2c, 0x21, 0x68, 0x1f, 0x15, 0x45, 0x77, 0xab, 0x7c, 0xb1, 0x88, 0x8a, + 0x9c, 0x47, 0x90, 0x91, 0x5f, 0xcf, 0x7e, 0xf6, 0xd8, 0xdb, 0xd4, 0xec, 0x27, 0xcf, 0xfa, 0xd9, + 0x35, 0x1f, 0x1f, 0xea, 0xa7, 0xd4, 0x7c, 0x99, 0x71, 0xcc, 0x55, 0xe5, 0x96, 0xda, 0x2a, 0xd7, + 0xd8, 0x56, 0xbd, 0x0f, 0x61, 0xff, 0xf9, 0xf0, 0xe9, 0x71, 0x33, 0xd9, 0xa6, 0x87, 0xe9, 0xb3, + 0x01, 0xfc, 0x1f, 0xee, 0xd3, 0x3f, 0xcb, 0xd8, 0x75, 0xbb, 0x68, 0xea, 0xd8, 0x31, 0x6d, 0xdc, + 0xa7, 0x3f, 0xe8, 0x57, 0xaf, 0xb7, 0xe9, 0x77, 0xe8, 0xdd, 0xef, 0x00, 0x00, 0x00, 0xff, 0xff, + 0xbb, 0x4e, 0xdc, 0x68, 0x58, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/graph_debug_info.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/graph_debug_info.pb.go new file mode 100644 index 0000000..a0831b9 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/graph_debug_info.pb.go @@ -0,0 +1,237 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/graph_debug_info.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type GraphDebugInfo struct { + // This stores all the source code file names and can be indexed by the + // `file_index`. + Files []string `protobuf:"bytes,1,rep,name=files,proto3" json:"files,omitempty"` + // This maps a node name to a stack trace in the source code. + // The map key is a mangling of the containing function and op name with + // syntax: + // op.name '@' func_name + // For ops in the top-level graph, the func_name is the empty string. + // Note that op names are restricted to a small number of characters which + // exclude '@', making it impossible to collide keys of this form. Function + // names accept a much wider set of characters. + // It would be preferable to avoid mangling and use a tuple key of (op.name, + // func_name), but this is not supported with protocol buffers. + Traces map[string]*GraphDebugInfo_StackTrace `protobuf:"bytes,2,rep,name=traces,proto3" json:"traces,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphDebugInfo) Reset() { *m = GraphDebugInfo{} } +func (m *GraphDebugInfo) String() string { return proto.CompactTextString(m) } +func (*GraphDebugInfo) ProtoMessage() {} +func (*GraphDebugInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_2d49d5c184d173e1, []int{0} +} + +func (m *GraphDebugInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphDebugInfo.Unmarshal(m, b) +} +func (m *GraphDebugInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphDebugInfo.Marshal(b, m, deterministic) +} +func (m *GraphDebugInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphDebugInfo.Merge(m, src) +} +func (m *GraphDebugInfo) XXX_Size() int { + return xxx_messageInfo_GraphDebugInfo.Size(m) +} +func (m *GraphDebugInfo) XXX_DiscardUnknown() { + xxx_messageInfo_GraphDebugInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphDebugInfo proto.InternalMessageInfo + +func (m *GraphDebugInfo) GetFiles() []string { + if m != nil { + return m.Files + } + return nil +} + +func (m *GraphDebugInfo) GetTraces() map[string]*GraphDebugInfo_StackTrace { + if m != nil { + return m.Traces + } + return nil +} + +// This represents a file/line location in the source code. +type GraphDebugInfo_FileLineCol struct { + // File name index, which can be used to retrieve the file name string from + // `files`. The value should be between 0 and (len(files)-1) + FileIndex int32 `protobuf:"varint,1,opt,name=file_index,json=fileIndex,proto3" json:"file_index,omitempty"` + // Line number in the file. + Line int32 `protobuf:"varint,2,opt,name=line,proto3" json:"line,omitempty"` + // Col number in the file line. + Col int32 `protobuf:"varint,3,opt,name=col,proto3" json:"col,omitempty"` + // Name of function contains the file line. + Func string `protobuf:"bytes,4,opt,name=func,proto3" json:"func,omitempty"` + // Source code contained in this file line. + Code string `protobuf:"bytes,5,opt,name=code,proto3" json:"code,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphDebugInfo_FileLineCol) Reset() { *m = GraphDebugInfo_FileLineCol{} } +func (m *GraphDebugInfo_FileLineCol) String() string { return proto.CompactTextString(m) } +func (*GraphDebugInfo_FileLineCol) ProtoMessage() {} +func (*GraphDebugInfo_FileLineCol) Descriptor() ([]byte, []int) { + return fileDescriptor_2d49d5c184d173e1, []int{0, 0} +} + +func (m *GraphDebugInfo_FileLineCol) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphDebugInfo_FileLineCol.Unmarshal(m, b) +} +func (m *GraphDebugInfo_FileLineCol) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphDebugInfo_FileLineCol.Marshal(b, m, deterministic) +} +func (m *GraphDebugInfo_FileLineCol) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphDebugInfo_FileLineCol.Merge(m, src) +} +func (m *GraphDebugInfo_FileLineCol) XXX_Size() int { + return xxx_messageInfo_GraphDebugInfo_FileLineCol.Size(m) +} +func (m *GraphDebugInfo_FileLineCol) XXX_DiscardUnknown() { + xxx_messageInfo_GraphDebugInfo_FileLineCol.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphDebugInfo_FileLineCol proto.InternalMessageInfo + +func (m *GraphDebugInfo_FileLineCol) GetFileIndex() int32 { + if m != nil { + return m.FileIndex + } + return 0 +} + +func (m *GraphDebugInfo_FileLineCol) GetLine() int32 { + if m != nil { + return m.Line + } + return 0 +} + +func (m *GraphDebugInfo_FileLineCol) GetCol() int32 { + if m != nil { + return m.Col + } + return 0 +} + +func (m *GraphDebugInfo_FileLineCol) GetFunc() string { + if m != nil { + return m.Func + } + return "" +} + +func (m *GraphDebugInfo_FileLineCol) GetCode() string { + if m != nil { + return m.Code + } + return "" +} + +// This represents a stack trace which is a ordered list of `FileLineCol`. +type GraphDebugInfo_StackTrace struct { + // Each line in the stack trace. + FileLineCols []*GraphDebugInfo_FileLineCol `protobuf:"bytes,1,rep,name=file_line_cols,json=fileLineCols,proto3" json:"file_line_cols,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GraphDebugInfo_StackTrace) Reset() { *m = GraphDebugInfo_StackTrace{} } +func (m *GraphDebugInfo_StackTrace) String() string { return proto.CompactTextString(m) } +func (*GraphDebugInfo_StackTrace) ProtoMessage() {} +func (*GraphDebugInfo_StackTrace) Descriptor() ([]byte, []int) { + return fileDescriptor_2d49d5c184d173e1, []int{0, 1} +} + +func (m *GraphDebugInfo_StackTrace) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GraphDebugInfo_StackTrace.Unmarshal(m, b) +} +func (m *GraphDebugInfo_StackTrace) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GraphDebugInfo_StackTrace.Marshal(b, m, deterministic) +} +func (m *GraphDebugInfo_StackTrace) XXX_Merge(src proto.Message) { + xxx_messageInfo_GraphDebugInfo_StackTrace.Merge(m, src) +} +func (m *GraphDebugInfo_StackTrace) XXX_Size() int { + return xxx_messageInfo_GraphDebugInfo_StackTrace.Size(m) +} +func (m *GraphDebugInfo_StackTrace) XXX_DiscardUnknown() { + xxx_messageInfo_GraphDebugInfo_StackTrace.DiscardUnknown(m) +} + +var xxx_messageInfo_GraphDebugInfo_StackTrace proto.InternalMessageInfo + +func (m *GraphDebugInfo_StackTrace) GetFileLineCols() []*GraphDebugInfo_FileLineCol { + if m != nil { + return m.FileLineCols + } + return nil +} + +func init() { + proto.RegisterType((*GraphDebugInfo)(nil), "tensorflow.GraphDebugInfo") + proto.RegisterMapType((map[string]*GraphDebugInfo_StackTrace)(nil), "tensorflow.GraphDebugInfo.TracesEntry") + proto.RegisterType((*GraphDebugInfo_FileLineCol)(nil), "tensorflow.GraphDebugInfo.FileLineCol") + proto.RegisterType((*GraphDebugInfo_StackTrace)(nil), "tensorflow.GraphDebugInfo.StackTrace") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/graph_debug_info.proto", fileDescriptor_2d49d5c184d173e1) +} + +var fileDescriptor_2d49d5c184d173e1 = []byte{ + // 367 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x7c, 0x92, 0x5d, 0xeb, 0xd3, 0x30, + 0x14, 0xc6, 0xc9, 0xfa, 0xef, 0xa0, 0xa7, 0x32, 0x24, 0xec, 0x22, 0x0c, 0x84, 0x22, 0x28, 0xbd, + 0x6a, 0x61, 0xde, 0x88, 0x82, 0x17, 0xf3, 0x8d, 0xc1, 0x2e, 0x46, 0xd5, 0x9b, 0xdd, 0xd4, 0x36, + 0x4b, 0xba, 0xb2, 0x2c, 0x67, 0xa4, 0xad, 0x73, 0x7e, 0x06, 0x3f, 0x84, 0x1f, 0xd3, 0x4b, 0x49, + 0x2a, 0xb4, 0x13, 0xd9, 0xdd, 0x2f, 0x4f, 0xce, 0x79, 0x72, 0x1e, 0x72, 0x20, 0x6d, 0x85, 0x6e, + 0xd0, 0x48, 0x85, 0x97, 0x94, 0xa3, 0x11, 0xe9, 0xd9, 0x60, 0x8b, 0x65, 0x27, 0xd3, 0xca, 0x14, + 0xe7, 0x43, 0xbe, 0x17, 0x65, 0x57, 0xe5, 0xb5, 0x96, 0x98, 0xb8, 0x1b, 0x0a, 0x43, 0xc3, 0xd3, + 0x5f, 0x1e, 0xcc, 0x3e, 0xda, 0xb2, 0x77, 0xb6, 0x6a, 0xad, 0x25, 0xd2, 0x39, 0xf8, 0xb2, 0x56, + 0xa2, 0x61, 0x24, 0xf2, 0xe2, 0x20, 0xeb, 0x0f, 0xf4, 0x0d, 0x4c, 0x5b, 0x53, 0x70, 0xd1, 0xb0, + 0x49, 0xe4, 0xc5, 0xe1, 0xf2, 0x79, 0x32, 0xb8, 0x24, 0xb7, 0x0e, 0xc9, 0x67, 0x57, 0xf8, 0x5e, + 0xb7, 0xe6, 0x9a, 0xfd, 0xed, 0x5a, 0xfc, 0x80, 0xf0, 0x43, 0xad, 0xc4, 0xa6, 0xd6, 0xe2, 0x2d, + 0x2a, 0xfa, 0x04, 0xc0, 0xfa, 0xe6, 0xb5, 0xde, 0x8b, 0xef, 0x8c, 0x44, 0x24, 0xf6, 0xb3, 0xc0, + 0x2a, 0x6b, 0x2b, 0x50, 0x0a, 0x0f, 0xaa, 0xd6, 0x82, 0x4d, 0xdc, 0x85, 0x63, 0xfa, 0x18, 0x3c, + 0x8e, 0x8a, 0x79, 0x4e, 0xb2, 0x68, 0xab, 0x64, 0xa7, 0x39, 0x7b, 0x88, 0x48, 0x1c, 0x64, 0x8e, + 0xad, 0xc6, 0x71, 0x2f, 0x98, 0xdf, 0x6b, 0x96, 0x17, 0x3b, 0x80, 0x4f, 0x6d, 0xc1, 0x8f, 0x6e, + 0x2e, 0xba, 0x81, 0x99, 0x7b, 0xda, 0x9a, 0xe6, 0x1c, 0x55, 0x1f, 0xf4, 0x7e, 0xa2, 0xd1, 0xe8, + 0xd9, 0x23, 0x39, 0x1c, 0x9a, 0xc5, 0x57, 0x08, 0x47, 0x71, 0xed, 0x90, 0x47, 0x71, 0x75, 0x81, + 0x82, 0xcc, 0x22, 0x7d, 0x0d, 0xfe, 0xb7, 0x42, 0x75, 0x7d, 0x96, 0x70, 0xf9, 0xec, 0xce, 0x2b, + 0xc3, 0x90, 0x59, 0xdf, 0xf3, 0x6a, 0xf2, 0x92, 0xac, 0x7e, 0x12, 0x60, 0x68, 0xaa, 0x71, 0x9f, + 0x34, 0xc5, 0x49, 0x5c, 0xd0, 0x1c, 0x57, 0xf3, 0x5b, 0x8b, 0xad, 0xfd, 0xe0, 0x66, 0x4b, 0x76, + 0x5f, 0xaa, 0xba, 0x3d, 0x74, 0x65, 0xc2, 0xf1, 0x34, 0xde, 0x8f, 0xff, 0x63, 0x85, 0xff, 0x2c, + 0x8e, 0x44, 0x93, 0x5b, 0x25, 0x77, 0x4a, 0x93, 0x57, 0xd8, 0xd3, 0x6f, 0x42, 0xca, 0xa9, 0xa3, + 0x17, 0x7f, 0x02, 0x00, 0x00, 0xff, 0xff, 0xbc, 0x88, 0x9f, 0xb3, 0x77, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/master.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/master.pb.go new file mode 100644 index 0000000..a166f58 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/master.pb.go @@ -0,0 +1,1213 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/master.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + device_attributes_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/device_attributes_go_proto" + graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type CreateSessionRequest struct { + // The initial graph definition. + GraphDef *graph_go_proto.GraphDef `protobuf:"bytes,1,opt,name=graph_def,json=graphDef,proto3" json:"graph_def,omitempty"` + // Configuration options. + Config *ConfigProto `protobuf:"bytes,2,opt,name=config,proto3" json:"config,omitempty"` + // The target string used from the client's perspective. + Target string `protobuf:"bytes,3,opt,name=target,proto3" json:"target,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CreateSessionRequest) Reset() { *m = CreateSessionRequest{} } +func (m *CreateSessionRequest) String() string { return proto.CompactTextString(m) } +func (*CreateSessionRequest) ProtoMessage() {} +func (*CreateSessionRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{0} +} + +func (m *CreateSessionRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CreateSessionRequest.Unmarshal(m, b) +} +func (m *CreateSessionRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CreateSessionRequest.Marshal(b, m, deterministic) +} +func (m *CreateSessionRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CreateSessionRequest.Merge(m, src) +} +func (m *CreateSessionRequest) XXX_Size() int { + return xxx_messageInfo_CreateSessionRequest.Size(m) +} +func (m *CreateSessionRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CreateSessionRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CreateSessionRequest proto.InternalMessageInfo + +func (m *CreateSessionRequest) GetGraphDef() *graph_go_proto.GraphDef { + if m != nil { + return m.GraphDef + } + return nil +} + +func (m *CreateSessionRequest) GetConfig() *ConfigProto { + if m != nil { + return m.Config + } + return nil +} + +func (m *CreateSessionRequest) GetTarget() string { + if m != nil { + return m.Target + } + return "" +} + +type CreateSessionResponse struct { + // The session handle to be used in subsequent calls for the created session. + // + // The client must arrange to call CloseSession with this returned + // session handle to close the session. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // The initial version number for the graph, to be used in the next call + // to ExtendSession. + GraphVersion int64 `protobuf:"varint,2,opt,name=graph_version,json=graphVersion,proto3" json:"graph_version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CreateSessionResponse) Reset() { *m = CreateSessionResponse{} } +func (m *CreateSessionResponse) String() string { return proto.CompactTextString(m) } +func (*CreateSessionResponse) ProtoMessage() {} +func (*CreateSessionResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{1} +} + +func (m *CreateSessionResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CreateSessionResponse.Unmarshal(m, b) +} +func (m *CreateSessionResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CreateSessionResponse.Marshal(b, m, deterministic) +} +func (m *CreateSessionResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CreateSessionResponse.Merge(m, src) +} +func (m *CreateSessionResponse) XXX_Size() int { + return xxx_messageInfo_CreateSessionResponse.Size(m) +} +func (m *CreateSessionResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CreateSessionResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CreateSessionResponse proto.InternalMessageInfo + +func (m *CreateSessionResponse) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *CreateSessionResponse) GetGraphVersion() int64 { + if m != nil { + return m.GraphVersion + } + return 0 +} + +type ExtendSessionRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // REQUIRED: The nodes to be added to the session's graph. If any node has + // the same name as an existing node, the operation will fail with + // ILLEGAL_ARGUMENT. + GraphDef *graph_go_proto.GraphDef `protobuf:"bytes,2,opt,name=graph_def,json=graphDef,proto3" json:"graph_def,omitempty"` + // REQUIRED: The version number of the graph to be extended. This will be + // tested against the current server-side version number, and the operation + // will fail with FAILED_PRECONDITION if they do not match. + CurrentGraphVersion int64 `protobuf:"varint,3,opt,name=current_graph_version,json=currentGraphVersion,proto3" json:"current_graph_version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ExtendSessionRequest) Reset() { *m = ExtendSessionRequest{} } +func (m *ExtendSessionRequest) String() string { return proto.CompactTextString(m) } +func (*ExtendSessionRequest) ProtoMessage() {} +func (*ExtendSessionRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{2} +} + +func (m *ExtendSessionRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ExtendSessionRequest.Unmarshal(m, b) +} +func (m *ExtendSessionRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ExtendSessionRequest.Marshal(b, m, deterministic) +} +func (m *ExtendSessionRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_ExtendSessionRequest.Merge(m, src) +} +func (m *ExtendSessionRequest) XXX_Size() int { + return xxx_messageInfo_ExtendSessionRequest.Size(m) +} +func (m *ExtendSessionRequest) XXX_DiscardUnknown() { + xxx_messageInfo_ExtendSessionRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_ExtendSessionRequest proto.InternalMessageInfo + +func (m *ExtendSessionRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *ExtendSessionRequest) GetGraphDef() *graph_go_proto.GraphDef { + if m != nil { + return m.GraphDef + } + return nil +} + +func (m *ExtendSessionRequest) GetCurrentGraphVersion() int64 { + if m != nil { + return m.CurrentGraphVersion + } + return 0 +} + +type ExtendSessionResponse struct { + // The new version number for the extended graph, to be used in the next call + // to ExtendSession. + NewGraphVersion int64 `protobuf:"varint,4,opt,name=new_graph_version,json=newGraphVersion,proto3" json:"new_graph_version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ExtendSessionResponse) Reset() { *m = ExtendSessionResponse{} } +func (m *ExtendSessionResponse) String() string { return proto.CompactTextString(m) } +func (*ExtendSessionResponse) ProtoMessage() {} +func (*ExtendSessionResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{3} +} + +func (m *ExtendSessionResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ExtendSessionResponse.Unmarshal(m, b) +} +func (m *ExtendSessionResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ExtendSessionResponse.Marshal(b, m, deterministic) +} +func (m *ExtendSessionResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_ExtendSessionResponse.Merge(m, src) +} +func (m *ExtendSessionResponse) XXX_Size() int { + return xxx_messageInfo_ExtendSessionResponse.Size(m) +} +func (m *ExtendSessionResponse) XXX_DiscardUnknown() { + xxx_messageInfo_ExtendSessionResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_ExtendSessionResponse proto.InternalMessageInfo + +func (m *ExtendSessionResponse) GetNewGraphVersion() int64 { + if m != nil { + return m.NewGraphVersion + } + return 0 +} + +type RunStepRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Tensors to be fed in the step. Each feed is a named tensor. + Feed []*NamedTensorProto `protobuf:"bytes,2,rep,name=feed,proto3" json:"feed,omitempty"` + // Fetches. A list of tensor names. The caller expects a tensor to + // be returned for each fetch[i] (see RunStepResponse.tensor). The + // order of specified fetches does not change the execution order. + Fetch []string `protobuf:"bytes,3,rep,name=fetch,proto3" json:"fetch,omitempty"` + // Target Nodes. A list of node names. The named nodes will be run + // to but their outputs will not be fetched. + Target []string `protobuf:"bytes,4,rep,name=target,proto3" json:"target,omitempty"` + // Options for the run call. + Options *RunOptions `protobuf:"bytes,5,opt,name=options,proto3" json:"options,omitempty"` + // Partial run handle (optional). If specified, this will be a partial run + // execution, run up to the specified fetches. + PartialRunHandle string `protobuf:"bytes,6,opt,name=partial_run_handle,json=partialRunHandle,proto3" json:"partial_run_handle,omitempty"` + // If true then some errors, e.g., execution errors that have long + // error messages, may return an OK RunStepResponse with the actual + // error saved in the status_code/status_error_message fields of the + // response body. This is a workaround since the RPC subsystem may + // truncate long metadata messages. + StoreErrorsInResponseBody bool `protobuf:"varint,7,opt,name=store_errors_in_response_body,json=storeErrorsInResponseBody,proto3" json:"store_errors_in_response_body,omitempty"` + // Unique identifier for this request. Every RunStepRequest must + // have a unique request_id, and retried RunStepRequest must have + // the same request_id. If request_id is zero, retry detection is disabled. + RequestId int64 `protobuf:"varint,8,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunStepRequest) Reset() { *m = RunStepRequest{} } +func (m *RunStepRequest) String() string { return proto.CompactTextString(m) } +func (*RunStepRequest) ProtoMessage() {} +func (*RunStepRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{4} +} + +func (m *RunStepRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunStepRequest.Unmarshal(m, b) +} +func (m *RunStepRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunStepRequest.Marshal(b, m, deterministic) +} +func (m *RunStepRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunStepRequest.Merge(m, src) +} +func (m *RunStepRequest) XXX_Size() int { + return xxx_messageInfo_RunStepRequest.Size(m) +} +func (m *RunStepRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RunStepRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RunStepRequest proto.InternalMessageInfo + +func (m *RunStepRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *RunStepRequest) GetFeed() []*NamedTensorProto { + if m != nil { + return m.Feed + } + return nil +} + +func (m *RunStepRequest) GetFetch() []string { + if m != nil { + return m.Fetch + } + return nil +} + +func (m *RunStepRequest) GetTarget() []string { + if m != nil { + return m.Target + } + return nil +} + +func (m *RunStepRequest) GetOptions() *RunOptions { + if m != nil { + return m.Options + } + return nil +} + +func (m *RunStepRequest) GetPartialRunHandle() string { + if m != nil { + return m.PartialRunHandle + } + return "" +} + +func (m *RunStepRequest) GetStoreErrorsInResponseBody() bool { + if m != nil { + return m.StoreErrorsInResponseBody + } + return false +} + +func (m *RunStepRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type RunStepResponse struct { + // NOTE: The order of the returned tensors may or may not match + // the fetch order specified in RunStepRequest. + Tensor []*NamedTensorProto `protobuf:"bytes,1,rep,name=tensor,proto3" json:"tensor,omitempty"` + // Returned metadata if requested in the options. + Metadata *RunMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` + // If store_errors_in_response_body is true in the request, then + // optionally the server may return an OK status for the RPC and + // fill the true status into the fields below, to allow for messages + // that are too long to fit in metadata. + StatusCode Code `protobuf:"varint,3,opt,name=status_code,json=statusCode,proto3,enum=tensorflow.error.Code" json:"status_code,omitempty"` + StatusErrorMessage string `protobuf:"bytes,4,opt,name=status_error_message,json=statusErrorMessage,proto3" json:"status_error_message,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunStepResponse) Reset() { *m = RunStepResponse{} } +func (m *RunStepResponse) String() string { return proto.CompactTextString(m) } +func (*RunStepResponse) ProtoMessage() {} +func (*RunStepResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{5} +} + +func (m *RunStepResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunStepResponse.Unmarshal(m, b) +} +func (m *RunStepResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunStepResponse.Marshal(b, m, deterministic) +} +func (m *RunStepResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunStepResponse.Merge(m, src) +} +func (m *RunStepResponse) XXX_Size() int { + return xxx_messageInfo_RunStepResponse.Size(m) +} +func (m *RunStepResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RunStepResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RunStepResponse proto.InternalMessageInfo + +func (m *RunStepResponse) GetTensor() []*NamedTensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +func (m *RunStepResponse) GetMetadata() *RunMetadata { + if m != nil { + return m.Metadata + } + return nil +} + +func (m *RunStepResponse) GetStatusCode() Code { + if m != nil { + return m.StatusCode + } + return Code_OK +} + +func (m *RunStepResponse) GetStatusErrorMessage() string { + if m != nil { + return m.StatusErrorMessage + } + return "" +} + +type PartialRunSetupRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Tensors to be fed in future steps. + Feed []string `protobuf:"bytes,2,rep,name=feed,proto3" json:"feed,omitempty"` + // Fetches. A list of tensor names. The caller expects a tensor to be returned + // for each fetch[i] (see RunStepResponse.tensor), for corresponding partial + // RunStepRequests. The order of specified fetches does not change the + // execution order. + Fetch []string `protobuf:"bytes,3,rep,name=fetch,proto3" json:"fetch,omitempty"` + // Target Nodes. A list of node names. The named nodes will be run in future + // steps, but their outputs will not be fetched. + Target []string `protobuf:"bytes,4,rep,name=target,proto3" json:"target,omitempty"` + // Unique identifier for this request. Every PartialRunSetupRequest must + // have a unique request_id, and retried PartialRunSetupRequest must have + // the same request_id. If request_id is zero, retry detection is disabled. + RequestId int64 `protobuf:"varint,5,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *PartialRunSetupRequest) Reset() { *m = PartialRunSetupRequest{} } +func (m *PartialRunSetupRequest) String() string { return proto.CompactTextString(m) } +func (*PartialRunSetupRequest) ProtoMessage() {} +func (*PartialRunSetupRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{6} +} + +func (m *PartialRunSetupRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_PartialRunSetupRequest.Unmarshal(m, b) +} +func (m *PartialRunSetupRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_PartialRunSetupRequest.Marshal(b, m, deterministic) +} +func (m *PartialRunSetupRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_PartialRunSetupRequest.Merge(m, src) +} +func (m *PartialRunSetupRequest) XXX_Size() int { + return xxx_messageInfo_PartialRunSetupRequest.Size(m) +} +func (m *PartialRunSetupRequest) XXX_DiscardUnknown() { + xxx_messageInfo_PartialRunSetupRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_PartialRunSetupRequest proto.InternalMessageInfo + +func (m *PartialRunSetupRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *PartialRunSetupRequest) GetFeed() []string { + if m != nil { + return m.Feed + } + return nil +} + +func (m *PartialRunSetupRequest) GetFetch() []string { + if m != nil { + return m.Fetch + } + return nil +} + +func (m *PartialRunSetupRequest) GetTarget() []string { + if m != nil { + return m.Target + } + return nil +} + +func (m *PartialRunSetupRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type PartialRunSetupResponse struct { + // The unique handle corresponding to the ongoing partial run call setup by + // the invocation to PartialRunSetup. This handle may be passed to + // RunStepRequest to send and receive tensors for this partial run. + PartialRunHandle string `protobuf:"bytes,1,opt,name=partial_run_handle,json=partialRunHandle,proto3" json:"partial_run_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *PartialRunSetupResponse) Reset() { *m = PartialRunSetupResponse{} } +func (m *PartialRunSetupResponse) String() string { return proto.CompactTextString(m) } +func (*PartialRunSetupResponse) ProtoMessage() {} +func (*PartialRunSetupResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{7} +} + +func (m *PartialRunSetupResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_PartialRunSetupResponse.Unmarshal(m, b) +} +func (m *PartialRunSetupResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_PartialRunSetupResponse.Marshal(b, m, deterministic) +} +func (m *PartialRunSetupResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_PartialRunSetupResponse.Merge(m, src) +} +func (m *PartialRunSetupResponse) XXX_Size() int { + return xxx_messageInfo_PartialRunSetupResponse.Size(m) +} +func (m *PartialRunSetupResponse) XXX_DiscardUnknown() { + xxx_messageInfo_PartialRunSetupResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_PartialRunSetupResponse proto.InternalMessageInfo + +func (m *PartialRunSetupResponse) GetPartialRunHandle() string { + if m != nil { + return m.PartialRunHandle + } + return "" +} + +type CloseSessionRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CloseSessionRequest) Reset() { *m = CloseSessionRequest{} } +func (m *CloseSessionRequest) String() string { return proto.CompactTextString(m) } +func (*CloseSessionRequest) ProtoMessage() {} +func (*CloseSessionRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{8} +} + +func (m *CloseSessionRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CloseSessionRequest.Unmarshal(m, b) +} +func (m *CloseSessionRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CloseSessionRequest.Marshal(b, m, deterministic) +} +func (m *CloseSessionRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CloseSessionRequest.Merge(m, src) +} +func (m *CloseSessionRequest) XXX_Size() int { + return xxx_messageInfo_CloseSessionRequest.Size(m) +} +func (m *CloseSessionRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CloseSessionRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CloseSessionRequest proto.InternalMessageInfo + +func (m *CloseSessionRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +type CloseSessionResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CloseSessionResponse) Reset() { *m = CloseSessionResponse{} } +func (m *CloseSessionResponse) String() string { return proto.CompactTextString(m) } +func (*CloseSessionResponse) ProtoMessage() {} +func (*CloseSessionResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{9} +} + +func (m *CloseSessionResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CloseSessionResponse.Unmarshal(m, b) +} +func (m *CloseSessionResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CloseSessionResponse.Marshal(b, m, deterministic) +} +func (m *CloseSessionResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CloseSessionResponse.Merge(m, src) +} +func (m *CloseSessionResponse) XXX_Size() int { + return xxx_messageInfo_CloseSessionResponse.Size(m) +} +func (m *CloseSessionResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CloseSessionResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CloseSessionResponse proto.InternalMessageInfo + +// Reset() allows misbehaving or slow sessions to be aborted and closed, and +// causes their resources eventually to be released. Reset() does not wait +// for the computations in old sessions to cease; it merely starts the +// process of tearing them down. However, if a new session is started after +// a Reset(), the new session is isolated from changes that old sessions +// (started prior to the Reset()) may continue to make to resources, provided +// all those resources are in containers listed in "containers". +// +// Old sessions may continue to have side-effects on resources not in +// containers listed in "containers", and thus may affect future +// sessions' results in ways that are hard to predict. Thus, if well-defined +// behavior is desired, is it recommended that all containers be listed in +// "containers". Similarly, if a device_filter is specified, results may be +// hard to predict. +type ResetRequest struct { + // A list of container names, which may be empty. + // + // If 'container' is not empty, releases resources in the given + // containers in all devices. + // + // If 'container' is empty, releases resources in the default + // container in all devices. + Container []string `protobuf:"bytes,1,rep,name=container,proto3" json:"container,omitempty"` + // When any filters are present, only devices that match the filters + // will be reset. Each filter can be partially specified, + // e.g. "/job:ps" "/job:worker/replica:3", etc. + DeviceFilters []string `protobuf:"bytes,2,rep,name=device_filters,json=deviceFilters,proto3" json:"device_filters,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ResetRequest) Reset() { *m = ResetRequest{} } +func (m *ResetRequest) String() string { return proto.CompactTextString(m) } +func (*ResetRequest) ProtoMessage() {} +func (*ResetRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{10} +} + +func (m *ResetRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ResetRequest.Unmarshal(m, b) +} +func (m *ResetRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ResetRequest.Marshal(b, m, deterministic) +} +func (m *ResetRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_ResetRequest.Merge(m, src) +} +func (m *ResetRequest) XXX_Size() int { + return xxx_messageInfo_ResetRequest.Size(m) +} +func (m *ResetRequest) XXX_DiscardUnknown() { + xxx_messageInfo_ResetRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_ResetRequest proto.InternalMessageInfo + +func (m *ResetRequest) GetContainer() []string { + if m != nil { + return m.Container + } + return nil +} + +func (m *ResetRequest) GetDeviceFilters() []string { + if m != nil { + return m.DeviceFilters + } + return nil +} + +type ResetResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ResetResponse) Reset() { *m = ResetResponse{} } +func (m *ResetResponse) String() string { return proto.CompactTextString(m) } +func (*ResetResponse) ProtoMessage() {} +func (*ResetResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{11} +} + +func (m *ResetResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ResetResponse.Unmarshal(m, b) +} +func (m *ResetResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ResetResponse.Marshal(b, m, deterministic) +} +func (m *ResetResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_ResetResponse.Merge(m, src) +} +func (m *ResetResponse) XXX_Size() int { + return xxx_messageInfo_ResetResponse.Size(m) +} +func (m *ResetResponse) XXX_DiscardUnknown() { + xxx_messageInfo_ResetResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_ResetResponse proto.InternalMessageInfo + +type ListDevicesRequest struct { + // Optional: session_handle must be returned by a CreateSession call to the + // same master service. + // + // When session_handle is empty, the ClusterSpec provided when the master was + // started is used to compute the available devices. If the session_handle is + // provided but not recognized, an error is returned. Finally, if a valid + // session_handle is provided, the cluster configuration for that session is + // used when computing the response. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ListDevicesRequest) Reset() { *m = ListDevicesRequest{} } +func (m *ListDevicesRequest) String() string { return proto.CompactTextString(m) } +func (*ListDevicesRequest) ProtoMessage() {} +func (*ListDevicesRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{12} +} + +func (m *ListDevicesRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ListDevicesRequest.Unmarshal(m, b) +} +func (m *ListDevicesRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ListDevicesRequest.Marshal(b, m, deterministic) +} +func (m *ListDevicesRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_ListDevicesRequest.Merge(m, src) +} +func (m *ListDevicesRequest) XXX_Size() int { + return xxx_messageInfo_ListDevicesRequest.Size(m) +} +func (m *ListDevicesRequest) XXX_DiscardUnknown() { + xxx_messageInfo_ListDevicesRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_ListDevicesRequest proto.InternalMessageInfo + +func (m *ListDevicesRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +type ListDevicesResponse struct { + LocalDevice []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,1,rep,name=local_device,json=localDevice,proto3" json:"local_device,omitempty"` + RemoteDevice []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,2,rep,name=remote_device,json=remoteDevice,proto3" json:"remote_device,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ListDevicesResponse) Reset() { *m = ListDevicesResponse{} } +func (m *ListDevicesResponse) String() string { return proto.CompactTextString(m) } +func (*ListDevicesResponse) ProtoMessage() {} +func (*ListDevicesResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{13} +} + +func (m *ListDevicesResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ListDevicesResponse.Unmarshal(m, b) +} +func (m *ListDevicesResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ListDevicesResponse.Marshal(b, m, deterministic) +} +func (m *ListDevicesResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_ListDevicesResponse.Merge(m, src) +} +func (m *ListDevicesResponse) XXX_Size() int { + return xxx_messageInfo_ListDevicesResponse.Size(m) +} +func (m *ListDevicesResponse) XXX_DiscardUnknown() { + xxx_messageInfo_ListDevicesResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_ListDevicesResponse proto.InternalMessageInfo + +func (m *ListDevicesResponse) GetLocalDevice() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.LocalDevice + } + return nil +} + +func (m *ListDevicesResponse) GetRemoteDevice() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.RemoteDevice + } + return nil +} + +type MakeCallableRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Options that define the behavior of the created callable. + Options *CallableOptions `protobuf:"bytes,2,opt,name=options,proto3" json:"options,omitempty"` + // Unique identifier for this request. Every MakeCallableRequest must + // have a unique request_id, and retried MakeCallableRequest must have + // the same request_id. If request_id is zero, retry detection is disabled. + RequestId int64 `protobuf:"varint,3,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MakeCallableRequest) Reset() { *m = MakeCallableRequest{} } +func (m *MakeCallableRequest) String() string { return proto.CompactTextString(m) } +func (*MakeCallableRequest) ProtoMessage() {} +func (*MakeCallableRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{14} +} + +func (m *MakeCallableRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MakeCallableRequest.Unmarshal(m, b) +} +func (m *MakeCallableRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MakeCallableRequest.Marshal(b, m, deterministic) +} +func (m *MakeCallableRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_MakeCallableRequest.Merge(m, src) +} +func (m *MakeCallableRequest) XXX_Size() int { + return xxx_messageInfo_MakeCallableRequest.Size(m) +} +func (m *MakeCallableRequest) XXX_DiscardUnknown() { + xxx_messageInfo_MakeCallableRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_MakeCallableRequest proto.InternalMessageInfo + +func (m *MakeCallableRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *MakeCallableRequest) GetOptions() *CallableOptions { + if m != nil { + return m.Options + } + return nil +} + +func (m *MakeCallableRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type MakeCallableResponse struct { + // A handle to the created callable. + Handle int64 `protobuf:"varint,1,opt,name=handle,proto3" json:"handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MakeCallableResponse) Reset() { *m = MakeCallableResponse{} } +func (m *MakeCallableResponse) String() string { return proto.CompactTextString(m) } +func (*MakeCallableResponse) ProtoMessage() {} +func (*MakeCallableResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{15} +} + +func (m *MakeCallableResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MakeCallableResponse.Unmarshal(m, b) +} +func (m *MakeCallableResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MakeCallableResponse.Marshal(b, m, deterministic) +} +func (m *MakeCallableResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_MakeCallableResponse.Merge(m, src) +} +func (m *MakeCallableResponse) XXX_Size() int { + return xxx_messageInfo_MakeCallableResponse.Size(m) +} +func (m *MakeCallableResponse) XXX_DiscardUnknown() { + xxx_messageInfo_MakeCallableResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_MakeCallableResponse proto.InternalMessageInfo + +func (m *MakeCallableResponse) GetHandle() int64 { + if m != nil { + return m.Handle + } + return 0 +} + +type RunCallableRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // REQUIRED: handle must be returned by a MakeCallable call to the same + // master service. + Handle int64 `protobuf:"varint,2,opt,name=handle,proto3" json:"handle,omitempty"` + // Values of the tensors passed as arguments to the callable, in the order + // defined in the CallableOptions.feed field passed to MakeCallable. + Feed []*tensor_go_proto.TensorProto `protobuf:"bytes,3,rep,name=feed,proto3" json:"feed,omitempty"` + // Unique identifier for this request. Every RunCallableRequest must + // have a unique request_id, and retried RunCallableRequest must have + // the same request_id. If request_id is zero, retry detection is disabled. + RequestId int64 `protobuf:"varint,4,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunCallableRequest) Reset() { *m = RunCallableRequest{} } +func (m *RunCallableRequest) String() string { return proto.CompactTextString(m) } +func (*RunCallableRequest) ProtoMessage() {} +func (*RunCallableRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{16} +} + +func (m *RunCallableRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunCallableRequest.Unmarshal(m, b) +} +func (m *RunCallableRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunCallableRequest.Marshal(b, m, deterministic) +} +func (m *RunCallableRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunCallableRequest.Merge(m, src) +} +func (m *RunCallableRequest) XXX_Size() int { + return xxx_messageInfo_RunCallableRequest.Size(m) +} +func (m *RunCallableRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RunCallableRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RunCallableRequest proto.InternalMessageInfo + +func (m *RunCallableRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *RunCallableRequest) GetHandle() int64 { + if m != nil { + return m.Handle + } + return 0 +} + +func (m *RunCallableRequest) GetFeed() []*tensor_go_proto.TensorProto { + if m != nil { + return m.Feed + } + return nil +} + +func (m *RunCallableRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type RunCallableResponse struct { + // Values of the tensors returned by the callable, in the order defined in the + // CallableOptions.fetch field passed to MakeCallable. + Fetch []*tensor_go_proto.TensorProto `protobuf:"bytes,1,rep,name=fetch,proto3" json:"fetch,omitempty"` + // Returned metadata if requested in the options. + Metadata *RunMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunCallableResponse) Reset() { *m = RunCallableResponse{} } +func (m *RunCallableResponse) String() string { return proto.CompactTextString(m) } +func (*RunCallableResponse) ProtoMessage() {} +func (*RunCallableResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{17} +} + +func (m *RunCallableResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunCallableResponse.Unmarshal(m, b) +} +func (m *RunCallableResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunCallableResponse.Marshal(b, m, deterministic) +} +func (m *RunCallableResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunCallableResponse.Merge(m, src) +} +func (m *RunCallableResponse) XXX_Size() int { + return xxx_messageInfo_RunCallableResponse.Size(m) +} +func (m *RunCallableResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RunCallableResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RunCallableResponse proto.InternalMessageInfo + +func (m *RunCallableResponse) GetFetch() []*tensor_go_proto.TensorProto { + if m != nil { + return m.Fetch + } + return nil +} + +func (m *RunCallableResponse) GetMetadata() *RunMetadata { + if m != nil { + return m.Metadata + } + return nil +} + +type ReleaseCallableRequest struct { + // REQUIRED: session_handle must be returned by a CreateSession call + // to the same master service. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // REQUIRED: handle must be returned by a MakeCallable call to the same + // master service. + Handle int64 `protobuf:"varint,2,opt,name=handle,proto3" json:"handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ReleaseCallableRequest) Reset() { *m = ReleaseCallableRequest{} } +func (m *ReleaseCallableRequest) String() string { return proto.CompactTextString(m) } +func (*ReleaseCallableRequest) ProtoMessage() {} +func (*ReleaseCallableRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{18} +} + +func (m *ReleaseCallableRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ReleaseCallableRequest.Unmarshal(m, b) +} +func (m *ReleaseCallableRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ReleaseCallableRequest.Marshal(b, m, deterministic) +} +func (m *ReleaseCallableRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_ReleaseCallableRequest.Merge(m, src) +} +func (m *ReleaseCallableRequest) XXX_Size() int { + return xxx_messageInfo_ReleaseCallableRequest.Size(m) +} +func (m *ReleaseCallableRequest) XXX_DiscardUnknown() { + xxx_messageInfo_ReleaseCallableRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_ReleaseCallableRequest proto.InternalMessageInfo + +func (m *ReleaseCallableRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *ReleaseCallableRequest) GetHandle() int64 { + if m != nil { + return m.Handle + } + return 0 +} + +type ReleaseCallableResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ReleaseCallableResponse) Reset() { *m = ReleaseCallableResponse{} } +func (m *ReleaseCallableResponse) String() string { return proto.CompactTextString(m) } +func (*ReleaseCallableResponse) ProtoMessage() {} +func (*ReleaseCallableResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_5171b2a5dcde72cd, []int{19} +} + +func (m *ReleaseCallableResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ReleaseCallableResponse.Unmarshal(m, b) +} +func (m *ReleaseCallableResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ReleaseCallableResponse.Marshal(b, m, deterministic) +} +func (m *ReleaseCallableResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_ReleaseCallableResponse.Merge(m, src) +} +func (m *ReleaseCallableResponse) XXX_Size() int { + return xxx_messageInfo_ReleaseCallableResponse.Size(m) +} +func (m *ReleaseCallableResponse) XXX_DiscardUnknown() { + xxx_messageInfo_ReleaseCallableResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_ReleaseCallableResponse proto.InternalMessageInfo + +func init() { + proto.RegisterType((*CreateSessionRequest)(nil), "tensorflow.CreateSessionRequest") + proto.RegisterType((*CreateSessionResponse)(nil), "tensorflow.CreateSessionResponse") + proto.RegisterType((*ExtendSessionRequest)(nil), "tensorflow.ExtendSessionRequest") + proto.RegisterType((*ExtendSessionResponse)(nil), "tensorflow.ExtendSessionResponse") + proto.RegisterType((*RunStepRequest)(nil), "tensorflow.RunStepRequest") + proto.RegisterType((*RunStepResponse)(nil), "tensorflow.RunStepResponse") + proto.RegisterType((*PartialRunSetupRequest)(nil), "tensorflow.PartialRunSetupRequest") + proto.RegisterType((*PartialRunSetupResponse)(nil), "tensorflow.PartialRunSetupResponse") + proto.RegisterType((*CloseSessionRequest)(nil), "tensorflow.CloseSessionRequest") + proto.RegisterType((*CloseSessionResponse)(nil), "tensorflow.CloseSessionResponse") + proto.RegisterType((*ResetRequest)(nil), "tensorflow.ResetRequest") + proto.RegisterType((*ResetResponse)(nil), "tensorflow.ResetResponse") + proto.RegisterType((*ListDevicesRequest)(nil), "tensorflow.ListDevicesRequest") + proto.RegisterType((*ListDevicesResponse)(nil), "tensorflow.ListDevicesResponse") + proto.RegisterType((*MakeCallableRequest)(nil), "tensorflow.MakeCallableRequest") + proto.RegisterType((*MakeCallableResponse)(nil), "tensorflow.MakeCallableResponse") + proto.RegisterType((*RunCallableRequest)(nil), "tensorflow.RunCallableRequest") + proto.RegisterType((*RunCallableResponse)(nil), "tensorflow.RunCallableResponse") + proto.RegisterType((*ReleaseCallableRequest)(nil), "tensorflow.ReleaseCallableRequest") + proto.RegisterType((*ReleaseCallableResponse)(nil), "tensorflow.ReleaseCallableResponse") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/master.proto", fileDescriptor_5171b2a5dcde72cd) +} + +var fileDescriptor_5171b2a5dcde72cd = []byte{ + // 961 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xac, 0x56, 0x5f, 0x6f, 0x1b, 0x45, + 0x10, 0xd7, 0xd9, 0xa9, 0x1b, 0x4f, 0xfe, 0xc1, 0xda, 0x71, 0xae, 0xa1, 0x95, 0xac, 0x43, 0x41, + 0x56, 0x0b, 0x76, 0x9a, 0x82, 0x78, 0x00, 0x09, 0x5a, 0xa7, 0x84, 0x4a, 0x04, 0xa2, 0x0d, 0x7f, + 0x24, 0x5e, 0x4e, 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DO NOT EDIT. +// source: tensorflow/core/protobuf/master_service.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/master_service.proto", fileDescriptor_aec3657ea3852a92) +} + +var fileDescriptor_aec3657ea3852a92 = []byte{ + // 382 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x93, 0xcf, 0x4e, 0xf2, 0x40, + 0x14, 0xc5, 0xc3, 0xe2, 0xe3, 0x4b, 0x46, 0x09, 0x49, 0xdd, 0x68, 0x4d, 0x00, 0x31, 0x2e, 0x6d, + 0x13, 0xdd, 0xba, 0x02, 0xdd, 0x89, 0xc1, 0xa2, 0x1b, 0x36, 0xcd, 0xb4, 0x5c, 0x6a, 0x63, 0xe9, + 0xd4, 0xb9, 0xb7, 0xea, 0x33, 0xf8, 0x4a, 0xbe, 0x9c, 0xe9, 0x3f, 0x3a, 0x53, 0x0b, 0xae, 0x98, + 0x9c, 0x73, 0xee, 0x2f, 0x9c, 0xe9, 0x5c, 0x76, 0x49, 0x10, 0xa3, 0x90, 0xeb, 0x48, 0x7c, 0xd8, + 0xbe, 0x90, 0x60, 0x27, 0x52, 0x90, 0xf0, 0xd2, 0xb5, 0xbd, 0xe1, 0x48, 0x20, 0x5d, 0x04, 0xf9, + 0x1e, 0xfa, 0x60, 0xe5, 0xba, 0xd1, 0xaf, 0xe3, 0x56, 0x20, 0x13, 0xdf, 0xbc, 0xf8, 0x63, 0xbe, + 0x98, 0xbb, 0xfa, 0xee, 0xb2, 0xde, 0x2c, 0x17, 0x16, 0x05, 0xcf, 0x78, 0x62, 0xbd, 0xa9, 0x04, + 0x4e, 0xb0, 0x00, 0xc4, 0x50, 0xc4, 0xc6, 0xc8, 0x52, 0xd8, 0x9a, 0xe5, 0xc0, 0x5b, 0x0a, 0x48, + 0xe6, 0xd9, 0x9e, 0x04, 0x26, 0x22, 0xc6, 0x9c, 0x7a, 0xf7, 0x49, 0x10, 0xaf, 0x5a, 0xa9, 0x9a, + 0xd5, 0x4a, 0x6d, 0x24, 0x4a, 0xea, 0x92, 0xf5, 0xe7, 0x5c, 0x52, 0xc8, 0x23, 0x27, 0x8d, 0x17, + 0x40, 0x69, 0x62, 0x8c, 0xd5, 0xa9, 0x86, 0x59, 0x91, 0xcf, 0xf7, 0x66, 0x4a, 0xf6, 0x84, 0xfd, + 0xcf, 0x34, 0x82, 0xc4, 0x30, 0xd5, 0x7c, 0x29, 0x56, 0xac, 0xd3, 0x56, 0xaf, 0x64, 0x3c, 0xb2, + 0xc3, 0x69, 0x24, 0x70, 0x7b, 0x95, 0x43, 0xed, 0xa2, 0x14, 0xa7, 0xa2, 0x8d, 0x76, 0x07, 0x4a, + 0xe4, 0x03, 0x3b, 0xb8, 0x0f, 0x91, 0x6e, 0x21, 0xfb, 0x58, 0x68, 0x0c, 0xd4, 0x01, 0xc5, 0xa8, + 0x80, 0xc3, 0x9d, 0x7e, 0xc9, 0xbb, 0x61, 0xff, 0x1c, 0x40, 0x20, 0xe3, 0x58, 0x2b, 0x92, 0x49, + 0x15, 0xe3, 0xa4, 0xc5, 0xa9, 0x0b, 0xce, 0xf8, 0x2b, 0x4c, 0x79, 0x14, 0x71, 0x2f, 0x02, 0xbd, + 0xa0, 0xea, 0xb4, 0x16, 0xd4, 0x03, 0x75, 0x41, 0x27, 0x8d, 0xb7, 0xc4, 0x41, 0xe3, 0x7e, 0x9b, + 0xc0, 0xe1, 0x4e, 0xbf, 0x7e, 0x23, 0x0e, 0x44, 0xc0, 0xb1, 0xfe, 0x97, 0x63, 0xbd, 0x90, 0x66, + 0xb6, 0xbe, 0x91, 0x5f, 0x99, 0x82, 0x3d, 0xf9, 0xea, 0x30, 0x53, 0xc8, 0x40, 0x8d, 0xae, 0x42, + 0x24, 0x99, 0xc6, 0x14, 0x6e, 0x60, 0x72, 0xa4, 0x6d, 0xd6, 0x3c, 0x5b, 0x38, 0x9c, 0x77, 0x96, + 0xcf, 0x41, 0x48, 0x2f, 0xa9, 0x67, 0xf9, 0x62, 0x63, 0x2b, 0x5b, 0xda, 0x7e, 0x0c, 0x44, 0x63, + 0x7d, 0xd7, 0x42, 0xba, 0x99, 0xe2, 0xe6, 0x0a, 0xba, 0x81, 0x28, 0x4e, 0x5e, 0x37, 0xff, 0xb9, + 0xfe, 0x09, 0x00, 0x00, 0xff, 0xff, 0xf2, 0x4f, 0x8c, 0x01, 0x3a, 0x04, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/meta_graph.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/meta_graph.pb.go new file mode 100644 index 0000000..65b5499 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/meta_graph.pb.go @@ -0,0 +1,1168 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/meta_graph.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + any "github.com/golang/protobuf/ptypes/any" + graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto" + op_def_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// NOTE: This protocol buffer is evolving, and will go through revisions in the +// coming months. +// +// Protocol buffer containing the following which are necessary to restart +// training, run inference. It can be used to serialize/de-serialize memory +// objects necessary for running computation in a graph when crossing the +// process boundary. It can be used for long term storage of graphs, +// cross-language execution of graphs, etc. +// MetaInfoDef +// GraphDef +// SaverDef +// CollectionDef +// TensorInfo +// SignatureDef +type MetaGraphDef struct { + MetaInfoDef *MetaGraphDef_MetaInfoDef `protobuf:"bytes,1,opt,name=meta_info_def,json=metaInfoDef,proto3" json:"meta_info_def,omitempty"` + // GraphDef. + GraphDef *graph_go_proto.GraphDef `protobuf:"bytes,2,opt,name=graph_def,json=graphDef,proto3" json:"graph_def,omitempty"` + // SaverDef. + SaverDef *SaverDef `protobuf:"bytes,3,opt,name=saver_def,json=saverDef,proto3" json:"saver_def,omitempty"` + // collection_def: Map from collection name to collections. + // See CollectionDef section for details. + CollectionDef map[string]*CollectionDef `protobuf:"bytes,4,rep,name=collection_def,json=collectionDef,proto3" json:"collection_def,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // signature_def: Map from user supplied key for a signature to a single + // SignatureDef. + SignatureDef map[string]*SignatureDef `protobuf:"bytes,5,rep,name=signature_def,json=signatureDef,proto3" json:"signature_def,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // Asset file def to be used with the defined graph. + AssetFileDef []*AssetFileDef `protobuf:"bytes,6,rep,name=asset_file_def,json=assetFileDef,proto3" json:"asset_file_def,omitempty"` + // Extra information about the structure of functions and stateful objects. + ObjectGraphDef *SavedObjectGraph `protobuf:"bytes,7,opt,name=object_graph_def,json=objectGraphDef,proto3" json:"object_graph_def,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MetaGraphDef) Reset() { *m = MetaGraphDef{} } +func (m *MetaGraphDef) String() string { return proto.CompactTextString(m) } +func (*MetaGraphDef) ProtoMessage() {} +func (*MetaGraphDef) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{0} +} + +func (m *MetaGraphDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MetaGraphDef.Unmarshal(m, b) +} +func (m *MetaGraphDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MetaGraphDef.Marshal(b, m, deterministic) +} +func (m *MetaGraphDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_MetaGraphDef.Merge(m, src) +} +func (m *MetaGraphDef) XXX_Size() int { + return xxx_messageInfo_MetaGraphDef.Size(m) +} +func (m *MetaGraphDef) XXX_DiscardUnknown() { + xxx_messageInfo_MetaGraphDef.DiscardUnknown(m) +} + +var xxx_messageInfo_MetaGraphDef proto.InternalMessageInfo + +func (m *MetaGraphDef) GetMetaInfoDef() *MetaGraphDef_MetaInfoDef { + if m != nil { + return m.MetaInfoDef + } + return nil +} + +func (m *MetaGraphDef) GetGraphDef() *graph_go_proto.GraphDef { + if m != nil { + return m.GraphDef + } + return nil +} + +func (m *MetaGraphDef) GetSaverDef() *SaverDef { + if m != nil { + return m.SaverDef + } + return nil +} + +func (m *MetaGraphDef) GetCollectionDef() map[string]*CollectionDef { + if m != nil { + return m.CollectionDef + } + return nil +} + +func (m *MetaGraphDef) GetSignatureDef() map[string]*SignatureDef { + if m != nil { + return m.SignatureDef + } + return nil +} + +func (m *MetaGraphDef) GetAssetFileDef() []*AssetFileDef { + if m != nil { + return m.AssetFileDef + } + return nil +} + +func (m *MetaGraphDef) GetObjectGraphDef() *SavedObjectGraph { + if m != nil { + return m.ObjectGraphDef + } + return nil +} + +// Meta information regarding the graph to be exported. To be used by users +// of this protocol buffer to encode information regarding their meta graph. +type MetaGraphDef_MetaInfoDef struct { + // User specified Version string. Can be the name of the model and revision, + // steps this model has been trained to, etc. + MetaGraphVersion string `protobuf:"bytes,1,opt,name=meta_graph_version,json=metaGraphVersion,proto3" json:"meta_graph_version,omitempty"` + // A copy of the OpDefs used by the producer of this graph_def. + // Descriptions and Ops not used in graph_def are stripped out. + StrippedOpList *op_def_go_proto.OpList `protobuf:"bytes,2,opt,name=stripped_op_list,json=strippedOpList,proto3" json:"stripped_op_list,omitempty"` + // A serialized protobuf. Can be the time this meta graph is created, or + // modified, or name of the model. + AnyInfo *any.Any `protobuf:"bytes,3,opt,name=any_info,json=anyInfo,proto3" json:"any_info,omitempty"` + // User supplied tag(s) on the meta_graph and included graph_def. + // + // MetaGraphDefs should be tagged with their capabilities or use-cases. + // Examples: "train", "serve", "gpu", "tpu", etc. + // These tags enable loaders to access the MetaGraph(s) appropriate for a + // specific use-case or runtime environment. + Tags []string `protobuf:"bytes,4,rep,name=tags,proto3" json:"tags,omitempty"` + // The __version__ string of the tensorflow build used to write this graph. + // This will be populated by the framework, which will overwrite any user + // supplied value. + TensorflowVersion string `protobuf:"bytes,5,opt,name=tensorflow_version,json=tensorflowVersion,proto3" json:"tensorflow_version,omitempty"` + // The __git_version__ string of the tensorflow build used to write this + // graph. This will be populated by the framework, which will overwrite any + // user supplied value. + TensorflowGitVersion string `protobuf:"bytes,6,opt,name=tensorflow_git_version,json=tensorflowGitVersion,proto3" json:"tensorflow_git_version,omitempty"` + // A flag to denote whether default-valued attrs have been stripped from + // the nodes in this graph_def. + StrippedDefaultAttrs bool `protobuf:"varint,7,opt,name=stripped_default_attrs,json=strippedDefaultAttrs,proto3" json:"stripped_default_attrs,omitempty"` + // FunctionDef name to aliases mapping. + FunctionAliases map[string]string `protobuf:"bytes,8,rep,name=function_aliases,json=functionAliases,proto3" json:"function_aliases,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MetaGraphDef_MetaInfoDef) Reset() { *m = MetaGraphDef_MetaInfoDef{} } +func (m *MetaGraphDef_MetaInfoDef) String() string { return proto.CompactTextString(m) } +func (*MetaGraphDef_MetaInfoDef) ProtoMessage() {} +func (*MetaGraphDef_MetaInfoDef) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{0, 0} +} + +func (m *MetaGraphDef_MetaInfoDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MetaGraphDef_MetaInfoDef.Unmarshal(m, b) +} +func (m *MetaGraphDef_MetaInfoDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MetaGraphDef_MetaInfoDef.Marshal(b, m, deterministic) +} +func (m *MetaGraphDef_MetaInfoDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_MetaGraphDef_MetaInfoDef.Merge(m, src) +} +func (m *MetaGraphDef_MetaInfoDef) XXX_Size() int { + return xxx_messageInfo_MetaGraphDef_MetaInfoDef.Size(m) +} +func (m *MetaGraphDef_MetaInfoDef) XXX_DiscardUnknown() { + xxx_messageInfo_MetaGraphDef_MetaInfoDef.DiscardUnknown(m) +} + +var xxx_messageInfo_MetaGraphDef_MetaInfoDef proto.InternalMessageInfo + +func (m *MetaGraphDef_MetaInfoDef) GetMetaGraphVersion() string { + if m != nil { + return m.MetaGraphVersion + } + return "" +} + +func (m *MetaGraphDef_MetaInfoDef) GetStrippedOpList() *op_def_go_proto.OpList { + if m != nil { + return m.StrippedOpList + } + return nil +} + +func (m *MetaGraphDef_MetaInfoDef) GetAnyInfo() *any.Any { + if m != nil { + return m.AnyInfo + } + return nil +} + +func (m *MetaGraphDef_MetaInfoDef) GetTags() []string { + if m != nil { + return m.Tags + } + return nil +} + +func (m *MetaGraphDef_MetaInfoDef) GetTensorflowVersion() string { + if m != nil { + return m.TensorflowVersion + } + return "" +} + +func (m *MetaGraphDef_MetaInfoDef) GetTensorflowGitVersion() string { + if m != nil { + return m.TensorflowGitVersion + } + return "" +} + +func (m *MetaGraphDef_MetaInfoDef) GetStrippedDefaultAttrs() bool { + if m != nil { + return m.StrippedDefaultAttrs + } + return false +} + +func (m *MetaGraphDef_MetaInfoDef) GetFunctionAliases() map[string]string { + if m != nil { + return m.FunctionAliases + } + return nil +} + +// CollectionDef should cover most collections. +// To add a user-defined collection, do one of the following: +// 1. For simple data types, such as string, int, float: +// tf.add_to_collection("your_collection_name", your_simple_value) +// strings will be stored as bytes_list. +// +// 2. For Protobuf types, there are three ways to add them: +// 1) tf.add_to_collection("your_collection_name", +// your_proto.SerializeToString()) +// +// collection_def { +// key: "user_defined_bytes_collection" +// value { +// bytes_list { +// value: "queue_name: \"test_queue\"\n" +// } +// } +// } +// +// or +// +// 2) tf.add_to_collection("your_collection_name", str(your_proto)) +// +// collection_def { +// key: "user_defined_string_collection" +// value { +// bytes_list { +// value: "\n\ntest_queue" +// } +// } +// } +// +// or +// +// 3) any_buf = any_pb2.Any() +// tf.add_to_collection("your_collection_name", +// any_buf.Pack(your_proto)) +// +// collection_def { +// key: "user_defined_any_collection" +// value { +// any_list { +// value { +// type_url: "type.googleapis.com/tensorflow.QueueRunnerDef" +// value: "\n\ntest_queue" +// } +// } +// } +// } +// +// 3. For Python objects, implement to_proto() and from_proto(), and register +// them in the following manner: +// ops.register_proto_function("your_collection_name", +// proto_type, +// to_proto=YourPythonObject.to_proto, +// from_proto=YourPythonObject.from_proto) +// These functions will be invoked to serialize and de-serialize the +// collection. For example, +// ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES, +// proto_type=variable_pb2.VariableDef, +// to_proto=Variable.to_proto, +// from_proto=Variable.from_proto) +type CollectionDef struct { + // Types that are valid to be assigned to Kind: + // *CollectionDef_NodeList_ + // *CollectionDef_BytesList_ + // *CollectionDef_Int64List_ + // *CollectionDef_FloatList_ + // *CollectionDef_AnyList_ + Kind isCollectionDef_Kind `protobuf_oneof:"kind"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CollectionDef) Reset() { *m = CollectionDef{} } +func (m *CollectionDef) String() string { return proto.CompactTextString(m) } +func (*CollectionDef) ProtoMessage() {} +func (*CollectionDef) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{1} +} + +func (m *CollectionDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CollectionDef.Unmarshal(m, b) +} +func (m *CollectionDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CollectionDef.Marshal(b, m, deterministic) +} +func (m *CollectionDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_CollectionDef.Merge(m, src) +} +func (m *CollectionDef) XXX_Size() int { + return xxx_messageInfo_CollectionDef.Size(m) +} +func (m *CollectionDef) XXX_DiscardUnknown() { + xxx_messageInfo_CollectionDef.DiscardUnknown(m) +} + +var xxx_messageInfo_CollectionDef proto.InternalMessageInfo + +type isCollectionDef_Kind interface { + isCollectionDef_Kind() +} + +type CollectionDef_NodeList_ struct { + NodeList *CollectionDef_NodeList `protobuf:"bytes,1,opt,name=node_list,json=nodeList,proto3,oneof"` +} + +type CollectionDef_BytesList_ struct { + BytesList *CollectionDef_BytesList `protobuf:"bytes,2,opt,name=bytes_list,json=bytesList,proto3,oneof"` +} + +type CollectionDef_Int64List_ struct { + Int64List *CollectionDef_Int64List `protobuf:"bytes,3,opt,name=int64_list,json=int64List,proto3,oneof"` +} + +type CollectionDef_FloatList_ struct { + FloatList *CollectionDef_FloatList `protobuf:"bytes,4,opt,name=float_list,json=floatList,proto3,oneof"` +} + +type CollectionDef_AnyList_ struct { + AnyList *CollectionDef_AnyList `protobuf:"bytes,5,opt,name=any_list,json=anyList,proto3,oneof"` +} + +func (*CollectionDef_NodeList_) isCollectionDef_Kind() {} + +func (*CollectionDef_BytesList_) isCollectionDef_Kind() {} + +func (*CollectionDef_Int64List_) isCollectionDef_Kind() {} + +func (*CollectionDef_FloatList_) isCollectionDef_Kind() {} + +func (*CollectionDef_AnyList_) isCollectionDef_Kind() {} + +func (m *CollectionDef) GetKind() isCollectionDef_Kind { + if m != nil { + return m.Kind + } + return nil +} + +func (m *CollectionDef) GetNodeList() *CollectionDef_NodeList { + if x, ok := m.GetKind().(*CollectionDef_NodeList_); ok { + return x.NodeList + } + return nil +} + +func (m *CollectionDef) GetBytesList() *CollectionDef_BytesList { + if x, ok := m.GetKind().(*CollectionDef_BytesList_); ok { + return x.BytesList + } + return nil +} + +func (m *CollectionDef) GetInt64List() *CollectionDef_Int64List { + if x, ok := m.GetKind().(*CollectionDef_Int64List_); ok { + return x.Int64List + } + return nil +} + +func (m *CollectionDef) GetFloatList() *CollectionDef_FloatList { + if x, ok := m.GetKind().(*CollectionDef_FloatList_); ok { + return x.FloatList + } + return nil +} + +func (m *CollectionDef) GetAnyList() *CollectionDef_AnyList { + if x, ok := m.GetKind().(*CollectionDef_AnyList_); ok { + return x.AnyList + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*CollectionDef) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*CollectionDef_NodeList_)(nil), + (*CollectionDef_BytesList_)(nil), + (*CollectionDef_Int64List_)(nil), + (*CollectionDef_FloatList_)(nil), + (*CollectionDef_AnyList_)(nil), + } +} + +// NodeList is used for collecting nodes in graph. For example +// collection_def { +// key: "summaries" +// value { +// node_list { +// value: "input_producer/ScalarSummary:0" +// value: "shuffle_batch/ScalarSummary:0" +// value: "ImageSummary:0" +// } +// } +type CollectionDef_NodeList struct { + Value []string `protobuf:"bytes,1,rep,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CollectionDef_NodeList) Reset() { *m = CollectionDef_NodeList{} } +func (m *CollectionDef_NodeList) String() string { return proto.CompactTextString(m) } +func (*CollectionDef_NodeList) ProtoMessage() {} +func (*CollectionDef_NodeList) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{1, 0} +} + +func (m *CollectionDef_NodeList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CollectionDef_NodeList.Unmarshal(m, b) +} +func (m *CollectionDef_NodeList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CollectionDef_NodeList.Marshal(b, m, deterministic) +} +func (m *CollectionDef_NodeList) XXX_Merge(src proto.Message) { + xxx_messageInfo_CollectionDef_NodeList.Merge(m, src) +} +func (m *CollectionDef_NodeList) XXX_Size() int { + return xxx_messageInfo_CollectionDef_NodeList.Size(m) +} +func (m *CollectionDef_NodeList) XXX_DiscardUnknown() { + xxx_messageInfo_CollectionDef_NodeList.DiscardUnknown(m) +} + +var xxx_messageInfo_CollectionDef_NodeList proto.InternalMessageInfo + +func (m *CollectionDef_NodeList) GetValue() []string { + if m != nil { + return m.Value + } + return nil +} + +// BytesList is used for collecting strings and serialized protobufs. For +// example: +// collection_def { +// key: "trainable_variables" +// value { +// bytes_list { +// value: "\n\017conv1/weights:0\022\024conv1/weights/Assign +// \032\024conv1/weights/read:0" +// value: "\n\016conv1/biases:0\022\023conv1/biases/Assign\032 +// \023conv1/biases/read:0" +// } +// } +// } +type CollectionDef_BytesList struct { + Value [][]byte `protobuf:"bytes,1,rep,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CollectionDef_BytesList) Reset() { *m = CollectionDef_BytesList{} } +func (m *CollectionDef_BytesList) String() string { return proto.CompactTextString(m) } +func (*CollectionDef_BytesList) ProtoMessage() {} +func (*CollectionDef_BytesList) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{1, 1} +} + +func (m *CollectionDef_BytesList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CollectionDef_BytesList.Unmarshal(m, b) +} +func (m *CollectionDef_BytesList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CollectionDef_BytesList.Marshal(b, m, deterministic) +} +func (m *CollectionDef_BytesList) XXX_Merge(src proto.Message) { + xxx_messageInfo_CollectionDef_BytesList.Merge(m, src) +} +func (m *CollectionDef_BytesList) XXX_Size() int { + return xxx_messageInfo_CollectionDef_BytesList.Size(m) +} +func (m *CollectionDef_BytesList) XXX_DiscardUnknown() { + xxx_messageInfo_CollectionDef_BytesList.DiscardUnknown(m) +} + +var xxx_messageInfo_CollectionDef_BytesList proto.InternalMessageInfo + +func (m *CollectionDef_BytesList) GetValue() [][]byte { + if m != nil { + return m.Value + } + return nil +} + +// Int64List is used for collecting int, int64 and long values. +type CollectionDef_Int64List struct { + Value []int64 `protobuf:"varint,1,rep,packed,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CollectionDef_Int64List) Reset() { *m = CollectionDef_Int64List{} } +func (m *CollectionDef_Int64List) String() string { return proto.CompactTextString(m) } +func (*CollectionDef_Int64List) ProtoMessage() {} +func (*CollectionDef_Int64List) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{1, 2} +} + +func (m *CollectionDef_Int64List) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CollectionDef_Int64List.Unmarshal(m, b) +} +func (m *CollectionDef_Int64List) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CollectionDef_Int64List.Marshal(b, m, deterministic) +} +func (m *CollectionDef_Int64List) XXX_Merge(src proto.Message) { + xxx_messageInfo_CollectionDef_Int64List.Merge(m, src) +} +func (m *CollectionDef_Int64List) XXX_Size() int { + return xxx_messageInfo_CollectionDef_Int64List.Size(m) +} +func (m *CollectionDef_Int64List) XXX_DiscardUnknown() { + xxx_messageInfo_CollectionDef_Int64List.DiscardUnknown(m) +} + +var xxx_messageInfo_CollectionDef_Int64List proto.InternalMessageInfo + +func (m *CollectionDef_Int64List) GetValue() []int64 { + if m != nil { + return m.Value + } + return nil +} + +// FloatList is used for collecting float values. +type CollectionDef_FloatList struct { + Value []float32 `protobuf:"fixed32,1,rep,packed,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CollectionDef_FloatList) Reset() { *m = CollectionDef_FloatList{} } +func (m *CollectionDef_FloatList) String() string { return proto.CompactTextString(m) } +func (*CollectionDef_FloatList) ProtoMessage() {} +func (*CollectionDef_FloatList) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{1, 3} +} + +func (m *CollectionDef_FloatList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CollectionDef_FloatList.Unmarshal(m, b) +} +func (m *CollectionDef_FloatList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CollectionDef_FloatList.Marshal(b, m, deterministic) +} +func (m *CollectionDef_FloatList) XXX_Merge(src proto.Message) { + xxx_messageInfo_CollectionDef_FloatList.Merge(m, src) +} +func (m *CollectionDef_FloatList) XXX_Size() int { + return xxx_messageInfo_CollectionDef_FloatList.Size(m) +} +func (m *CollectionDef_FloatList) XXX_DiscardUnknown() { + xxx_messageInfo_CollectionDef_FloatList.DiscardUnknown(m) +} + +var xxx_messageInfo_CollectionDef_FloatList proto.InternalMessageInfo + +func (m *CollectionDef_FloatList) GetValue() []float32 { + if m != nil { + return m.Value + } + return nil +} + +// AnyList is used for collecting Any protos. +type CollectionDef_AnyList struct { + Value []*any.Any `protobuf:"bytes,1,rep,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CollectionDef_AnyList) Reset() { *m = CollectionDef_AnyList{} } +func (m *CollectionDef_AnyList) String() string { return proto.CompactTextString(m) } +func (*CollectionDef_AnyList) ProtoMessage() {} +func (*CollectionDef_AnyList) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{1, 4} +} + +func (m *CollectionDef_AnyList) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CollectionDef_AnyList.Unmarshal(m, b) +} +func (m *CollectionDef_AnyList) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CollectionDef_AnyList.Marshal(b, m, deterministic) +} +func (m *CollectionDef_AnyList) XXX_Merge(src proto.Message) { + xxx_messageInfo_CollectionDef_AnyList.Merge(m, src) +} +func (m *CollectionDef_AnyList) XXX_Size() int { + return xxx_messageInfo_CollectionDef_AnyList.Size(m) +} +func (m *CollectionDef_AnyList) XXX_DiscardUnknown() { + xxx_messageInfo_CollectionDef_AnyList.DiscardUnknown(m) +} + +var xxx_messageInfo_CollectionDef_AnyList proto.InternalMessageInfo + +func (m *CollectionDef_AnyList) GetValue() []*any.Any { + if m != nil { + return m.Value + } + return nil +} + +// Information about a Tensor necessary for feeding or retrieval. +type TensorInfo struct { + // Types that are valid to be assigned to Encoding: + // *TensorInfo_Name + // *TensorInfo_CooSparse_ + // *TensorInfo_CompositeTensor_ + Encoding isTensorInfo_Encoding `protobuf_oneof:"encoding"` + Dtype types_go_proto.DataType `protobuf:"varint,2,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + // The static shape should be recorded here, to the extent that it can + // be known in advance. In the case of a SparseTensor, this field describes + // the logical shape of the represented tensor (aka dense_shape). + TensorShape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,3,opt,name=tensor_shape,json=tensorShape,proto3" json:"tensor_shape,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorInfo) Reset() { *m = TensorInfo{} } +func (m *TensorInfo) String() string { return proto.CompactTextString(m) } +func (*TensorInfo) ProtoMessage() {} +func (*TensorInfo) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{2} +} + +func (m *TensorInfo) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorInfo.Unmarshal(m, b) +} +func (m *TensorInfo) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorInfo.Marshal(b, m, deterministic) +} +func (m *TensorInfo) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorInfo.Merge(m, src) +} +func (m *TensorInfo) XXX_Size() int { + return xxx_messageInfo_TensorInfo.Size(m) +} +func (m *TensorInfo) XXX_DiscardUnknown() { + xxx_messageInfo_TensorInfo.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorInfo proto.InternalMessageInfo + +type isTensorInfo_Encoding interface { + isTensorInfo_Encoding() +} + +type TensorInfo_Name struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3,oneof"` +} + +type TensorInfo_CooSparse_ struct { + CooSparse *TensorInfo_CooSparse `protobuf:"bytes,4,opt,name=coo_sparse,json=cooSparse,proto3,oneof"` +} + +type TensorInfo_CompositeTensor_ struct { + CompositeTensor *TensorInfo_CompositeTensor `protobuf:"bytes,5,opt,name=composite_tensor,json=compositeTensor,proto3,oneof"` +} + +func (*TensorInfo_Name) isTensorInfo_Encoding() {} + +func (*TensorInfo_CooSparse_) isTensorInfo_Encoding() {} + +func (*TensorInfo_CompositeTensor_) isTensorInfo_Encoding() {} + +func (m *TensorInfo) GetEncoding() isTensorInfo_Encoding { + if m != nil { + return m.Encoding + } + return nil +} + +func (m *TensorInfo) GetName() string { + if x, ok := m.GetEncoding().(*TensorInfo_Name); ok { + return x.Name + } + return "" +} + +func (m *TensorInfo) GetCooSparse() *TensorInfo_CooSparse { + if x, ok := m.GetEncoding().(*TensorInfo_CooSparse_); ok { + return x.CooSparse + } + return nil +} + +func (m *TensorInfo) GetCompositeTensor() *TensorInfo_CompositeTensor { + if x, ok := m.GetEncoding().(*TensorInfo_CompositeTensor_); ok { + return x.CompositeTensor + } + return nil +} + +func (m *TensorInfo) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *TensorInfo) GetTensorShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.TensorShape + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*TensorInfo) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*TensorInfo_Name)(nil), + (*TensorInfo_CooSparse_)(nil), + (*TensorInfo_CompositeTensor_)(nil), + } +} + +// For sparse tensors, The COO encoding stores a triple of values, indices, +// and shape. +type TensorInfo_CooSparse struct { + // The shape of the values Tensor is [?]. Its dtype must be the dtype of + // the SparseTensor as a whole, given in the enclosing TensorInfo. + ValuesTensorName string `protobuf:"bytes,1,opt,name=values_tensor_name,json=valuesTensorName,proto3" json:"values_tensor_name,omitempty"` + // The indices Tensor must have dtype int64 and shape [?, ?]. + IndicesTensorName string `protobuf:"bytes,2,opt,name=indices_tensor_name,json=indicesTensorName,proto3" json:"indices_tensor_name,omitempty"` + // The dynamic logical shape represented by the SparseTensor is recorded in + // the Tensor referenced here. It must have dtype int64 and shape [?]. + DenseShapeTensorName string `protobuf:"bytes,3,opt,name=dense_shape_tensor_name,json=denseShapeTensorName,proto3" json:"dense_shape_tensor_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorInfo_CooSparse) Reset() { *m = TensorInfo_CooSparse{} } +func (m *TensorInfo_CooSparse) String() string { return proto.CompactTextString(m) } +func (*TensorInfo_CooSparse) ProtoMessage() {} +func (*TensorInfo_CooSparse) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{2, 0} +} + +func (m *TensorInfo_CooSparse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorInfo_CooSparse.Unmarshal(m, b) +} +func (m *TensorInfo_CooSparse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorInfo_CooSparse.Marshal(b, m, deterministic) +} +func (m *TensorInfo_CooSparse) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorInfo_CooSparse.Merge(m, src) +} +func (m *TensorInfo_CooSparse) XXX_Size() int { + return xxx_messageInfo_TensorInfo_CooSparse.Size(m) +} +func (m *TensorInfo_CooSparse) XXX_DiscardUnknown() { + xxx_messageInfo_TensorInfo_CooSparse.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorInfo_CooSparse proto.InternalMessageInfo + +func (m *TensorInfo_CooSparse) GetValuesTensorName() string { + if m != nil { + return m.ValuesTensorName + } + return "" +} + +func (m *TensorInfo_CooSparse) GetIndicesTensorName() string { + if m != nil { + return m.IndicesTensorName + } + return "" +} + +func (m *TensorInfo_CooSparse) GetDenseShapeTensorName() string { + if m != nil { + return m.DenseShapeTensorName + } + return "" +} + +// Generic encoding for composite tensors. +type TensorInfo_CompositeTensor struct { + // The serialized TypeSpec for the composite tensor. + TypeSpec *TypeSpecProto `protobuf:"bytes,1,opt,name=type_spec,json=typeSpec,proto3" json:"type_spec,omitempty"` + // A TensorInfo for each flattened component tensor. + Components []*TensorInfo `protobuf:"bytes,2,rep,name=components,proto3" json:"components,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorInfo_CompositeTensor) Reset() { *m = TensorInfo_CompositeTensor{} } +func (m *TensorInfo_CompositeTensor) String() string { return proto.CompactTextString(m) } +func (*TensorInfo_CompositeTensor) ProtoMessage() {} +func (*TensorInfo_CompositeTensor) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{2, 1} +} + +func (m *TensorInfo_CompositeTensor) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorInfo_CompositeTensor.Unmarshal(m, b) +} +func (m *TensorInfo_CompositeTensor) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorInfo_CompositeTensor.Marshal(b, m, deterministic) +} +func (m *TensorInfo_CompositeTensor) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorInfo_CompositeTensor.Merge(m, src) +} +func (m *TensorInfo_CompositeTensor) XXX_Size() int { + return xxx_messageInfo_TensorInfo_CompositeTensor.Size(m) +} +func (m *TensorInfo_CompositeTensor) XXX_DiscardUnknown() { + xxx_messageInfo_TensorInfo_CompositeTensor.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorInfo_CompositeTensor proto.InternalMessageInfo + +func (m *TensorInfo_CompositeTensor) GetTypeSpec() *TypeSpecProto { + if m != nil { + return m.TypeSpec + } + return nil +} + +func (m *TensorInfo_CompositeTensor) GetComponents() []*TensorInfo { + if m != nil { + return m.Components + } + return nil +} + +// SignatureDef defines the signature of a computation supported by a TensorFlow +// graph. +// +// For example, a model with two loss computations, sharing a single input, +// might have the following signature_def map. +// +// Note that across the two SignatureDefs "loss_A" and "loss_B", the input key, +// output key, and method_name are identical, and will be used by system(s) that +// implement or rely upon this particular loss method. The output tensor names +// differ, demonstrating how different outputs can exist for the same method. +// +// signature_def { +// key: "loss_A" +// value { +// inputs { +// key: "input" +// value { +// name: "input:0" +// dtype: DT_STRING +// tensor_shape: ... +// } +// } +// outputs { +// key: "loss_output" +// value { +// name: "loss_output_A:0" +// dtype: DT_FLOAT +// tensor_shape: ... +// } +// } +// } +// ... +// method_name: "some/package/compute_loss" +// } +// signature_def { +// key: "loss_B" +// value { +// inputs { +// key: "input" +// value { +// name: "input:0" +// dtype: DT_STRING +// tensor_shape: ... +// } +// } +// outputs { +// key: "loss_output" +// value { +// name: "loss_output_B:0" +// dtype: DT_FLOAT +// tensor_shape: ... +// } +// } +// } +// ... +// method_name: "some/package/compute_loss" +// } +type SignatureDef struct { + // Named input parameters. + Inputs map[string]*TensorInfo `protobuf:"bytes,1,rep,name=inputs,proto3" json:"inputs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // Named output parameters. + Outputs map[string]*TensorInfo `protobuf:"bytes,2,rep,name=outputs,proto3" json:"outputs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + // Extensible method_name information enabling third-party users to mark a + // SignatureDef as supporting a particular method. This enables producers and + // consumers of SignatureDefs, e.g. a model definition library and a serving + // library to have a clear hand-off regarding the semantics of a computation. + // + // Note that multiple SignatureDefs in a single MetaGraphDef may have the same + // method_name. This is commonly used to support multi-headed computation, + // where a single graph computation may return multiple results. + MethodName string `protobuf:"bytes,3,opt,name=method_name,json=methodName,proto3" json:"method_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SignatureDef) Reset() { *m = SignatureDef{} } +func (m *SignatureDef) String() string { return proto.CompactTextString(m) } +func (*SignatureDef) ProtoMessage() {} +func (*SignatureDef) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{3} +} + +func (m *SignatureDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SignatureDef.Unmarshal(m, b) +} +func (m *SignatureDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SignatureDef.Marshal(b, m, deterministic) +} +func (m *SignatureDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_SignatureDef.Merge(m, src) +} +func (m *SignatureDef) XXX_Size() int { + return xxx_messageInfo_SignatureDef.Size(m) +} +func (m *SignatureDef) XXX_DiscardUnknown() { + xxx_messageInfo_SignatureDef.DiscardUnknown(m) +} + +var xxx_messageInfo_SignatureDef proto.InternalMessageInfo + +func (m *SignatureDef) GetInputs() map[string]*TensorInfo { + if m != nil { + return m.Inputs + } + return nil +} + +func (m *SignatureDef) GetOutputs() map[string]*TensorInfo { + if m != nil { + return m.Outputs + } + return nil +} + +func (m *SignatureDef) GetMethodName() string { + if m != nil { + return m.MethodName + } + return "" +} + +// An asset file def for a single file or a set of sharded files with the same +// name. +type AssetFileDef struct { + // The tensor to bind the asset filename to. + TensorInfo *TensorInfo `protobuf:"bytes,1,opt,name=tensor_info,json=tensorInfo,proto3" json:"tensor_info,omitempty"` + // The filename within an assets directory. Note: does not include the path + // prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename + // would be "vocab.txt". + Filename string `protobuf:"bytes,2,opt,name=filename,proto3" json:"filename,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AssetFileDef) Reset() { *m = AssetFileDef{} } +func (m *AssetFileDef) String() string { return proto.CompactTextString(m) } +func (*AssetFileDef) ProtoMessage() {} +func (*AssetFileDef) Descriptor() ([]byte, []int) { + return fileDescriptor_e94adf32e895c059, []int{4} +} + +func (m *AssetFileDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AssetFileDef.Unmarshal(m, b) +} +func (m *AssetFileDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AssetFileDef.Marshal(b, m, deterministic) +} +func (m *AssetFileDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_AssetFileDef.Merge(m, src) +} +func (m *AssetFileDef) XXX_Size() int { + return xxx_messageInfo_AssetFileDef.Size(m) +} +func (m *AssetFileDef) XXX_DiscardUnknown() { + xxx_messageInfo_AssetFileDef.DiscardUnknown(m) +} + +var xxx_messageInfo_AssetFileDef proto.InternalMessageInfo + +func (m *AssetFileDef) GetTensorInfo() *TensorInfo { + if m != nil { + return m.TensorInfo + } + return nil +} + +func (m *AssetFileDef) GetFilename() string { + if m != nil { + return m.Filename + } + return "" +} + +func init() { + proto.RegisterType((*MetaGraphDef)(nil), "tensorflow.MetaGraphDef") + proto.RegisterMapType((map[string]*CollectionDef)(nil), "tensorflow.MetaGraphDef.CollectionDefEntry") + proto.RegisterMapType((map[string]*SignatureDef)(nil), "tensorflow.MetaGraphDef.SignatureDefEntry") + proto.RegisterType((*MetaGraphDef_MetaInfoDef)(nil), "tensorflow.MetaGraphDef.MetaInfoDef") + proto.RegisterMapType((map[string]string)(nil), "tensorflow.MetaGraphDef.MetaInfoDef.FunctionAliasesEntry") + proto.RegisterType((*CollectionDef)(nil), "tensorflow.CollectionDef") + proto.RegisterType((*CollectionDef_NodeList)(nil), "tensorflow.CollectionDef.NodeList") + proto.RegisterType((*CollectionDef_BytesList)(nil), "tensorflow.CollectionDef.BytesList") + proto.RegisterType((*CollectionDef_Int64List)(nil), "tensorflow.CollectionDef.Int64List") + proto.RegisterType((*CollectionDef_FloatList)(nil), "tensorflow.CollectionDef.FloatList") + proto.RegisterType((*CollectionDef_AnyList)(nil), "tensorflow.CollectionDef.AnyList") + proto.RegisterType((*TensorInfo)(nil), "tensorflow.TensorInfo") + proto.RegisterType((*TensorInfo_CooSparse)(nil), "tensorflow.TensorInfo.CooSparse") + proto.RegisterType((*TensorInfo_CompositeTensor)(nil), "tensorflow.TensorInfo.CompositeTensor") + proto.RegisterType((*SignatureDef)(nil), "tensorflow.SignatureDef") + proto.RegisterMapType((map[string]*TensorInfo)(nil), "tensorflow.SignatureDef.InputsEntry") + proto.RegisterMapType((map[string]*TensorInfo)(nil), "tensorflow.SignatureDef.OutputsEntry") + proto.RegisterType((*AssetFileDef)(nil), "tensorflow.AssetFileDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/meta_graph.proto", fileDescriptor_e94adf32e895c059) +} + +var fileDescriptor_e94adf32e895c059 = []byte{ + // 1214 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xac, 0x57, 0xdd, 0x8e, 0xdb, 0x44, + 0x14, 0xae, 0x93, 0xec, 0x6e, 0x7c, 0x92, 0xdd, 0x4d, 0x87, 0xa8, 0xa4, 0x56, 0x25, 0xb6, 0xa1, + 0xad, 0x4a, 0x29, 0x8e, 0x5a, 0xda, 0xf2, 0xa3, 0xaa, 0x55, 0xd2, 0xb0, 0xdd, 0x4a, 0xd0, 0x2d, + 0x4e, 0x41, 0x02, 0x2e, 0x2c, 0xc7, 0x1e, 0x7b, 0x4d, 0x93, 0x19, 0xcb, 0x33, 0xd9, 0x2a, 0x97, + 0x88, 0x0b, 0x5e, 0x83, 0x5b, 0x5e, 0x80, 0x37, 0xe1, 0x86, 0x27, 0xe1, 0x12, 0xcd, 0x8c, 0x7f, + 0xc6, 0xbb, 0x71, 0x97, 0x0b, 0xee, 0xe6, 0xcc, 0xf9, 0xce, 0x37, 0xe7, 0x6f, 0x8e, 0xc7, 0xf0, + 0x11, 0xc7, 0x84, 0xd1, 0x34, 0x5c, 0xd0, 0xb7, 0x23, 0x9f, 0xa6, 0x78, 0x94, 0xa4, 0x94, 0xd3, + 0xf9, 0x2a, 0x1c, 0x2d, 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b/tensorflow/go/core/protobuf/for_core_protos_go_proto/named_tensor.pb.go @@ -0,0 +1,102 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/named_tensor.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// A pair of tensor name and tensor values. +type NamedTensorProto struct { + // Name of the tensor. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // The client can populate a TensorProto using a tensorflow::Tensor`, or + // directly using the protobuf field accessors. + // + // The client specifies whether the returned tensor values should be + // filled tensor fields (float_val, int_val, etc.) or encoded in a + // compact form in tensor.tensor_content. + Tensor *tensor_go_proto.TensorProto `protobuf:"bytes,2,opt,name=tensor,proto3" json:"tensor,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NamedTensorProto) Reset() { *m = NamedTensorProto{} } +func (m *NamedTensorProto) String() string { return proto.CompactTextString(m) } +func (*NamedTensorProto) ProtoMessage() {} +func (*NamedTensorProto) Descriptor() ([]byte, []int) { + return fileDescriptor_5c12ce8841c69dcd, []int{0} +} + +func (m *NamedTensorProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NamedTensorProto.Unmarshal(m, b) +} +func (m *NamedTensorProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NamedTensorProto.Marshal(b, m, deterministic) +} +func (m *NamedTensorProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_NamedTensorProto.Merge(m, src) +} +func (m *NamedTensorProto) XXX_Size() int { + return xxx_messageInfo_NamedTensorProto.Size(m) +} +func (m *NamedTensorProto) XXX_DiscardUnknown() { + xxx_messageInfo_NamedTensorProto.DiscardUnknown(m) +} + +var xxx_messageInfo_NamedTensorProto proto.InternalMessageInfo + +func (m *NamedTensorProto) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *NamedTensorProto) GetTensor() *tensor_go_proto.TensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +func init() { + proto.RegisterType((*NamedTensorProto)(nil), "tensorflow.NamedTensorProto") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/named_tensor.proto", fileDescriptor_5c12ce8841c69dcd) +} + +var fileDescriptor_5c12ce8841c69dcd = []byte{ + // 196 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xe2, 0xd2, 0x2e, 0x49, 0xcd, 0x2b, + 0xce, 0x2f, 0x4a, 0xcb, 0xc9, 0x2f, 0xd7, 0x4f, 0xce, 0x2f, 0x4a, 0xd5, 0x2f, 0x28, 0xca, 0x2f, + 0xc9, 0x4f, 0x2a, 0x4d, 0xd3, 0xcf, 0x4b, 0xcc, 0x4d, 0x4d, 0x89, 0x87, 0x48, 0xeb, 0x81, 0x45, + 0x85, 0xb8, 0x10, 0x8a, 0xa5, 0xd4, 0xd0, 0x35, 0xa6, 0x15, 0x25, 0xe6, 0xa6, 0x96, 0xe7, 0x17, + 0x65, 0xeb, 0x23, 0xeb, 0x51, 0x0a, 0xe7, 0x12, 0xf0, 0x03, 0x99, 0x14, 0x02, 0x16, 0x0c, 0x00, + 0x9b, 0x23, 0xc4, 0xc5, 0x02, 0x32, 0x5d, 0x82, 0x51, 0x81, 0x51, 0x83, 0x33, 0x08, 0xcc, 0x16, + 0xd2, 0xe7, 0x62, 0x83, 0xe8, 0x93, 0x60, 0x52, 0x60, 0xd4, 0xe0, 0x36, 0x12, 0xd7, 0x43, 0x58, + 0xa0, 0x87, 0xa4, 0x39, 0x08, 0xaa, 0xcc, 0xa9, 0x93, 0x91, 0x4b, 0x22, 0xbf, 0x28, 0x1d, 0x59, + 0x19, 0xdc, 0x09, 0x4e, 0x82, 0xe8, 0x76, 0x16, 0x07, 0x30, 0x46, 0x85, 0xa6, 0x67, 0x96, 0x64, + 0x94, 0x26, 0xe9, 0x25, 0xe7, 0xe7, 0xea, 0x23, 0xb9, 0x1e, 0x3b, 0x33, 0x3d, 0x1f, 0x2d, 0x3c, + 0xd2, 0xf2, 0x8b, 0xe2, 0x41, 0x22, 0xf1, 0x60, 0x91, 0xe2, 0xf8, 0xf4, 0x7c, 0x08, 0xeb, 0x07, + 0x23, 0x63, 0x12, 0x1b, 0x98, 0x65, 0x0c, 0x08, 0x00, 0x00, 0xff, 0xff, 0xeb, 0x7a, 0x55, 0x26, + 0x4e, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/queue_runner.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/queue_runner.pb.go new file mode 100644 index 0000000..cb58a0c --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/queue_runner.pb.go @@ -0,0 +1,130 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/queue_runner.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Protocol buffer representing a QueueRunner. +type QueueRunnerDef struct { + // Queue name. + QueueName string `protobuf:"bytes,1,opt,name=queue_name,json=queueName,proto3" json:"queue_name,omitempty"` + // A list of enqueue operations. + EnqueueOpName []string `protobuf:"bytes,2,rep,name=enqueue_op_name,json=enqueueOpName,proto3" json:"enqueue_op_name,omitempty"` + // The operation to run to close the queue. + CloseOpName string `protobuf:"bytes,3,opt,name=close_op_name,json=closeOpName,proto3" json:"close_op_name,omitempty"` + // The operation to run to cancel the queue. + CancelOpName string `protobuf:"bytes,4,opt,name=cancel_op_name,json=cancelOpName,proto3" json:"cancel_op_name,omitempty"` + // A list of exception types considered to signal a safely closed queue + // if raised during enqueue operations. + QueueClosedExceptionTypes []Code `protobuf:"varint,5,rep,packed,name=queue_closed_exception_types,json=queueClosedExceptionTypes,proto3,enum=tensorflow.error.Code" json:"queue_closed_exception_types,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *QueueRunnerDef) Reset() { *m = QueueRunnerDef{} } +func (m *QueueRunnerDef) String() string { return proto.CompactTextString(m) } +func (*QueueRunnerDef) ProtoMessage() {} +func (*QueueRunnerDef) Descriptor() ([]byte, []int) { + return fileDescriptor_7af35200d68d14ae, []int{0} +} + +func (m *QueueRunnerDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_QueueRunnerDef.Unmarshal(m, b) +} +func (m *QueueRunnerDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_QueueRunnerDef.Marshal(b, m, deterministic) +} +func (m *QueueRunnerDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_QueueRunnerDef.Merge(m, src) +} +func (m *QueueRunnerDef) XXX_Size() int { + return xxx_messageInfo_QueueRunnerDef.Size(m) +} +func (m *QueueRunnerDef) XXX_DiscardUnknown() { + xxx_messageInfo_QueueRunnerDef.DiscardUnknown(m) +} + +var xxx_messageInfo_QueueRunnerDef proto.InternalMessageInfo + +func (m *QueueRunnerDef) GetQueueName() string { + if m != nil { + return m.QueueName + } + return "" +} + +func (m *QueueRunnerDef) GetEnqueueOpName() []string { + if m != nil { + return m.EnqueueOpName + } + return nil +} + +func (m *QueueRunnerDef) GetCloseOpName() string { + if m != nil { + return m.CloseOpName + } + return "" +} + +func (m *QueueRunnerDef) GetCancelOpName() string { + if m != nil { + return m.CancelOpName + } + return "" +} + +func (m *QueueRunnerDef) GetQueueClosedExceptionTypes() []Code { + if m != nil { + return m.QueueClosedExceptionTypes + } + return nil +} + +func init() { + proto.RegisterType((*QueueRunnerDef)(nil), "tensorflow.QueueRunnerDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/queue_runner.proto", fileDescriptor_7af35200d68d14ae) +} + +var fileDescriptor_7af35200d68d14ae = []byte{ + // 299 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x74, 0x90, 0xc1, 0x4a, 0xc3, 0x40, + 0x10, 0x86, 0x59, 0xab, 0x42, 0x57, 0x5b, 0xb1, 0x07, 0xa9, 0xa2, 0x50, 0x8a, 0x48, 0x51, 0x48, + 0x40, 0xdf, 0xa0, 0xd5, 0xab, 0xd6, 0xa0, 0x08, 0x5e, 0x96, 0x74, 0x33, 0x89, 0xc5, 0x64, 0x27, + 0xce, 0x26, 0x54, 0x1f, 0xc1, 0x37, 0x16, 0x4f, 0x92, 0xd9, 0x68, 0x82, 0xe8, 0x6d, 0xf8, 0xf9, + 0xbe, 0x19, 0xe6, 0x97, 0x67, 0x05, 0x18, 0x8b, 0x14, 0xa7, 0xb8, 0xf2, 0x35, 0x12, 0xf8, 0x39, + 0x61, 0x81, 0x8b, 0x32, 0xf6, 0x5f, 0x4a, 0x28, 0x41, 0x51, 0x69, 0x0c, 0x90, 0xc7, 0xe9, 0x40, + 0x36, 0xf0, 0xc1, 0xe9, 0xbf, 0x22, 0x10, 0x21, 0x29, 0x8d, 0x11, 0x58, 0xe7, 0x8d, 0x3f, 0x85, + 0xec, 0xdf, 0x56, 0xeb, 0x02, 0xde, 0x76, 0x09, 0xf1, 0xe0, 0x48, 0x4a, 0x77, 0xc0, 0x84, 0x19, + 0x0c, 0xc5, 0x48, 0x4c, 0xba, 0x41, 0x97, 0x93, 0xeb, 0x30, 0x83, 0xc1, 0x89, 0xdc, 0x01, 0xe3, + 0x00, 0xcc, 0x1d, 0xb3, 0x36, 0xea, 0x4c, 0xba, 0x41, 0xaf, 0x8e, 0x6f, 0x72, 0xe6, 0xc6, 0xb2, + 0xa7, 0x53, 0xb4, 0x0d, 0xd5, 0xe1, 0x4d, 0x5b, 0x1c, 0xd6, 0xcc, 0xb1, 0xec, 0xeb, 0xd0, 0x68, + 0x48, 0x7f, 0xa0, 0x75, 0x86, 0xb6, 0x5d, 0x5a, 0x53, 0x0f, 0xf2, 0xd0, 0xdd, 0x63, 0x35, 0x52, + 0xf0, 0xaa, 0x21, 0x2f, 0x96, 0x68, 0x54, 0xf1, 0x96, 0x83, 0x1d, 0x6e, 0x8c, 0x3a, 0x93, 0xfe, + 0xf9, 0x9e, 0xd7, 0xbc, 0xed, 0xf1, 0xa3, 0xde, 0x0c, 0x23, 0x08, 0xf6, 0xd9, 0x9d, 0xb1, 0x7a, + 0xf5, 0x6d, 0xde, 0x55, 0xe2, 0xf4, 0x5d, 0xc8, 0x21, 0x52, 0xd2, 0x16, 0x63, 0x0a, 0x33, 0x58, + 0x21, 0x3d, 0x4f, 0x77, 0x5b, 0xb5, 0xcc, 0xab, 0xae, 0xec, 0x5c, 0x3c, 0xde, 0x27, 0xcb, 0xe2, + 0xa9, 0x5c, 0x78, 0x1a, 0x33, 0xbf, 0xd5, 0xf2, 0xdf, 0x63, 0x82, 0xbf, 0xea, 0x8f, 0xb9, 0x7c, + 0x02, 0xc5, 0x89, 0x55, 0x09, 0xba, 0xe9, 0x43, 0x88, 0xc5, 0x26, 0x4f, 0x17, 0x5f, 0x01, 0x00, + 0x00, 0xff, 0xff, 0x8d, 0x6d, 0xb6, 0x77, 0xf6, 0x01, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/remote_tensor_handle.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/remote_tensor_handle.pb.go new file mode 100644 index 0000000..8111104 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/remote_tensor_handle.pb.go @@ -0,0 +1,194 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/remote_tensor_handle.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type ResourceDtypeAndShape struct { + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ResourceDtypeAndShape) Reset() { *m = ResourceDtypeAndShape{} } +func (m *ResourceDtypeAndShape) String() string { return proto.CompactTextString(m) } +func (*ResourceDtypeAndShape) ProtoMessage() {} +func (*ResourceDtypeAndShape) Descriptor() ([]byte, []int) { + return fileDescriptor_c7497b828cba9b15, []int{0} +} + +func (m *ResourceDtypeAndShape) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ResourceDtypeAndShape.Unmarshal(m, b) +} +func (m *ResourceDtypeAndShape) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ResourceDtypeAndShape.Marshal(b, m, deterministic) +} +func (m *ResourceDtypeAndShape) XXX_Merge(src proto.Message) { + xxx_messageInfo_ResourceDtypeAndShape.Merge(m, src) +} +func (m *ResourceDtypeAndShape) XXX_Size() int { + return xxx_messageInfo_ResourceDtypeAndShape.Size(m) +} +func (m *ResourceDtypeAndShape) XXX_DiscardUnknown() { + xxx_messageInfo_ResourceDtypeAndShape.DiscardUnknown(m) +} + +var xxx_messageInfo_ResourceDtypeAndShape proto.InternalMessageInfo + +func (m *ResourceDtypeAndShape) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *ResourceDtypeAndShape) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +type RemoteTensorHandle struct { + // The ID of the operation that produced this tensor. + OpId int64 `protobuf:"varint,1,opt,name=op_id,json=opId,proto3" json:"op_id,omitempty"` + // The index into the outputs of the operation that produced this tensor. + OutputNum int32 `protobuf:"varint,2,opt,name=output_num,json=outputNum,proto3" json:"output_num,omitempty"` + // Device where the tensor is located. Cannot be empty. + // For multi-device functions, it's the default device passed to placer. + Device string `protobuf:"bytes,3,opt,name=device,proto3" json:"device,omitempty"` + // Device of the operation producing this tensor. Can be empty if the + // operation producing this tensor is a multi-device function. + OpDevice string `protobuf:"bytes,4,opt,name=op_device,json=opDevice,proto3" json:"op_device,omitempty"` + // Tensor type. + Dtype types_go_proto.DataType `protobuf:"varint,5,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + // Optional data types and shapes of a remote resource variable. + ResourceDtypesAndShapes []*ResourceDtypeAndShape `protobuf:"bytes,6,rep,name=resource_dtypes_and_shapes,json=resourceDtypesAndShapes,proto3" json:"resource_dtypes_and_shapes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RemoteTensorHandle) Reset() { *m = RemoteTensorHandle{} } +func (m *RemoteTensorHandle) String() string { return proto.CompactTextString(m) } +func (*RemoteTensorHandle) ProtoMessage() {} +func (*RemoteTensorHandle) Descriptor() ([]byte, []int) { + return fileDescriptor_c7497b828cba9b15, []int{1} +} + +func (m *RemoteTensorHandle) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RemoteTensorHandle.Unmarshal(m, b) +} +func (m *RemoteTensorHandle) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RemoteTensorHandle.Marshal(b, m, deterministic) +} +func (m *RemoteTensorHandle) XXX_Merge(src proto.Message) { + xxx_messageInfo_RemoteTensorHandle.Merge(m, src) +} +func (m *RemoteTensorHandle) XXX_Size() int { + return xxx_messageInfo_RemoteTensorHandle.Size(m) +} +func (m *RemoteTensorHandle) XXX_DiscardUnknown() { + xxx_messageInfo_RemoteTensorHandle.DiscardUnknown(m) +} + +var xxx_messageInfo_RemoteTensorHandle proto.InternalMessageInfo + +func (m *RemoteTensorHandle) GetOpId() int64 { + if m != nil { + return m.OpId + } + return 0 +} + +func (m *RemoteTensorHandle) GetOutputNum() int32 { + if m != nil { + return m.OutputNum + } + return 0 +} + +func (m *RemoteTensorHandle) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *RemoteTensorHandle) GetOpDevice() string { + if m != nil { + return m.OpDevice + } + return "" +} + +func (m *RemoteTensorHandle) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *RemoteTensorHandle) GetResourceDtypesAndShapes() []*ResourceDtypeAndShape { + if m != nil { + return m.ResourceDtypesAndShapes + } + return nil +} + +func init() { + proto.RegisterType((*ResourceDtypeAndShape)(nil), "tensorflow.eager.ResourceDtypeAndShape") + proto.RegisterType((*RemoteTensorHandle)(nil), "tensorflow.eager.RemoteTensorHandle") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/remote_tensor_handle.proto", fileDescriptor_c7497b828cba9b15) +} + +var fileDescriptor_c7497b828cba9b15 = []byte{ + // 374 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x52, 0x4d, 0x6b, 0xe3, 0x30, + 0x10, 0x45, 0x49, 0x1c, 0x36, 0x0a, 0x2c, 0x8b, 0xf6, 0xcb, 0x64, 0xb7, 0x60, 0x02, 0xa5, 0xa6, + 0x14, 0x1b, 0x9c, 0x5f, 0xd0, 0x90, 0x43, 0x7b, 0x29, 0x41, 0x4d, 0x2f, 0xbd, 0x08, 0xc7, 0x92, + 0x9d, 0xd0, 0xd8, 0x23, 0x24, 0xbb, 0x21, 0xbf, 0xa2, 0xfd, 0xb9, 0x3d, 0x16, 0x4b, 0x6e, 0x6a, + 0xd2, 0xd0, 0xde, 0xc6, 0xef, 0xbd, 0x79, 0xe3, 0x79, 0x23, 0x3c, 0x29, 0x45, 0xa1, 0x41, 0xa5, + 0x1b, 0xd8, 0x86, 0x09, 0x28, 0x11, 0x4a, 0x05, 0x25, 0x2c, 0xab, 0x34, 0x54, 0x22, 0x87, 0x52, + 0x30, 0xcb, 0xb3, 0x55, 0x5c, 0xf0, 0x8d, 0x08, 0x0c, 0x4b, 0x7e, 0xbc, 0x37, 0x05, 0x22, 0xce, + 0x84, 0x1a, 0x5d, 0x1c, 0xda, 0xa4, 0x2a, 0xce, 0xc5, 0x16, 0xd4, 0x43, 0xd8, 0x18, 0xe8, 0x55, + 0x2c, 0x9b, 0xfe, 0xd1, 0xe9, 0x27, 0xea, 0x9d, 0x14, 0xda, 0xca, 0xc6, 0x5b, 0xfc, 0x9b, 0x0a, + 0x0d, 0x95, 0x4a, 0xc4, 0xac, 0xc6, 0x2f, 0x0b, 0x7e, 0x5b, 0xbb, 0x90, 0x73, 0xec, 0xf0, 0x1a, + 0x70, 0x91, 0x87, 0xfc, 0xef, 0xd1, 0xaf, 0xa0, 0xf5, 0x3f, 0xb3, 0xb8, 0x8c, 0x17, 0x3b, 0x29, + 0xa8, 0x95, 0x90, 0x08, 0x3b, 0x66, 0xb4, 0xdb, 0xf1, 0x90, 0x3f, 0x8c, 0xfe, 0xb7, 0xb5, 0x0b, + 0x53, 0x1a, 0xcf, 0x79, 0x3d, 0x91, 0x5a, 0xe9, 0xf8, 0xa9, 0x83, 0x09, 0x35, 0xeb, 0x5b, 0xc5, + 0x95, 0x59, 0x9e, 0xfc, 0xc4, 0x0e, 0x48, 0xb6, 0xe6, 0x66, 0x6c, 0x97, 0xf6, 0x40, 0x5e, 0x73, + 0x72, 0x82, 0x31, 0x54, 0xa5, 0xac, 0x4a, 0x56, 0x54, 0xb9, 0x19, 0xe2, 0xd0, 0x81, 0x45, 0x6e, + 0xaa, 0x9c, 0xfc, 0xc1, 0x7d, 0x2e, 0x1e, 0xd7, 0x89, 0x70, 0xbb, 0x1e, 0xf2, 0x07, 0xb4, 0xf9, + 0x22, 0xff, 0xf0, 0x00, 0x24, 0x6b, 0xa8, 0x9e, 0xa1, 0xbe, 0x81, 0x9c, 0x59, 0x72, 0xbf, 0x9f, + 0xf3, 0xf5, 0x7e, 0x1c, 0x8f, 0x54, 0x13, 0x12, 0x33, 0x88, 0x66, 0x71, 0xc1, 0x6d, 0xdc, 0xda, + 0xed, 0x7b, 0x5d, 0x7f, 0x18, 0x9d, 0x05, 0x87, 0x07, 0x0b, 0x8e, 0x06, 0x4b, 0xff, 0xaa, 0x36, + 0xac, 0xdf, 0x70, 0x3d, 0x7d, 0x46, 0xd8, 0x05, 0x95, 0xb5, 0x7d, 0xf6, 0x37, 0x9b, 0xba, 0x1f, + 0xb3, 0x32, 0x71, 0xea, 0x39, 0xba, 0xbf, 0xcb, 0xd6, 0xe5, 0xaa, 0x5a, 0x06, 0x09, 0xe4, 0x61, + 0xeb, 0xea, 0xc7, 0xcb, 0x0c, 0x0e, 0xde, 0x60, 0x0a, 0x8a, 0xd5, 0x08, 0x33, 0x88, 0x66, 0x19, + 0xd8, 0xea, 0x05, 0xa1, 0x65, 0xdf, 0x54, 0x93, 0xd7, 0x00, 0x00, 0x00, 0xff, 0xff, 0x71, 0x2b, + 0x6f, 0x0c, 0xc2, 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/replay_log.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/replay_log.pb.go new file mode 100644 index 0000000..d4b6f2a --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/replay_log.pb.go @@ -0,0 +1,522 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/replay_log.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Records the creation of a new replay session. We record the device listing +// here to capture the state of the cluster. +type NewReplaySession struct { + Devices *ListDevicesResponse `protobuf:"bytes,1,opt,name=devices,proto3" json:"devices,omitempty"` + SessionHandle string `protobuf:"bytes,2,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NewReplaySession) Reset() { *m = NewReplaySession{} } +func (m *NewReplaySession) String() string { return proto.CompactTextString(m) } +func (*NewReplaySession) ProtoMessage() {} +func (*NewReplaySession) Descriptor() ([]byte, []int) { + return fileDescriptor_9ee59cdea95caad8, []int{0} +} + +func (m *NewReplaySession) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NewReplaySession.Unmarshal(m, b) +} +func (m *NewReplaySession) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NewReplaySession.Marshal(b, m, deterministic) +} +func (m *NewReplaySession) XXX_Merge(src proto.Message) { + xxx_messageInfo_NewReplaySession.Merge(m, src) +} +func (m *NewReplaySession) XXX_Size() int { + return xxx_messageInfo_NewReplaySession.Size(m) +} +func (m *NewReplaySession) XXX_DiscardUnknown() { + xxx_messageInfo_NewReplaySession.DiscardUnknown(m) +} + +var xxx_messageInfo_NewReplaySession proto.InternalMessageInfo + +func (m *NewReplaySession) GetDevices() *ListDevicesResponse { + if m != nil { + return m.Devices + } + return nil +} + +func (m *NewReplaySession) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +type ReplayOp struct { + StartTimeUs float64 `protobuf:"fixed64,31,opt,name=start_time_us,json=startTimeUs,proto3" json:"start_time_us,omitempty"` + EndTimeUs float64 `protobuf:"fixed64,32,opt,name=end_time_us,json=endTimeUs,proto3" json:"end_time_us,omitempty"` + // Types that are valid to be assigned to Op: + // *ReplayOp_CreateSession + // *ReplayOp_ExtendSession + // *ReplayOp_PartialRunSetup + // *ReplayOp_RunStep + // *ReplayOp_CloseSession + // *ReplayOp_ListDevices + // *ReplayOp_ResetRequest + // *ReplayOp_MakeCallable + // *ReplayOp_RunCallable + // *ReplayOp_ReleaseCallable + // *ReplayOp_NewReplaySession + Op isReplayOp_Op `protobuf_oneof:"op"` + // Types that are valid to be assigned to Response: + // *ReplayOp_CreateSessionResponse + // *ReplayOp_ExtendSessionResponse + // *ReplayOp_PartialRunSetupResponse + // *ReplayOp_RunStepResponse + // *ReplayOp_CloseSessionResponse + // *ReplayOp_ListDevicesResponse + // *ReplayOp_ResetRequestResponse + // *ReplayOp_MakeCallableResponse + // *ReplayOp_RunCallableResponse + // *ReplayOp_ReleaseCallableResponse + Response isReplayOp_Response `protobuf_oneof:"response"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ReplayOp) Reset() { *m = ReplayOp{} } +func (m *ReplayOp) String() string { return proto.CompactTextString(m) } +func (*ReplayOp) ProtoMessage() {} +func (*ReplayOp) Descriptor() ([]byte, []int) { + return fileDescriptor_9ee59cdea95caad8, []int{1} +} + +func (m *ReplayOp) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ReplayOp.Unmarshal(m, b) +} +func (m *ReplayOp) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ReplayOp.Marshal(b, m, deterministic) +} +func (m *ReplayOp) XXX_Merge(src proto.Message) { + xxx_messageInfo_ReplayOp.Merge(m, src) +} +func (m *ReplayOp) XXX_Size() int { + return xxx_messageInfo_ReplayOp.Size(m) +} +func (m *ReplayOp) XXX_DiscardUnknown() { + xxx_messageInfo_ReplayOp.DiscardUnknown(m) +} + +var xxx_messageInfo_ReplayOp proto.InternalMessageInfo + +func (m *ReplayOp) GetStartTimeUs() float64 { + if m != nil { + return m.StartTimeUs + } + return 0 +} + +func (m *ReplayOp) GetEndTimeUs() float64 { + if m != nil { + return m.EndTimeUs + } + return 0 +} + +type isReplayOp_Op interface { + isReplayOp_Op() +} + +type ReplayOp_CreateSession struct { + CreateSession *CreateSessionRequest `protobuf:"bytes,1,opt,name=create_session,json=createSession,proto3,oneof"` +} + +type ReplayOp_ExtendSession struct { + ExtendSession *ExtendSessionRequest `protobuf:"bytes,2,opt,name=extend_session,json=extendSession,proto3,oneof"` +} + +type ReplayOp_PartialRunSetup struct { + PartialRunSetup *PartialRunSetupRequest `protobuf:"bytes,3,opt,name=partial_run_setup,json=partialRunSetup,proto3,oneof"` +} + +type ReplayOp_RunStep struct { + RunStep *RunStepRequest `protobuf:"bytes,4,opt,name=run_step,json=runStep,proto3,oneof"` +} + +type ReplayOp_CloseSession struct { + CloseSession *CloseSessionRequest `protobuf:"bytes,5,opt,name=close_session,json=closeSession,proto3,oneof"` +} + +type ReplayOp_ListDevices struct { + ListDevices *ListDevicesRequest `protobuf:"bytes,6,opt,name=list_devices,json=listDevices,proto3,oneof"` +} + +type ReplayOp_ResetRequest struct { + ResetRequest *ResetRequest `protobuf:"bytes,7,opt,name=reset_request,json=resetRequest,proto3,oneof"` +} + +type ReplayOp_MakeCallable struct { + MakeCallable *MakeCallableRequest `protobuf:"bytes,8,opt,name=make_callable,json=makeCallable,proto3,oneof"` +} + +type ReplayOp_RunCallable struct { + RunCallable *RunCallableRequest `protobuf:"bytes,9,opt,name=run_callable,json=runCallable,proto3,oneof"` +} + +type ReplayOp_ReleaseCallable struct { + ReleaseCallable *ReleaseCallableRequest `protobuf:"bytes,10,opt,name=release_callable,json=releaseCallable,proto3,oneof"` +} + +type ReplayOp_NewReplaySession struct { + NewReplaySession *NewReplaySession `protobuf:"bytes,11,opt,name=new_replay_session,json=newReplaySession,proto3,oneof"` +} + +func (*ReplayOp_CreateSession) isReplayOp_Op() {} + +func (*ReplayOp_ExtendSession) isReplayOp_Op() {} + +func (*ReplayOp_PartialRunSetup) isReplayOp_Op() {} + +func (*ReplayOp_RunStep) isReplayOp_Op() {} + +func (*ReplayOp_CloseSession) isReplayOp_Op() {} + +func (*ReplayOp_ListDevices) isReplayOp_Op() {} + +func (*ReplayOp_ResetRequest) isReplayOp_Op() {} + +func (*ReplayOp_MakeCallable) isReplayOp_Op() {} + +func (*ReplayOp_RunCallable) isReplayOp_Op() {} + +func (*ReplayOp_ReleaseCallable) isReplayOp_Op() {} + +func (*ReplayOp_NewReplaySession) isReplayOp_Op() {} + +func (m *ReplayOp) GetOp() isReplayOp_Op { + if m != nil { + return m.Op + } + return nil +} + +func (m *ReplayOp) GetCreateSession() *CreateSessionRequest { + if x, ok := m.GetOp().(*ReplayOp_CreateSession); ok { + return x.CreateSession + } + return nil +} + +func (m *ReplayOp) GetExtendSession() *ExtendSessionRequest { + if x, ok := m.GetOp().(*ReplayOp_ExtendSession); ok { + return x.ExtendSession + } + return nil +} + +func (m *ReplayOp) GetPartialRunSetup() *PartialRunSetupRequest { + if x, ok := m.GetOp().(*ReplayOp_PartialRunSetup); ok { + return x.PartialRunSetup + } + return nil +} + +func (m *ReplayOp) GetRunStep() *RunStepRequest { + if x, ok := m.GetOp().(*ReplayOp_RunStep); ok { + return x.RunStep + } + return nil +} + +func (m *ReplayOp) GetCloseSession() *CloseSessionRequest { + if x, ok := m.GetOp().(*ReplayOp_CloseSession); ok { + return x.CloseSession + } + return nil +} + +func (m *ReplayOp) GetListDevices() *ListDevicesRequest { + if x, ok := m.GetOp().(*ReplayOp_ListDevices); ok { + return x.ListDevices + } + return nil +} + +func (m *ReplayOp) GetResetRequest() *ResetRequest { + if x, ok := m.GetOp().(*ReplayOp_ResetRequest); ok { + return x.ResetRequest + } + return nil +} + +func (m *ReplayOp) GetMakeCallable() *MakeCallableRequest { + if x, ok := m.GetOp().(*ReplayOp_MakeCallable); ok { + return x.MakeCallable + } + return nil +} + +func (m *ReplayOp) GetRunCallable() *RunCallableRequest { + if x, ok := m.GetOp().(*ReplayOp_RunCallable); ok { + return x.RunCallable + } + return nil +} + +func (m *ReplayOp) GetReleaseCallable() *ReleaseCallableRequest { + if x, ok := m.GetOp().(*ReplayOp_ReleaseCallable); ok { + return x.ReleaseCallable + } + return nil +} + +func (m *ReplayOp) GetNewReplaySession() *NewReplaySession { + if x, ok := m.GetOp().(*ReplayOp_NewReplaySession); ok { + return x.NewReplaySession + } + return nil +} + +type isReplayOp_Response interface { + isReplayOp_Response() +} + +type ReplayOp_CreateSessionResponse struct { + CreateSessionResponse *CreateSessionResponse `protobuf:"bytes,21,opt,name=create_session_response,json=createSessionResponse,proto3,oneof"` +} + +type ReplayOp_ExtendSessionResponse struct { + ExtendSessionResponse *ExtendSessionResponse `protobuf:"bytes,22,opt,name=extend_session_response,json=extendSessionResponse,proto3,oneof"` +} + +type ReplayOp_PartialRunSetupResponse struct { + PartialRunSetupResponse *PartialRunSetupResponse `protobuf:"bytes,23,opt,name=partial_run_setup_response,json=partialRunSetupResponse,proto3,oneof"` +} + +type ReplayOp_RunStepResponse struct { + RunStepResponse *RunStepResponse `protobuf:"bytes,24,opt,name=run_step_response,json=runStepResponse,proto3,oneof"` +} + +type ReplayOp_CloseSessionResponse struct { + CloseSessionResponse *CloseSessionResponse `protobuf:"bytes,25,opt,name=close_session_response,json=closeSessionResponse,proto3,oneof"` +} + +type ReplayOp_ListDevicesResponse struct { + ListDevicesResponse *ListDevicesResponse `protobuf:"bytes,26,opt,name=list_devices_response,json=listDevicesResponse,proto3,oneof"` +} + +type ReplayOp_ResetRequestResponse struct { + ResetRequestResponse *ResetResponse `protobuf:"bytes,27,opt,name=reset_request_response,json=resetRequestResponse,proto3,oneof"` +} + +type ReplayOp_MakeCallableResponse struct { + MakeCallableResponse *MakeCallableResponse `protobuf:"bytes,28,opt,name=make_callable_response,json=makeCallableResponse,proto3,oneof"` +} + +type ReplayOp_RunCallableResponse struct { + RunCallableResponse *RunCallableResponse `protobuf:"bytes,29,opt,name=run_callable_response,json=runCallableResponse,proto3,oneof"` +} + +type ReplayOp_ReleaseCallableResponse struct { + ReleaseCallableResponse *ReleaseCallableResponse `protobuf:"bytes,30,opt,name=release_callable_response,json=releaseCallableResponse,proto3,oneof"` +} + +func (*ReplayOp_CreateSessionResponse) isReplayOp_Response() {} + +func (*ReplayOp_ExtendSessionResponse) isReplayOp_Response() {} + +func (*ReplayOp_PartialRunSetupResponse) isReplayOp_Response() {} + +func (*ReplayOp_RunStepResponse) isReplayOp_Response() {} + +func (*ReplayOp_CloseSessionResponse) isReplayOp_Response() {} + +func (*ReplayOp_ListDevicesResponse) isReplayOp_Response() {} + +func (*ReplayOp_ResetRequestResponse) isReplayOp_Response() {} + +func (*ReplayOp_MakeCallableResponse) isReplayOp_Response() {} + +func (*ReplayOp_RunCallableResponse) isReplayOp_Response() {} + +func (*ReplayOp_ReleaseCallableResponse) isReplayOp_Response() {} + +func (m *ReplayOp) GetResponse() isReplayOp_Response { + if m != nil { + return m.Response + } + return nil +} + +func (m *ReplayOp) GetCreateSessionResponse() *CreateSessionResponse { + if x, ok := m.GetResponse().(*ReplayOp_CreateSessionResponse); ok { + return x.CreateSessionResponse + } + return nil +} + +func (m *ReplayOp) GetExtendSessionResponse() *ExtendSessionResponse { + if x, ok := m.GetResponse().(*ReplayOp_ExtendSessionResponse); ok { + return x.ExtendSessionResponse + } + return nil +} + +func (m *ReplayOp) GetPartialRunSetupResponse() *PartialRunSetupResponse { + if x, ok := m.GetResponse().(*ReplayOp_PartialRunSetupResponse); ok { + return x.PartialRunSetupResponse + } + return nil +} + +func (m *ReplayOp) GetRunStepResponse() *RunStepResponse { + if x, ok := m.GetResponse().(*ReplayOp_RunStepResponse); ok { + return x.RunStepResponse + } + return nil +} + +func (m *ReplayOp) GetCloseSessionResponse() *CloseSessionResponse { + if x, ok := m.GetResponse().(*ReplayOp_CloseSessionResponse); ok { + return x.CloseSessionResponse + } + return nil +} + +func (m *ReplayOp) GetListDevicesResponse() *ListDevicesResponse { + if x, ok := m.GetResponse().(*ReplayOp_ListDevicesResponse); ok { + return x.ListDevicesResponse + } + return nil +} + +func (m *ReplayOp) GetResetRequestResponse() *ResetResponse { + if x, ok := m.GetResponse().(*ReplayOp_ResetRequestResponse); ok { + return x.ResetRequestResponse + } + return nil +} + +func (m *ReplayOp) GetMakeCallableResponse() *MakeCallableResponse { + if x, ok := m.GetResponse().(*ReplayOp_MakeCallableResponse); ok { + return x.MakeCallableResponse + } + return nil +} + +func (m *ReplayOp) GetRunCallableResponse() *RunCallableResponse { + if x, ok := m.GetResponse().(*ReplayOp_RunCallableResponse); ok { + return x.RunCallableResponse + } + return nil +} + +func (m *ReplayOp) GetReleaseCallableResponse() *ReleaseCallableResponse { + if x, ok := m.GetResponse().(*ReplayOp_ReleaseCallableResponse); ok { + return x.ReleaseCallableResponse + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*ReplayOp) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*ReplayOp_CreateSession)(nil), + (*ReplayOp_ExtendSession)(nil), + (*ReplayOp_PartialRunSetup)(nil), + (*ReplayOp_RunStep)(nil), + (*ReplayOp_CloseSession)(nil), + (*ReplayOp_ListDevices)(nil), + (*ReplayOp_ResetRequest)(nil), + (*ReplayOp_MakeCallable)(nil), + (*ReplayOp_RunCallable)(nil), + (*ReplayOp_ReleaseCallable)(nil), + (*ReplayOp_NewReplaySession)(nil), + (*ReplayOp_CreateSessionResponse)(nil), + (*ReplayOp_ExtendSessionResponse)(nil), + (*ReplayOp_PartialRunSetupResponse)(nil), + (*ReplayOp_RunStepResponse)(nil), + (*ReplayOp_CloseSessionResponse)(nil), + (*ReplayOp_ListDevicesResponse)(nil), + (*ReplayOp_ResetRequestResponse)(nil), + (*ReplayOp_MakeCallableResponse)(nil), + (*ReplayOp_RunCallableResponse)(nil), + (*ReplayOp_ReleaseCallableResponse)(nil), + } +} + +func init() { + proto.RegisterType((*NewReplaySession)(nil), "tensorflow.NewReplaySession") + proto.RegisterType((*ReplayOp)(nil), "tensorflow.ReplayOp") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/replay_log.proto", fileDescriptor_9ee59cdea95caad8) +} + +var fileDescriptor_9ee59cdea95caad8 = []byte{ + // 714 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x8c, 0x95, 0xcf, 0x4f, 0xdb, 0x30, + 0x14, 0xc7, 0x09, 0x63, 0x50, 0x5e, 0x29, 0x3f, 0xbc, 0x01, 0xa1, 0x30, 0xe8, 0x3a, 0x21, 0xb1, + 0x4b, 0x2b, 0x6d, 0x87, 0x69, 0xa7, 0x49, 0x74, 0x9b, 0x8a, 0xc4, 0x06, 0x0b, 0x43, 0x9a, 0xd8, + 0xc1, 0x4a, 0xd3, 0x47, 0x89, 0x48, 0xe3, 0xcc, 0x76, 0xc6, 0xf6, 0x9f, 0xef, 0xb0, 0xc3, 0x14, + 0x27, 0xad, 0x9d, 0x3a, 0xa0, 0xdd, 0xd2, 0xef, 0xfb, 0xf6, 0x63, 0xbf, 0x67, 0x7f, 0x13, 0x78, + 0x29, 0x31, 0x16, 0x8c, 0x5f, 0x47, 0xec, 0xae, 0x1b, 0x30, 0x8e, 0xdd, 0x84, 0x33, 0xc9, 0x06, + 0xe9, 0x75, 0x97, 0x63, 0x12, 0xf9, 0xbf, 0x69, 0xc4, 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b/tensorflow/go/core/protobuf/for_core_protos_go_proto/rewriter_config.pb.go @@ -0,0 +1,763 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/rewriter_config.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + attr_value_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type RewriterConfig_Toggle int32 + +const ( + RewriterConfig_DEFAULT RewriterConfig_Toggle = 0 + RewriterConfig_ON RewriterConfig_Toggle = 1 + RewriterConfig_OFF RewriterConfig_Toggle = 2 + // Enable some aggressive optimizations that use assumptions that TF graphs + // may break. For example, assume the shape of a placeholder matches its + // actual feed. + RewriterConfig_AGGRESSIVE RewriterConfig_Toggle = 3 +) + +var RewriterConfig_Toggle_name = map[int32]string{ + 0: "DEFAULT", + 1: "ON", + 2: "OFF", + 3: "AGGRESSIVE", +} + +var RewriterConfig_Toggle_value = map[string]int32{ + "DEFAULT": 0, + "ON": 1, + "OFF": 2, + "AGGRESSIVE": 3, +} + +func (x RewriterConfig_Toggle) String() string { + return proto.EnumName(RewriterConfig_Toggle_name, int32(x)) +} + +func (RewriterConfig_Toggle) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{2, 0} +} + +// Enum for layout conversion between NCHW and NHWC on CPU. Default is OFF. +type RewriterConfig_CpuLayout int32 + +const ( + RewriterConfig_NO_CONVERSION_ON_CPU RewriterConfig_CpuLayout = 0 + RewriterConfig_NCHW_TO_NHWC RewriterConfig_CpuLayout = 1 + RewriterConfig_NHWC_TO_NCHW RewriterConfig_CpuLayout = 2 +) + +var RewriterConfig_CpuLayout_name = map[int32]string{ + 0: "NO_CONVERSION_ON_CPU", + 1: "NCHW_TO_NHWC", + 2: "NHWC_TO_NCHW", +} + +var RewriterConfig_CpuLayout_value = map[string]int32{ + "NO_CONVERSION_ON_CPU": 0, + "NCHW_TO_NHWC": 1, + "NHWC_TO_NCHW": 2, +} + +func (x RewriterConfig_CpuLayout) String() string { + return proto.EnumName(RewriterConfig_CpuLayout_name, int32(x)) +} + +func (RewriterConfig_CpuLayout) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{2, 1} +} + +// Enum controlling the number of times to run optimizers. The default is to +// run them twice. +type RewriterConfig_NumIterationsType int32 + +const ( + RewriterConfig_DEFAULT_NUM_ITERS RewriterConfig_NumIterationsType = 0 + RewriterConfig_ONE RewriterConfig_NumIterationsType = 1 + RewriterConfig_TWO RewriterConfig_NumIterationsType = 2 +) + +var RewriterConfig_NumIterationsType_name = map[int32]string{ + 0: "DEFAULT_NUM_ITERS", + 1: "ONE", + 2: "TWO", +} + +var RewriterConfig_NumIterationsType_value = map[string]int32{ + "DEFAULT_NUM_ITERS": 0, + "ONE": 1, + "TWO": 2, +} + +func (x RewriterConfig_NumIterationsType) String() string { + return proto.EnumName(RewriterConfig_NumIterationsType_name, int32(x)) +} + +func (RewriterConfig_NumIterationsType) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{2, 2} +} + +type RewriterConfig_MemOptType int32 + +const ( + // The default setting (SCHEDULING and SWAPPING HEURISTICS only) + RewriterConfig_DEFAULT_MEM_OPT RewriterConfig_MemOptType = 0 + // Disabled in the meta-optimizer. + RewriterConfig_NO_MEM_OPT RewriterConfig_MemOptType = 1 + // Driven by manual op-level annotations. + RewriterConfig_MANUAL RewriterConfig_MemOptType = 2 + // Swapping heuristic will move a tensor from the GPU to the CPU and move + // it back when needed to reduce peak memory usage. + RewriterConfig_SWAPPING_HEURISTICS RewriterConfig_MemOptType = 4 + // Recomputation heuristics will recompute ops (such as Relu activation) + // during backprop instead of storing them, reducing peak memory usage. + RewriterConfig_RECOMPUTATION_HEURISTICS RewriterConfig_MemOptType = 5 + // Scheduling will split big ops such as AddN and try to enforce a schedule + // of the new computations that decreases peak memory usage. + RewriterConfig_SCHEDULING_HEURISTICS RewriterConfig_MemOptType = 6 + // Use any combination of swapping and recomputation heuristics. + RewriterConfig_HEURISTICS RewriterConfig_MemOptType = 3 +) + +var RewriterConfig_MemOptType_name = map[int32]string{ + 0: "DEFAULT_MEM_OPT", + 1: "NO_MEM_OPT", + 2: "MANUAL", + 4: "SWAPPING_HEURISTICS", + 5: "RECOMPUTATION_HEURISTICS", + 6: "SCHEDULING_HEURISTICS", + 3: "HEURISTICS", +} + +var RewriterConfig_MemOptType_value = map[string]int32{ + "DEFAULT_MEM_OPT": 0, + "NO_MEM_OPT": 1, + "MANUAL": 2, + "SWAPPING_HEURISTICS": 4, + "RECOMPUTATION_HEURISTICS": 5, + "SCHEDULING_HEURISTICS": 6, + "HEURISTICS": 3, +} + +func (x RewriterConfig_MemOptType) String() string { + return proto.EnumName(RewriterConfig_MemOptType_name, int32(x)) +} + +func (RewriterConfig_MemOptType) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{2, 3} +} + +type AutoParallelOptions struct { + Enable bool `protobuf:"varint,1,opt,name=enable,proto3" json:"enable,omitempty"` + NumReplicas int32 `protobuf:"varint,2,opt,name=num_replicas,json=numReplicas,proto3" json:"num_replicas,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AutoParallelOptions) Reset() { *m = AutoParallelOptions{} } +func (m *AutoParallelOptions) String() string { return proto.CompactTextString(m) } +func (*AutoParallelOptions) ProtoMessage() {} +func (*AutoParallelOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{0} +} + +func (m *AutoParallelOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AutoParallelOptions.Unmarshal(m, b) +} +func (m *AutoParallelOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AutoParallelOptions.Marshal(b, m, deterministic) +} +func (m *AutoParallelOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_AutoParallelOptions.Merge(m, src) +} +func (m *AutoParallelOptions) XXX_Size() int { + return xxx_messageInfo_AutoParallelOptions.Size(m) +} +func (m *AutoParallelOptions) XXX_DiscardUnknown() { + xxx_messageInfo_AutoParallelOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_AutoParallelOptions proto.InternalMessageInfo + +func (m *AutoParallelOptions) GetEnable() bool { + if m != nil { + return m.Enable + } + return false +} + +func (m *AutoParallelOptions) GetNumReplicas() int32 { + if m != nil { + return m.NumReplicas + } + return 0 +} + +type ScopedAllocatorOptions struct { + // If present, only perform optimization for these ops. + EnableOp []string `protobuf:"bytes,1,rep,name=enable_op,json=enableOp,proto3" json:"enable_op,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ScopedAllocatorOptions) Reset() { *m = ScopedAllocatorOptions{} } +func (m *ScopedAllocatorOptions) String() string { return proto.CompactTextString(m) } +func (*ScopedAllocatorOptions) ProtoMessage() {} +func (*ScopedAllocatorOptions) Descriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{1} +} + +func (m *ScopedAllocatorOptions) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ScopedAllocatorOptions.Unmarshal(m, b) +} +func (m *ScopedAllocatorOptions) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ScopedAllocatorOptions.Marshal(b, m, deterministic) +} +func (m *ScopedAllocatorOptions) XXX_Merge(src proto.Message) { + xxx_messageInfo_ScopedAllocatorOptions.Merge(m, src) +} +func (m *ScopedAllocatorOptions) XXX_Size() int { + return xxx_messageInfo_ScopedAllocatorOptions.Size(m) +} +func (m *ScopedAllocatorOptions) XXX_DiscardUnknown() { + xxx_messageInfo_ScopedAllocatorOptions.DiscardUnknown(m) +} + +var xxx_messageInfo_ScopedAllocatorOptions proto.InternalMessageInfo + +func (m *ScopedAllocatorOptions) GetEnableOp() []string { + if m != nil { + return m.EnableOp + } + return nil +} + +type RewriterConfig struct { + // CPU Conversion settings between NHCW and NCHW. + CpuLayoutConversion RewriterConfig_CpuLayout `protobuf:"varint,50,opt,name=cpu_layout_conversion,json=cpuLayoutConversion,proto3,enum=tensorflow.RewriterConfig_CpuLayout" json:"cpu_layout_conversion,omitempty"` + // Optimize tensor layouts (default is ON) + // e.g. This will try to use NCHW layout on GPU which is faster. + LayoutOptimizer RewriterConfig_Toggle `protobuf:"varint,1,opt,name=layout_optimizer,json=layoutOptimizer,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"layout_optimizer,omitempty"` + // Fold constants (default is ON) + // Statically infer the value of tensors when possible, and materialize the + // result using constants. + ConstantFolding RewriterConfig_Toggle `protobuf:"varint,3,opt,name=constant_folding,json=constantFolding,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"constant_folding,omitempty"` + // Shape optimizations (default is ON) + // Simplify computations made on shapes. + ShapeOptimization RewriterConfig_Toggle `protobuf:"varint,13,opt,name=shape_optimization,json=shapeOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"shape_optimization,omitempty"` + // Remapping (default is ON) + // Remap subgraphs onto more efficient implementations. + Remapping RewriterConfig_Toggle `protobuf:"varint,14,opt,name=remapping,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"remapping,omitempty"` + // Common subgraph elimination (default is ON) + // e.g. Simplify arithmetic ops; merge ops with same value (like constants). + CommonSubgraphElimination RewriterConfig_Toggle `protobuf:"varint,24,opt,name=common_subgraph_elimination,json=commonSubgraphElimination,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"common_subgraph_elimination,omitempty"` + // Arithmetic optimizations (default is ON) + // e.g. Simplify arithmetic ops; merge ops with same value (like constants). + ArithmeticOptimization RewriterConfig_Toggle `protobuf:"varint,7,opt,name=arithmetic_optimization,json=arithmeticOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"arithmetic_optimization,omitempty"` + // Control dependency optimizations (default is ON). + // Remove redundant control dependencies, which may enable other optimization. + DependencyOptimization RewriterConfig_Toggle `protobuf:"varint,8,opt,name=dependency_optimization,json=dependencyOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"dependency_optimization,omitempty"` + // Loop optimizations (default is ON). + LoopOptimization RewriterConfig_Toggle `protobuf:"varint,9,opt,name=loop_optimization,json=loopOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"loop_optimization,omitempty"` + // Function optimizations (default is ON). + FunctionOptimization RewriterConfig_Toggle `protobuf:"varint,10,opt,name=function_optimization,json=functionOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"function_optimization,omitempty"` + // Strips debug-related nodes from the graph (off by default). + DebugStripper RewriterConfig_Toggle `protobuf:"varint,11,opt,name=debug_stripper,json=debugStripper,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"debug_stripper,omitempty"` + // If true, don't remove unnecessary ops from the graph + DisableModelPruning bool `protobuf:"varint,2,opt,name=disable_model_pruning,json=disableModelPruning,proto3" json:"disable_model_pruning,omitempty"` + // Try to allocate some independent Op outputs contiguously in order to + // merge or eliminate downstream Ops (off by default). + ScopedAllocatorOptimization RewriterConfig_Toggle `protobuf:"varint,15,opt,name=scoped_allocator_optimization,json=scopedAllocatorOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"scoped_allocator_optimization,omitempty"` + // Force small ops onto the CPU (default is OFF). + PinToHostOptimization RewriterConfig_Toggle `protobuf:"varint,18,opt,name=pin_to_host_optimization,json=pinToHostOptimization,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"pin_to_host_optimization,omitempty"` + // Enable the swap of kernel implementations based on the device placement + // (default is ON). + ImplementationSelector RewriterConfig_Toggle `protobuf:"varint,22,opt,name=implementation_selector,json=implementationSelector,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"implementation_selector,omitempty"` + // Optimize data types for CUDA (default is OFF). + // This will try to use float16 on GPU which is faster. + // Note that this can change the numerical stability of the graph and may + // require the use of loss scaling to maintain model convergence. + AutoMixedPrecision RewriterConfig_Toggle `protobuf:"varint,23,opt,name=auto_mixed_precision,json=autoMixedPrecision,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"auto_mixed_precision,omitempty"` + // Optimize data types for MKL (default is OFF). + // This will try to use bfloat16 on CPUs, which is faster. + // Note that this can change the numerical stability of the graph. + AutoMixedPrecisionMkl RewriterConfig_Toggle `protobuf:"varint,25,opt,name=auto_mixed_precision_mkl,json=autoMixedPrecisionMkl,proto3,enum=tensorflow.RewriterConfig_Toggle" json:"auto_mixed_precision_mkl,omitempty"` + // Disable the entire meta optimizer (off by default). + DisableMetaOptimizer bool `protobuf:"varint,19,opt,name=disable_meta_optimizer,json=disableMetaOptimizer,proto3" json:"disable_meta_optimizer,omitempty"` + // Controls how many times we run the optimizers in meta optimizer (default + // is once). + MetaOptimizerIterations RewriterConfig_NumIterationsType `protobuf:"varint,12,opt,name=meta_optimizer_iterations,json=metaOptimizerIterations,proto3,enum=tensorflow.RewriterConfig_NumIterationsType" json:"meta_optimizer_iterations,omitempty"` + // The minimum number of nodes in a graph to optimizer. For smaller graphs, + // optimization is skipped. + // 0 means the system picks an appropriate number. + // < 0 means do not skip optimization. + MinGraphNodes int32 `protobuf:"varint,17,opt,name=min_graph_nodes,json=minGraphNodes,proto3" json:"min_graph_nodes,omitempty"` + // Disable optimizations that assume compressed tensors. Note that this flag + // is experimental and may be removed in the future. + ExperimentalDisableCompressedTensorOptimization bool `protobuf:"varint,26,opt,name=experimental_disable_compressed_tensor_optimization,json=experimentalDisableCompressedTensorOptimization,proto3" json:"experimental_disable_compressed_tensor_optimization,omitempty"` + // Configures memory optimization passes through the meta-optimizer. Has no + // effect on manually requested memory optimization passes in the optimizers + // field. + MemoryOptimization RewriterConfig_MemOptType `protobuf:"varint,4,opt,name=memory_optimization,json=memoryOptimization,proto3,enum=tensorflow.RewriterConfig_MemOptType" json:"memory_optimization,omitempty"` + // A node name scope for node names which are valid outputs of recomputations. + // Inputs to nodes that match this scope may be recomputed (subject either to + // manual annotation of those input nodes or to manual annotation and + // heuristics depending on memory_optimization), but the nodes themselves will + // not be recomputed. This matches any sub-scopes as well, meaning the scope + // can appear not just as a top-level scope. For example, if the value is + // "gradients/", the default, it will match node name "gradients/foo", + // "foo/gradients/bar", but not "foo_gradients/" + MemoryOptimizerTargetNodeNameScope string `protobuf:"bytes,6,opt,name=memory_optimizer_target_node_name_scope,json=memoryOptimizerTargetNodeNameScope,proto3" json:"memory_optimizer_target_node_name_scope,omitempty"` + // Maximum number of milliseconds to spend optimizing a single graph before + // timing out. If equal to 0 the system picks a default (currently 5 minutes). + // If less than 0 the optimizer will never time out. + MetaOptimizerTimeoutMs int64 `protobuf:"varint,20,opt,name=meta_optimizer_timeout_ms,json=metaOptimizerTimeoutMs,proto3" json:"meta_optimizer_timeout_ms,omitempty"` + // Configures AutoParallel optimization passes either through the + // meta-optimizer or when manually specified through the optimizers field. + AutoParallel *AutoParallelOptions `protobuf:"bytes,5,opt,name=auto_parallel,json=autoParallel,proto3" json:"auto_parallel,omitempty"` + // If true, any optimization pass failing will cause the MetaOptimizer to + // stop with an error. By default - or when set to false, failing passes are + // skipped silently. + FailOnOptimizerErrors bool `protobuf:"varint,21,opt,name=fail_on_optimizer_errors,json=failOnOptimizerErrors,proto3" json:"fail_on_optimizer_errors,omitempty"` + ScopedAllocatorOpts *ScopedAllocatorOptions `protobuf:"bytes,16,opt,name=scoped_allocator_opts,json=scopedAllocatorOpts,proto3" json:"scoped_allocator_opts,omitempty"` + // If non-empty, will use this as an alternative way to specify a list of + // optimizations to turn on and the order of the optimizations (replacing the + // meta-optimizer). + // + // Of the RewriterConfig options, only the AutoParallel configuration options + // (the auto_parallel field) apply to manually requested optimization passes + // ("autoparallel"). Memory optimization passes ("memory") invoked here are + // not configurable (in contrast to memory optimization passes through the + // meta-optimizer) and act only on manual op annotations. + // + // Custom optimizers (see custom_optimizers) that are not part of this + // schedule will be run after - in the order that they were specified. + Optimizers []string `protobuf:"bytes,100,rep,name=optimizers,proto3" json:"optimizers,omitempty"` + // list of CustomGraphOptimizers to apply. + CustomOptimizers []*RewriterConfig_CustomGraphOptimizer `protobuf:"bytes,200,rep,name=custom_optimizers,json=customOptimizers,proto3" json:"custom_optimizers,omitempty"` + // VerifierConfig specifying the verifiers to be run after every optimizer. + InterOptimizerVerifierConfig *VerifierConfig `protobuf:"bytes,300,opt,name=inter_optimizer_verifier_config,json=interOptimizerVerifierConfig,proto3" json:"inter_optimizer_verifier_config,omitempty"` + // VerifierConfig specifying the verifiers to be run at the end, after all + // optimizers have run. + PostOptimizationVerifierConfig *VerifierConfig `protobuf:"bytes,301,opt,name=post_optimization_verifier_config,json=postOptimizationVerifierConfig,proto3" json:"post_optimization_verifier_config,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RewriterConfig) Reset() { *m = RewriterConfig{} } +func (m *RewriterConfig) String() string { return proto.CompactTextString(m) } +func (*RewriterConfig) ProtoMessage() {} +func (*RewriterConfig) Descriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{2} +} + +func (m *RewriterConfig) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RewriterConfig.Unmarshal(m, b) +} +func (m *RewriterConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RewriterConfig.Marshal(b, m, deterministic) +} +func (m *RewriterConfig) XXX_Merge(src proto.Message) { + xxx_messageInfo_RewriterConfig.Merge(m, src) +} +func (m *RewriterConfig) XXX_Size() int { + return xxx_messageInfo_RewriterConfig.Size(m) +} +func (m *RewriterConfig) XXX_DiscardUnknown() { + xxx_messageInfo_RewriterConfig.DiscardUnknown(m) +} + +var xxx_messageInfo_RewriterConfig proto.InternalMessageInfo + +func (m *RewriterConfig) GetCpuLayoutConversion() RewriterConfig_CpuLayout { + if m != nil { + return m.CpuLayoutConversion + } + return RewriterConfig_NO_CONVERSION_ON_CPU +} + +func (m *RewriterConfig) GetLayoutOptimizer() RewriterConfig_Toggle { + if m != nil { + return m.LayoutOptimizer + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetConstantFolding() RewriterConfig_Toggle { + if m != nil { + return m.ConstantFolding + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetShapeOptimization() RewriterConfig_Toggle { + if m != nil { + return m.ShapeOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetRemapping() RewriterConfig_Toggle { + if m != nil { + return m.Remapping + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetCommonSubgraphElimination() RewriterConfig_Toggle { + if m != nil { + return m.CommonSubgraphElimination + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetArithmeticOptimization() RewriterConfig_Toggle { + if m != nil { + return m.ArithmeticOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetDependencyOptimization() RewriterConfig_Toggle { + if m != nil { + return m.DependencyOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetLoopOptimization() RewriterConfig_Toggle { + if m != nil { + return m.LoopOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetFunctionOptimization() RewriterConfig_Toggle { + if m != nil { + return m.FunctionOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetDebugStripper() RewriterConfig_Toggle { + if m != nil { + return m.DebugStripper + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetDisableModelPruning() bool { + if m != nil { + return m.DisableModelPruning + } + return false +} + +func (m *RewriterConfig) GetScopedAllocatorOptimization() RewriterConfig_Toggle { + if m != nil { + return m.ScopedAllocatorOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetPinToHostOptimization() RewriterConfig_Toggle { + if m != nil { + return m.PinToHostOptimization + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetImplementationSelector() RewriterConfig_Toggle { + if m != nil { + return m.ImplementationSelector + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetAutoMixedPrecision() RewriterConfig_Toggle { + if m != nil { + return m.AutoMixedPrecision + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetAutoMixedPrecisionMkl() RewriterConfig_Toggle { + if m != nil { + return m.AutoMixedPrecisionMkl + } + return RewriterConfig_DEFAULT +} + +func (m *RewriterConfig) GetDisableMetaOptimizer() bool { + if m != nil { + return m.DisableMetaOptimizer + } + return false +} + +func (m *RewriterConfig) GetMetaOptimizerIterations() RewriterConfig_NumIterationsType { + if m != nil { + return m.MetaOptimizerIterations + } + return RewriterConfig_DEFAULT_NUM_ITERS +} + +func (m *RewriterConfig) GetMinGraphNodes() int32 { + if m != nil { + return m.MinGraphNodes + } + return 0 +} + +func (m *RewriterConfig) GetExperimentalDisableCompressedTensorOptimization() bool { + if m != nil { + return m.ExperimentalDisableCompressedTensorOptimization + } + return false +} + +func (m *RewriterConfig) GetMemoryOptimization() RewriterConfig_MemOptType { + if m != nil { + return m.MemoryOptimization + } + return RewriterConfig_DEFAULT_MEM_OPT +} + +func (m *RewriterConfig) GetMemoryOptimizerTargetNodeNameScope() string { + if m != nil { + return m.MemoryOptimizerTargetNodeNameScope + } + return "" +} + +func (m *RewriterConfig) GetMetaOptimizerTimeoutMs() int64 { + if m != nil { + return m.MetaOptimizerTimeoutMs + } + return 0 +} + +func (m *RewriterConfig) GetAutoParallel() *AutoParallelOptions { + if m != nil { + return m.AutoParallel + } + return nil +} + +func (m *RewriterConfig) GetFailOnOptimizerErrors() bool { + if m != nil { + return m.FailOnOptimizerErrors + } + return false +} + +func (m *RewriterConfig) GetScopedAllocatorOpts() *ScopedAllocatorOptions { + if m != nil { + return m.ScopedAllocatorOpts + } + return nil +} + +func (m *RewriterConfig) GetOptimizers() []string { + if m != nil { + return m.Optimizers + } + return nil +} + +func (m *RewriterConfig) GetCustomOptimizers() []*RewriterConfig_CustomGraphOptimizer { + if m != nil { + return m.CustomOptimizers + } + return nil +} + +func (m *RewriterConfig) GetInterOptimizerVerifierConfig() *VerifierConfig { + if m != nil { + return m.InterOptimizerVerifierConfig + } + return nil +} + +func (m *RewriterConfig) GetPostOptimizationVerifierConfig() *VerifierConfig { + if m != nil { + return m.PostOptimizationVerifierConfig + } + return nil +} + +// Message to describe custom graph optimizer and its parameters +type RewriterConfig_CustomGraphOptimizer struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + ParameterMap map[string]*attr_value_go_proto.AttrValue `protobuf:"bytes,2,rep,name=parameter_map,json=parameterMap,proto3" json:"parameter_map,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RewriterConfig_CustomGraphOptimizer) Reset() { *m = RewriterConfig_CustomGraphOptimizer{} } +func (m *RewriterConfig_CustomGraphOptimizer) String() string { return proto.CompactTextString(m) } +func (*RewriterConfig_CustomGraphOptimizer) ProtoMessage() {} +func (*RewriterConfig_CustomGraphOptimizer) Descriptor() ([]byte, []int) { + return fileDescriptor_1dd7de60bf190bbb, []int{2, 0} +} + +func (m *RewriterConfig_CustomGraphOptimizer) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RewriterConfig_CustomGraphOptimizer.Unmarshal(m, b) +} +func (m *RewriterConfig_CustomGraphOptimizer) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RewriterConfig_CustomGraphOptimizer.Marshal(b, m, deterministic) +} +func (m *RewriterConfig_CustomGraphOptimizer) XXX_Merge(src proto.Message) { + xxx_messageInfo_RewriterConfig_CustomGraphOptimizer.Merge(m, src) +} +func (m *RewriterConfig_CustomGraphOptimizer) XXX_Size() int { + return xxx_messageInfo_RewriterConfig_CustomGraphOptimizer.Size(m) +} +func (m *RewriterConfig_CustomGraphOptimizer) XXX_DiscardUnknown() { + xxx_messageInfo_RewriterConfig_CustomGraphOptimizer.DiscardUnknown(m) +} + +var xxx_messageInfo_RewriterConfig_CustomGraphOptimizer proto.InternalMessageInfo + +func (m *RewriterConfig_CustomGraphOptimizer) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *RewriterConfig_CustomGraphOptimizer) GetParameterMap() map[string]*attr_value_go_proto.AttrValue { + if m != nil { + return m.ParameterMap + } + return nil +} + +func init() { + proto.RegisterEnum("tensorflow.RewriterConfig_Toggle", RewriterConfig_Toggle_name, RewriterConfig_Toggle_value) + proto.RegisterEnum("tensorflow.RewriterConfig_CpuLayout", RewriterConfig_CpuLayout_name, RewriterConfig_CpuLayout_value) + proto.RegisterEnum("tensorflow.RewriterConfig_NumIterationsType", RewriterConfig_NumIterationsType_name, RewriterConfig_NumIterationsType_value) + proto.RegisterEnum("tensorflow.RewriterConfig_MemOptType", RewriterConfig_MemOptType_name, RewriterConfig_MemOptType_value) + proto.RegisterType((*AutoParallelOptions)(nil), "tensorflow.AutoParallelOptions") + proto.RegisterType((*ScopedAllocatorOptions)(nil), "tensorflow.ScopedAllocatorOptions") + proto.RegisterType((*RewriterConfig)(nil), "tensorflow.RewriterConfig") + proto.RegisterType((*RewriterConfig_CustomGraphOptimizer)(nil), "tensorflow.RewriterConfig.CustomGraphOptimizer") + proto.RegisterMapType((map[string]*attr_value_go_proto.AttrValue)(nil), "tensorflow.RewriterConfig.CustomGraphOptimizer.ParameterMapEntry") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/rewriter_config.proto", fileDescriptor_1dd7de60bf190bbb) +} + +var fileDescriptor_1dd7de60bf190bbb = []byte{ + // 1400 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x97, 0x6d, 0x6e, 0xdb, 0x46, + 0x13, 0xc7, 0x43, 0x39, 0x76, 0xe2, 0xf1, 0x1b, 0xbd, 0xb2, 0x6c, 0xda, 0xc9, 0x93, 0x38, 0xc2, + 0xf3, 0x3c, 0x35, 0xda, 0xc2, 0x06, 0x9c, 0xbe, 0xa3, 0x40, 0xa1, 0xc8, 0xb2, 0x25, 0xc0, 0x22, + 0x05, 0x4a, 0xb2, 0x8b, 0x00, 0xc5, 0x82, 0xa6, 0x56, 0x32, 0x61, 0x2e, 0x77, 0xb1, 0x5c, 0x26, + 0x71, 0xcf, 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0x42, 0x10, 0xac, 0x77, 0x62, 0x3c, + 0x62, 0xe9, 0xea, 0x4f, 0xc3, 0xb8, 0x5a, 0xd2, 0xab, 0xd7, 0x7f, 0x05, 0x00, 0x00, 0xff, 0xff, + 0x69, 0x97, 0x7d, 0x09, 0xf5, 0x0d, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_model.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_model.pb.go new file mode 100644 index 0000000..f40ccd7 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_model.pb.go @@ -0,0 +1,102 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/saved_model.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// SavedModel is the high level serialization format for TensorFlow Models. +// See [todo: doc links, similar to session_bundle] for more information. +type SavedModel struct { + // The schema version of the SavedModel instance. Used for versioning when + // making future changes to the specification/implementation. Initial value + // at release will be 1. + SavedModelSchemaVersion int64 `protobuf:"varint,1,opt,name=saved_model_schema_version,json=savedModelSchemaVersion,proto3" json:"saved_model_schema_version,omitempty"` + // One or more MetaGraphs. + MetaGraphs []*MetaGraphDef `protobuf:"bytes,2,rep,name=meta_graphs,json=metaGraphs,proto3" json:"meta_graphs,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedModel) Reset() { *m = SavedModel{} } +func (m *SavedModel) String() string { return proto.CompactTextString(m) } +func (*SavedModel) ProtoMessage() {} +func (*SavedModel) Descriptor() ([]byte, []int) { + return fileDescriptor_537826d0bcc2f334, []int{0} +} + +func (m *SavedModel) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedModel.Unmarshal(m, b) +} +func (m *SavedModel) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedModel.Marshal(b, m, deterministic) +} +func (m *SavedModel) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedModel.Merge(m, src) +} +func (m *SavedModel) XXX_Size() int { + return xxx_messageInfo_SavedModel.Size(m) +} +func (m *SavedModel) XXX_DiscardUnknown() { + xxx_messageInfo_SavedModel.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedModel proto.InternalMessageInfo + +func (m *SavedModel) GetSavedModelSchemaVersion() int64 { + if m != nil { + return m.SavedModelSchemaVersion + } + return 0 +} + +func (m *SavedModel) GetMetaGraphs() []*MetaGraphDef { + if m != nil { + return m.MetaGraphs + } + return nil +} + +func init() { + proto.RegisterType((*SavedModel)(nil), "tensorflow.SavedModel") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/saved_model.proto", fileDescriptor_537826d0bcc2f334) +} + +var fileDescriptor_537826d0bcc2f334 = []byte{ + // 241 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x74, 0x50, 0x3d, 0x4b, 0x03, 0x41, + 0x10, 0x65, 0x0d, 0x58, 0x4c, 0x1a, 0xb9, 0xc6, 0x23, 0x55, 0xb0, 0x8a, 0x16, 0x77, 0xa0, 0x95, + 0xd8, 0x05, 0xc1, 0x2a, 0x10, 0x12, 0xb4, 0xb0, 0x59, 0xf6, 0x2e, 0xb3, 0x7b, 0xc1, 0x6c, 0x26, + 0xcc, 0x6c, 0x72, 0x7f, 0xc0, 0xc2, 0x9f, 0x6c, 0x29, 0x7b, 0x87, 0xee, 0x21, 0xda, 0xbd, 0x7d, + 0x5f, 0xbc, 0x1d, 0xb8, 0x09, 0xb8, 0x17, 0x62, 0xbb, 0xa3, 0xb6, 0xac, 0x89, 0xb1, 0x3c, 0x30, + 0x05, 0xaa, 0x8e, 0xb6, 0x14, 0x73, 0xc2, 0x8d, 0xf6, 0xb4, 0xc1, 0x5d, 0xd1, 0x91, 0x19, 0x24, + 0xef, 0xe4, 0xfa, 0xdf, 0x9c, 0xc7, 0x60, 0xb4, 0x63, 0x73, 0x68, 0xfa, 0xd8, 0xd5, 0xbb, 0x02, + 0x58, 0xc7, 0xb2, 0x45, 0xec, 0xca, 0x1e, 0x60, 0x32, 0xa8, 0xd6, 0x52, 0x37, 0xe8, 0x8d, 0x3e, + 0x21, 0xcb, 0x96, 0xf6, 0xb9, 0x9a, 0xaa, 0xd9, 0x68, 0x75, 0x29, 0x3f, 0xfe, 0x75, 0xa7, 0xbf, + 0xf4, 0x72, 0x76, 0x0f, 0xe3, 0xd4, 0x2f, 0xf9, 0xd9, 0x74, 0x34, 0x1b, 0xdf, 0xe6, 0x45, 0x1a, + 0x53, 0x2c, 0x30, 0x98, 0xa7, 0xa8, 0x3e, 0xa2, 0x5d, 0x81, 0xff, 0x7e, 0xc9, 0xfc, 0x43, 0x41, + 0x4e, 0xec, 0x86, 0x5e, 0xcb, 0xc6, 0x63, 0x4b, 0xfc, 0x36, 0xbf, 0x48, 0x03, 0x97, 0x71, 0xb4, + 0x2c, 0xd5, 0xeb, 0xb3, 0xdb, 0x86, 0xe6, 0x58, 0x15, 0x35, 0xf9, 0x72, 0xf0, 0xdb, 0xbf, 0xa1, + 0xa3, 0x5f, 0x67, 0xb0, 0xc4, 0x3a, 0x32, 0xba, 0x63, 0x44, 0x3b, 0xea, 0xd1, 0xa7, 0x52, 0xd5, + 0x79, 0x87, 0xee, 0xbe, 0x02, 0x00, 0x00, 0xff, 0xff, 0x7e, 0x6d, 0x16, 0x78, 0x7d, 0x01, 0x00, + 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_object_graph.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_object_graph.pb.go new file mode 100644 index 0000000..16c76d6 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/saved_object_graph.pb.go @@ -0,0 +1,1014 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/saved_object_graph.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + variable_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/variable_go_proto" + versions_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Whether the function should be compiled by XLA. +// +// The public interface to `tf.function` uses an optional boolean to +// represent three distinct states for this field. Unfortunately, proto3 +// removes the ability to explicitly check for the presence or absence of a +// field, so we instead map to an enum. +// +// See `tf.function` for details. +type FunctionSpec_JitCompile int32 + +const ( + FunctionSpec_DEFAULT FunctionSpec_JitCompile = 0 + FunctionSpec_ON FunctionSpec_JitCompile = 1 + FunctionSpec_OFF FunctionSpec_JitCompile = 2 +) + +var FunctionSpec_JitCompile_name = map[int32]string{ + 0: "DEFAULT", + 1: "ON", + 2: "OFF", +} + +var FunctionSpec_JitCompile_value = map[string]int32{ + "DEFAULT": 0, + "ON": 1, + "OFF": 2, +} + +func (x FunctionSpec_JitCompile) String() string { + return proto.EnumName(FunctionSpec_JitCompile_name, int32(x)) +} + +func (FunctionSpec_JitCompile) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{9, 0} +} + +type SavedObjectGraph struct { + // Flattened list of objects in the object graph. + // + // The position of the object in this list indicates its id. + // Nodes[0] is considered the root node. + Nodes []*SavedObject `protobuf:"bytes,1,rep,name=nodes,proto3" json:"nodes,omitempty"` + // Information about captures and output structures in concrete functions. + // Referenced from SavedBareConcreteFunction and SavedFunction. + ConcreteFunctions map[string]*SavedConcreteFunction `protobuf:"bytes,2,rep,name=concrete_functions,json=concreteFunctions,proto3" json:"concrete_functions,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedObjectGraph) Reset() { *m = SavedObjectGraph{} } +func (m *SavedObjectGraph) String() string { return proto.CompactTextString(m) } +func (*SavedObjectGraph) ProtoMessage() {} +func (*SavedObjectGraph) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{0} +} + +func (m *SavedObjectGraph) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedObjectGraph.Unmarshal(m, b) +} +func (m *SavedObjectGraph) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedObjectGraph.Marshal(b, m, deterministic) +} +func (m *SavedObjectGraph) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedObjectGraph.Merge(m, src) +} +func (m *SavedObjectGraph) XXX_Size() int { + return xxx_messageInfo_SavedObjectGraph.Size(m) +} +func (m *SavedObjectGraph) XXX_DiscardUnknown() { + xxx_messageInfo_SavedObjectGraph.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedObjectGraph proto.InternalMessageInfo + +func (m *SavedObjectGraph) GetNodes() []*SavedObject { + if m != nil { + return m.Nodes + } + return nil +} + +func (m *SavedObjectGraph) GetConcreteFunctions() map[string]*SavedConcreteFunction { + if m != nil { + return m.ConcreteFunctions + } + return nil +} + +type SavedObject struct { + // Objects which this object depends on: named edges in the dependency + // graph. + // + // Note: currently only valid if kind == "user_object". + Children []*TrackableObjectGraph_TrackableObject_ObjectReference `protobuf:"bytes,1,rep,name=children,proto3" json:"children,omitempty"` + // Slot variables owned by this object. This describes the three-way + // (optimizer, variable, slot variable) relationship; none of the three + // depend on the others directly. + // + // Note: currently only valid if kind == "user_object". + SlotVariables []*TrackableObjectGraph_TrackableObject_SlotVariableReference `protobuf:"bytes,3,rep,name=slot_variables,json=slotVariables,proto3" json:"slot_variables,omitempty"` + // Types that are valid to be assigned to Kind: + // *SavedObject_UserObject + // *SavedObject_Asset + // *SavedObject_Function + // *SavedObject_Variable + // *SavedObject_BareConcreteFunction + // *SavedObject_Constant + // *SavedObject_Resource + Kind isSavedObject_Kind `protobuf_oneof:"kind"` + SaveableObjects map[string]*SaveableObject `protobuf:"bytes,11,rep,name=saveable_objects,json=saveableObjects,proto3" json:"saveable_objects,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedObject) Reset() { *m = SavedObject{} } +func (m *SavedObject) String() string { return proto.CompactTextString(m) } +func (*SavedObject) ProtoMessage() {} +func (*SavedObject) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{1} +} + +func (m *SavedObject) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedObject.Unmarshal(m, b) +} +func (m *SavedObject) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedObject.Marshal(b, m, deterministic) +} +func (m *SavedObject) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedObject.Merge(m, src) +} +func (m *SavedObject) XXX_Size() int { + return xxx_messageInfo_SavedObject.Size(m) +} +func (m *SavedObject) XXX_DiscardUnknown() { + xxx_messageInfo_SavedObject.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedObject proto.InternalMessageInfo + +func (m *SavedObject) GetChildren() []*TrackableObjectGraph_TrackableObject_ObjectReference { + if m != nil { + return m.Children + } + return nil +} + +func (m *SavedObject) GetSlotVariables() []*TrackableObjectGraph_TrackableObject_SlotVariableReference { + if m != nil { + return m.SlotVariables + } + return nil +} + +type isSavedObject_Kind interface { + isSavedObject_Kind() +} + +type SavedObject_UserObject struct { + UserObject *SavedUserObject `protobuf:"bytes,4,opt,name=user_object,json=userObject,proto3,oneof"` +} + +type SavedObject_Asset struct { + Asset *SavedAsset `protobuf:"bytes,5,opt,name=asset,proto3,oneof"` +} + +type SavedObject_Function struct { + Function *SavedFunction `protobuf:"bytes,6,opt,name=function,proto3,oneof"` +} + +type SavedObject_Variable struct { + Variable *SavedVariable `protobuf:"bytes,7,opt,name=variable,proto3,oneof"` +} + +type SavedObject_BareConcreteFunction struct { + BareConcreteFunction *SavedBareConcreteFunction `protobuf:"bytes,8,opt,name=bare_concrete_function,json=bareConcreteFunction,proto3,oneof"` +} + +type SavedObject_Constant struct { + Constant *SavedConstant `protobuf:"bytes,9,opt,name=constant,proto3,oneof"` +} + +type SavedObject_Resource struct { + Resource *SavedResource `protobuf:"bytes,10,opt,name=resource,proto3,oneof"` +} + +func (*SavedObject_UserObject) isSavedObject_Kind() {} + +func (*SavedObject_Asset) isSavedObject_Kind() {} + +func (*SavedObject_Function) isSavedObject_Kind() {} + +func (*SavedObject_Variable) isSavedObject_Kind() {} + +func (*SavedObject_BareConcreteFunction) isSavedObject_Kind() {} + +func (*SavedObject_Constant) isSavedObject_Kind() {} + +func (*SavedObject_Resource) isSavedObject_Kind() {} + +func (m *SavedObject) GetKind() isSavedObject_Kind { + if m != nil { + return m.Kind + } + return nil +} + +func (m *SavedObject) GetUserObject() *SavedUserObject { + if x, ok := m.GetKind().(*SavedObject_UserObject); ok { + return x.UserObject + } + return nil +} + +func (m *SavedObject) GetAsset() *SavedAsset { + if x, ok := m.GetKind().(*SavedObject_Asset); ok { + return x.Asset + } + return nil +} + +func (m *SavedObject) GetFunction() *SavedFunction { + if x, ok := m.GetKind().(*SavedObject_Function); ok { + return x.Function + } + return nil +} + +func (m *SavedObject) GetVariable() *SavedVariable { + if x, ok := m.GetKind().(*SavedObject_Variable); ok { + return x.Variable + } + return nil +} + +func (m *SavedObject) GetBareConcreteFunction() *SavedBareConcreteFunction { + if x, ok := m.GetKind().(*SavedObject_BareConcreteFunction); ok { + return x.BareConcreteFunction + } + return nil +} + +func (m *SavedObject) GetConstant() *SavedConstant { + if x, ok := m.GetKind().(*SavedObject_Constant); ok { + return x.Constant + } + return nil +} + +func (m *SavedObject) GetResource() *SavedResource { + if x, ok := m.GetKind().(*SavedObject_Resource); ok { + return x.Resource + } + return nil +} + +func (m *SavedObject) GetSaveableObjects() map[string]*SaveableObject { + if m != nil { + return m.SaveableObjects + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*SavedObject) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*SavedObject_UserObject)(nil), + (*SavedObject_Asset)(nil), + (*SavedObject_Function)(nil), + (*SavedObject_Variable)(nil), + (*SavedObject_BareConcreteFunction)(nil), + (*SavedObject_Constant)(nil), + (*SavedObject_Resource)(nil), + } +} + +// A SavedUserObject is an object (in the object-oriented language of the +// TensorFlow program) of some user- or framework-defined class other than +// those handled specifically by the other kinds of SavedObjects. +// +// This object cannot be evaluated as a tensor, and therefore cannot be bound +// to an input of a function. +type SavedUserObject struct { + // Corresponds to a registration of the type to use in the loading program. + Identifier string `protobuf:"bytes,1,opt,name=identifier,proto3" json:"identifier,omitempty"` + // Version information from the producer of this SavedUserObject. + Version *versions_go_proto.VersionDef `protobuf:"bytes,2,opt,name=version,proto3" json:"version,omitempty"` + // Deprecated! At the time of deprecation, Keras was the only user of this + // field, and its saving and loading code will be updated shortly. + // Please save your application-specific metadata to separate file + // Initialization-related metadata. + Metadata string `protobuf:"bytes,3,opt,name=metadata,proto3" json:"metadata,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedUserObject) Reset() { *m = SavedUserObject{} } +func (m *SavedUserObject) String() string { return proto.CompactTextString(m) } +func (*SavedUserObject) ProtoMessage() {} +func (*SavedUserObject) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{2} +} + +func (m *SavedUserObject) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedUserObject.Unmarshal(m, b) +} +func (m *SavedUserObject) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedUserObject.Marshal(b, m, deterministic) +} +func (m *SavedUserObject) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedUserObject.Merge(m, src) +} +func (m *SavedUserObject) XXX_Size() int { + return xxx_messageInfo_SavedUserObject.Size(m) +} +func (m *SavedUserObject) XXX_DiscardUnknown() { + xxx_messageInfo_SavedUserObject.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedUserObject proto.InternalMessageInfo + +func (m *SavedUserObject) GetIdentifier() string { + if m != nil { + return m.Identifier + } + return "" +} + +func (m *SavedUserObject) GetVersion() *versions_go_proto.VersionDef { + if m != nil { + return m.Version + } + return nil +} + +func (m *SavedUserObject) GetMetadata() string { + if m != nil { + return m.Metadata + } + return "" +} + +// A SavedAsset points to an asset in the MetaGraph. +// +// When bound to a function this object evaluates to a tensor with the absolute +// filename. Users should not depend on a particular part of the filename to +// remain stable (e.g. basename could be changed). +type SavedAsset struct { + // Index into `MetaGraphDef.asset_file_def[]` that describes the Asset. + // + // Only the field `AssetFileDef.filename` is used. Other fields, such as + // `AssetFileDef.tensor_info`, MUST be ignored. + AssetFileDefIndex int32 `protobuf:"varint,1,opt,name=asset_file_def_index,json=assetFileDefIndex,proto3" json:"asset_file_def_index,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedAsset) Reset() { *m = SavedAsset{} } +func (m *SavedAsset) String() string { return proto.CompactTextString(m) } +func (*SavedAsset) ProtoMessage() {} +func (*SavedAsset) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{3} +} + +func (m *SavedAsset) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedAsset.Unmarshal(m, b) +} +func (m *SavedAsset) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedAsset.Marshal(b, m, deterministic) +} +func (m *SavedAsset) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedAsset.Merge(m, src) +} +func (m *SavedAsset) XXX_Size() int { + return xxx_messageInfo_SavedAsset.Size(m) +} +func (m *SavedAsset) XXX_DiscardUnknown() { + xxx_messageInfo_SavedAsset.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedAsset proto.InternalMessageInfo + +func (m *SavedAsset) GetAssetFileDefIndex() int32 { + if m != nil { + return m.AssetFileDefIndex + } + return 0 +} + +// A function with multiple signatures, possibly with non-Tensor arguments. +type SavedFunction struct { + ConcreteFunctions []string `protobuf:"bytes,1,rep,name=concrete_functions,json=concreteFunctions,proto3" json:"concrete_functions,omitempty"` + FunctionSpec *FunctionSpec `protobuf:"bytes,2,opt,name=function_spec,json=functionSpec,proto3" json:"function_spec,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedFunction) Reset() { *m = SavedFunction{} } +func (m *SavedFunction) String() string { return proto.CompactTextString(m) } +func (*SavedFunction) ProtoMessage() {} +func (*SavedFunction) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{4} +} + +func (m *SavedFunction) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedFunction.Unmarshal(m, b) +} +func (m *SavedFunction) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedFunction.Marshal(b, m, deterministic) +} +func (m *SavedFunction) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedFunction.Merge(m, src) +} +func (m *SavedFunction) XXX_Size() int { + return xxx_messageInfo_SavedFunction.Size(m) +} +func (m *SavedFunction) XXX_DiscardUnknown() { + xxx_messageInfo_SavedFunction.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedFunction proto.InternalMessageInfo + +func (m *SavedFunction) GetConcreteFunctions() []string { + if m != nil { + return m.ConcreteFunctions + } + return nil +} + +func (m *SavedFunction) GetFunctionSpec() *FunctionSpec { + if m != nil { + return m.FunctionSpec + } + return nil +} + +// Stores low-level information about a concrete function. Referenced in either +// a SavedFunction or a SavedBareConcreteFunction. +type SavedConcreteFunction struct { + // Bound inputs to the function. The SavedObjects identified by the node ids + // given here are appended as extra inputs to the caller-supplied inputs. + // The only types of SavedObjects valid here are SavedVariable, SavedResource + // and SavedAsset. + BoundInputs []int32 `protobuf:"varint,2,rep,packed,name=bound_inputs,json=boundInputs,proto3" json:"bound_inputs,omitempty"` + // Input in canonicalized form that was received to create this concrete + // function. + CanonicalizedInputSignature *StructuredValue `protobuf:"bytes,3,opt,name=canonicalized_input_signature,json=canonicalizedInputSignature,proto3" json:"canonicalized_input_signature,omitempty"` + // Output that was the return value of this function after replacing all + // Tensors with TensorSpecs. This can be an arbitrary nested function and will + // be used to reconstruct the full structure from pure tensors. + OutputSignature *StructuredValue `protobuf:"bytes,4,opt,name=output_signature,json=outputSignature,proto3" json:"output_signature,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedConcreteFunction) Reset() { *m = SavedConcreteFunction{} } +func (m *SavedConcreteFunction) String() string { return proto.CompactTextString(m) } +func (*SavedConcreteFunction) ProtoMessage() {} +func (*SavedConcreteFunction) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{5} +} + +func (m *SavedConcreteFunction) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedConcreteFunction.Unmarshal(m, b) +} +func (m *SavedConcreteFunction) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedConcreteFunction.Marshal(b, m, deterministic) +} +func (m *SavedConcreteFunction) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedConcreteFunction.Merge(m, src) +} +func (m *SavedConcreteFunction) XXX_Size() int { + return xxx_messageInfo_SavedConcreteFunction.Size(m) +} +func (m *SavedConcreteFunction) XXX_DiscardUnknown() { + xxx_messageInfo_SavedConcreteFunction.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedConcreteFunction proto.InternalMessageInfo + +func (m *SavedConcreteFunction) GetBoundInputs() []int32 { + if m != nil { + return m.BoundInputs + } + return nil +} + +func (m *SavedConcreteFunction) GetCanonicalizedInputSignature() *StructuredValue { + if m != nil { + return m.CanonicalizedInputSignature + } + return nil +} + +func (m *SavedConcreteFunction) GetOutputSignature() *StructuredValue { + if m != nil { + return m.OutputSignature + } + return nil +} + +type SavedBareConcreteFunction struct { + // Identifies a SavedConcreteFunction. + ConcreteFunctionName string `protobuf:"bytes,1,opt,name=concrete_function_name,json=concreteFunctionName,proto3" json:"concrete_function_name,omitempty"` + // A sequence of unique strings, one per Tensor argument. + ArgumentKeywords []string `protobuf:"bytes,2,rep,name=argument_keywords,json=argumentKeywords,proto3" json:"argument_keywords,omitempty"` + // The prefix of `argument_keywords` which may be identified by position. + AllowedPositionalArguments int64 `protobuf:"varint,3,opt,name=allowed_positional_arguments,json=allowedPositionalArguments,proto3" json:"allowed_positional_arguments,omitempty"` + // The spec of the function that this ConcreteFunction is traced from. This + // allows the ConcreteFunction to be called with nest structure inputs. This + // field may not be populated. If this field is absent, the concrete function + // can only be called with flat inputs. + // TODO(b/169361281): support calling saved ConcreteFunction with structured + // inputs in C++ SavedModel API. + FunctionSpec *FunctionSpec `protobuf:"bytes,4,opt,name=function_spec,json=functionSpec,proto3" json:"function_spec,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedBareConcreteFunction) Reset() { *m = SavedBareConcreteFunction{} } +func (m *SavedBareConcreteFunction) String() string { return proto.CompactTextString(m) } +func (*SavedBareConcreteFunction) ProtoMessage() {} +func (*SavedBareConcreteFunction) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{6} +} + +func (m *SavedBareConcreteFunction) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedBareConcreteFunction.Unmarshal(m, b) +} +func (m *SavedBareConcreteFunction) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedBareConcreteFunction.Marshal(b, m, deterministic) +} +func (m *SavedBareConcreteFunction) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedBareConcreteFunction.Merge(m, src) +} +func (m *SavedBareConcreteFunction) XXX_Size() int { + return xxx_messageInfo_SavedBareConcreteFunction.Size(m) +} +func (m *SavedBareConcreteFunction) XXX_DiscardUnknown() { + xxx_messageInfo_SavedBareConcreteFunction.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedBareConcreteFunction proto.InternalMessageInfo + +func (m *SavedBareConcreteFunction) GetConcreteFunctionName() string { + if m != nil { + return m.ConcreteFunctionName + } + return "" +} + +func (m *SavedBareConcreteFunction) GetArgumentKeywords() []string { + if m != nil { + return m.ArgumentKeywords + } + return nil +} + +func (m *SavedBareConcreteFunction) GetAllowedPositionalArguments() int64 { + if m != nil { + return m.AllowedPositionalArguments + } + return 0 +} + +func (m *SavedBareConcreteFunction) GetFunctionSpec() *FunctionSpec { + if m != nil { + return m.FunctionSpec + } + return nil +} + +type SavedConstant struct { + // An Operation name for a ConstantOp in this SavedObjectGraph's MetaGraph. + Operation string `protobuf:"bytes,1,opt,name=operation,proto3" json:"operation,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedConstant) Reset() { *m = SavedConstant{} } +func (m *SavedConstant) String() string { return proto.CompactTextString(m) } +func (*SavedConstant) ProtoMessage() {} +func (*SavedConstant) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{7} +} + +func (m *SavedConstant) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedConstant.Unmarshal(m, b) +} +func (m *SavedConstant) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedConstant.Marshal(b, m, deterministic) +} +func (m *SavedConstant) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedConstant.Merge(m, src) +} +func (m *SavedConstant) XXX_Size() int { + return xxx_messageInfo_SavedConstant.Size(m) +} +func (m *SavedConstant) XXX_DiscardUnknown() { + xxx_messageInfo_SavedConstant.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedConstant proto.InternalMessageInfo + +func (m *SavedConstant) GetOperation() string { + if m != nil { + return m.Operation + } + return "" +} + +// Represents a Variable that is initialized by loading the contents from the +// checkpoint. +type SavedVariable struct { + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + Trainable bool `protobuf:"varint,3,opt,name=trainable,proto3" json:"trainable,omitempty"` + Synchronization variable_go_proto.VariableSynchronization `protobuf:"varint,4,opt,name=synchronization,proto3,enum=tensorflow.VariableSynchronization" json:"synchronization,omitempty"` + Aggregation variable_go_proto.VariableAggregation `protobuf:"varint,5,opt,name=aggregation,proto3,enum=tensorflow.VariableAggregation" json:"aggregation,omitempty"` + Name string `protobuf:"bytes,6,opt,name=name,proto3" json:"name,omitempty"` + Device string `protobuf:"bytes,7,opt,name=device,proto3" json:"device,omitempty"` + // List of component variables for a distributed variable. + // + // When this field is non-empty, the SavedVariable will be assumed + // to be a distributed variable defined by the components listed here. + // + // This is only supported by experimental loaders at the moment. + ExperimentalDistributedVariableComponents []*SavedVariable `protobuf:"bytes,8,rep,name=experimental_distributed_variable_components,json=experimentalDistributedVariableComponents,proto3" json:"experimental_distributed_variable_components,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedVariable) Reset() { *m = SavedVariable{} } +func (m *SavedVariable) String() string { return proto.CompactTextString(m) } +func (*SavedVariable) ProtoMessage() {} +func (*SavedVariable) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{8} +} + +func (m *SavedVariable) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedVariable.Unmarshal(m, b) +} +func (m *SavedVariable) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedVariable.Marshal(b, m, deterministic) +} +func (m *SavedVariable) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedVariable.Merge(m, src) +} +func (m *SavedVariable) XXX_Size() int { + return xxx_messageInfo_SavedVariable.Size(m) +} +func (m *SavedVariable) XXX_DiscardUnknown() { + xxx_messageInfo_SavedVariable.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedVariable proto.InternalMessageInfo + +func (m *SavedVariable) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *SavedVariable) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *SavedVariable) GetTrainable() bool { + if m != nil { + return m.Trainable + } + return false +} + +func (m *SavedVariable) GetSynchronization() variable_go_proto.VariableSynchronization { + if m != nil { + return m.Synchronization + } + return variable_go_proto.VariableSynchronization_VARIABLE_SYNCHRONIZATION_AUTO +} + +func (m *SavedVariable) GetAggregation() variable_go_proto.VariableAggregation { + if m != nil { + return m.Aggregation + } + return variable_go_proto.VariableAggregation_VARIABLE_AGGREGATION_NONE +} + +func (m *SavedVariable) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *SavedVariable) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *SavedVariable) GetExperimentalDistributedVariableComponents() []*SavedVariable { + if m != nil { + return m.ExperimentalDistributedVariableComponents + } + return nil +} + +// Represents `FunctionSpec` used in `Function`. This represents a +// function that has been wrapped as a TensorFlow `Function`. +type FunctionSpec struct { + // Full arg spec from inspect.getfullargspec(). + Fullargspec *StructuredValue `protobuf:"bytes,1,opt,name=fullargspec,proto3" json:"fullargspec,omitempty"` + // Whether this represents a class method. + IsMethod bool `protobuf:"varint,2,opt,name=is_method,json=isMethod,proto3" json:"is_method,omitempty"` + // The input signature, if specified. + InputSignature *StructuredValue `protobuf:"bytes,5,opt,name=input_signature,json=inputSignature,proto3" json:"input_signature,omitempty"` + JitCompile FunctionSpec_JitCompile `protobuf:"varint,6,opt,name=jit_compile,json=jitCompile,proto3,enum=tensorflow.FunctionSpec_JitCompile" json:"jit_compile,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *FunctionSpec) Reset() { *m = FunctionSpec{} } +func (m *FunctionSpec) String() string { return proto.CompactTextString(m) } +func (*FunctionSpec) ProtoMessage() {} +func (*FunctionSpec) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{9} +} + +func (m *FunctionSpec) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_FunctionSpec.Unmarshal(m, b) +} +func (m *FunctionSpec) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_FunctionSpec.Marshal(b, m, deterministic) +} +func (m *FunctionSpec) XXX_Merge(src proto.Message) { + xxx_messageInfo_FunctionSpec.Merge(m, src) +} +func (m *FunctionSpec) XXX_Size() int { + return xxx_messageInfo_FunctionSpec.Size(m) +} +func (m *FunctionSpec) XXX_DiscardUnknown() { + xxx_messageInfo_FunctionSpec.DiscardUnknown(m) +} + +var xxx_messageInfo_FunctionSpec proto.InternalMessageInfo + +func (m *FunctionSpec) GetFullargspec() *StructuredValue { + if m != nil { + return m.Fullargspec + } + return nil +} + +func (m *FunctionSpec) GetIsMethod() bool { + if m != nil { + return m.IsMethod + } + return false +} + +func (m *FunctionSpec) GetInputSignature() *StructuredValue { + if m != nil { + return m.InputSignature + } + return nil +} + +func (m *FunctionSpec) GetJitCompile() FunctionSpec_JitCompile { + if m != nil { + return m.JitCompile + } + return FunctionSpec_DEFAULT +} + +// A SavedResource represents a TF object that holds state during its lifetime. +// An object of this type can have a reference to a: +// create_resource() and an initialize() function. +type SavedResource struct { + // A device specification indicating a required placement for the resource + // creation function, e.g. "CPU". An empty string allows the user to select a + // device. + Device string `protobuf:"bytes,1,opt,name=device,proto3" json:"device,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SavedResource) Reset() { *m = SavedResource{} } +func (m *SavedResource) String() string { return proto.CompactTextString(m) } +func (*SavedResource) ProtoMessage() {} +func (*SavedResource) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{10} +} + +func (m *SavedResource) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SavedResource.Unmarshal(m, b) +} +func (m *SavedResource) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SavedResource.Marshal(b, m, deterministic) +} +func (m *SavedResource) XXX_Merge(src proto.Message) { + xxx_messageInfo_SavedResource.Merge(m, src) +} +func (m *SavedResource) XXX_Size() int { + return xxx_messageInfo_SavedResource.Size(m) +} +func (m *SavedResource) XXX_DiscardUnknown() { + xxx_messageInfo_SavedResource.DiscardUnknown(m) +} + +var xxx_messageInfo_SavedResource proto.InternalMessageInfo + +func (m *SavedResource) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +type SaveableObject struct { + // Node ids of concrete functions for saving and loading from a checkpoint. + SaveFunction int32 `protobuf:"varint,2,opt,name=save_function,json=saveFunction,proto3" json:"save_function,omitempty"` + RestoreFunction int32 `protobuf:"varint,3,opt,name=restore_function,json=restoreFunction,proto3" json:"restore_function,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SaveableObject) Reset() { *m = SaveableObject{} } +func (m *SaveableObject) String() string { return proto.CompactTextString(m) } +func (*SaveableObject) ProtoMessage() {} +func (*SaveableObject) Descriptor() ([]byte, []int) { + return fileDescriptor_4f63c49021beb5aa, []int{11} +} + +func (m *SaveableObject) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SaveableObject.Unmarshal(m, b) +} +func (m *SaveableObject) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SaveableObject.Marshal(b, m, deterministic) +} +func (m *SaveableObject) XXX_Merge(src proto.Message) { + xxx_messageInfo_SaveableObject.Merge(m, src) +} +func (m *SaveableObject) XXX_Size() int { + return xxx_messageInfo_SaveableObject.Size(m) +} +func (m *SaveableObject) XXX_DiscardUnknown() { + xxx_messageInfo_SaveableObject.DiscardUnknown(m) +} + +var xxx_messageInfo_SaveableObject proto.InternalMessageInfo + +func (m *SaveableObject) GetSaveFunction() int32 { + if m != nil { + return m.SaveFunction + } + return 0 +} + +func (m *SaveableObject) GetRestoreFunction() int32 { + if m != nil { + return m.RestoreFunction + } + return 0 +} + +func init() { + proto.RegisterEnum("tensorflow.FunctionSpec_JitCompile", FunctionSpec_JitCompile_name, FunctionSpec_JitCompile_value) + proto.RegisterType((*SavedObjectGraph)(nil), "tensorflow.SavedObjectGraph") + proto.RegisterMapType((map[string]*SavedConcreteFunction)(nil), "tensorflow.SavedObjectGraph.ConcreteFunctionsEntry") + proto.RegisterType((*SavedObject)(nil), "tensorflow.SavedObject") + proto.RegisterMapType((map[string]*SaveableObject)(nil), "tensorflow.SavedObject.SaveableObjectsEntry") + proto.RegisterType((*SavedUserObject)(nil), "tensorflow.SavedUserObject") + proto.RegisterType((*SavedAsset)(nil), "tensorflow.SavedAsset") + proto.RegisterType((*SavedFunction)(nil), "tensorflow.SavedFunction") + proto.RegisterType((*SavedConcreteFunction)(nil), "tensorflow.SavedConcreteFunction") + proto.RegisterType((*SavedBareConcreteFunction)(nil), "tensorflow.SavedBareConcreteFunction") + proto.RegisterType((*SavedConstant)(nil), "tensorflow.SavedConstant") + proto.RegisterType((*SavedVariable)(nil), "tensorflow.SavedVariable") + proto.RegisterType((*FunctionSpec)(nil), "tensorflow.FunctionSpec") + proto.RegisterType((*SavedResource)(nil), "tensorflow.SavedResource") + proto.RegisterType((*SaveableObject)(nil), "tensorflow.SaveableObject") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/saved_object_graph.proto", fileDescriptor_4f63c49021beb5aa) +} + +var fileDescriptor_4f63c49021beb5aa = []byte{ + // 1290 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x56, 0x7d, 0x6f, 0x1b, 0xc5, + 0x13, 0x8e, 0x5f, 0x63, 0x8f, 0xf3, 0xe2, 0xae, 0xf2, 0xcb, 0xef, 0xea, 0x16, 0x68, 0x5d, 0x55, + 0xa4, 0xa5, 0x75, 0x4a, 0x0a, 0x2a, 0x42, 0x2a, 0x6a, 0x1a, 0xd7, 0xa4, 0x81, 0xbe, 0x68, 0xd3, + 0x16, 0xa9, 0x02, 0x8e, 0xf5, 0xdd, 0xd8, 0xd9, 0xe6, 0x7c, 0x6b, 0xed, 0xee, 0x25, 0x4d, 0x25, + 0xc4, 0x37, 0xe0, 0xa3, 0xf0, 0x35, 0xf8, 0x1a, 0x48, 0x7c, 0x09, 0xfe, 0x03, 0xdd, 0xde, 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0x73, 0x03, 0xea, 0x12, 0x95, 0x16, 0x32, 0x83, 0x2b, 0x18, 0xdc, 0xba, + 0xb5, 0xa7, 0xd0, 0x87, 0xaf, 0x5f, 0xbf, 0xec, 0x73, 0x7d, 0x14, 0x75, 0x5b, 0x9e, 0x18, 0x6c, + 0x67, 0xfe, 0xc9, 0xe7, 0x2f, 0xfb, 0x62, 0xea, 0x67, 0xbd, 0x27, 0xa4, 0x1b, 0x5b, 0x5c, 0x63, + 0x51, 0x6e, 0x5f, 0x24, 0xab, 0xbf, 0x72, 0xb9, 0x6e, 0xd9, 0xac, 0xee, 0xfe, 0x13, 0x00, 0x00, + 0xff, 0xff, 0xde, 0x67, 0xf6, 0x7b, 0xfa, 0x0c, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/saver.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/saver.pb.go new file mode 100644 index 0000000..3a2cc9d --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/saver.pb.go @@ -0,0 +1,191 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/saver.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// A version number that identifies a different on-disk checkpoint format. +// Usually, each subclass of BaseSaverBuilder works with a particular +// version/format. However, it is possible that the same builder may be +// upgraded to support a newer checkpoint format in the future. +type SaverDef_CheckpointFormatVersion int32 + +const ( + // Internal legacy format. + SaverDef_LEGACY SaverDef_CheckpointFormatVersion = 0 + // Deprecated format: tf.Saver() which works with tensorflow::table::Table. + SaverDef_V1 SaverDef_CheckpointFormatVersion = 1 + // Current format: more efficient. + SaverDef_V2 SaverDef_CheckpointFormatVersion = 2 +) + +var SaverDef_CheckpointFormatVersion_name = map[int32]string{ + 0: "LEGACY", + 1: "V1", + 2: "V2", +} + +var SaverDef_CheckpointFormatVersion_value = map[string]int32{ + "LEGACY": 0, + "V1": 1, + "V2": 2, +} + +func (x SaverDef_CheckpointFormatVersion) String() string { + return proto.EnumName(SaverDef_CheckpointFormatVersion_name, int32(x)) +} + +func (SaverDef_CheckpointFormatVersion) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_5551ea1a7581c104, []int{0, 0} +} + +// Protocol buffer representing the configuration of a Saver. +type SaverDef struct { + // The name of the tensor in which to specify the filename when saving or + // restoring a model checkpoint. + FilenameTensorName string `protobuf:"bytes,1,opt,name=filename_tensor_name,json=filenameTensorName,proto3" json:"filename_tensor_name,omitempty"` + // The operation to run when saving a model checkpoint. + SaveTensorName string `protobuf:"bytes,2,opt,name=save_tensor_name,json=saveTensorName,proto3" json:"save_tensor_name,omitempty"` + // The operation to run when restoring a model checkpoint. + RestoreOpName string `protobuf:"bytes,3,opt,name=restore_op_name,json=restoreOpName,proto3" json:"restore_op_name,omitempty"` + // Maximum number of checkpoints to keep. If 0, no checkpoints are deleted. + MaxToKeep int32 `protobuf:"varint,4,opt,name=max_to_keep,json=maxToKeep,proto3" json:"max_to_keep,omitempty"` + // Shard the save files, one per device that has Variable nodes. + Sharded bool `protobuf:"varint,5,opt,name=sharded,proto3" json:"sharded,omitempty"` + // How often to keep an additional checkpoint. If not specified, only the last + // "max_to_keep" checkpoints are kept; if specified, in addition to keeping + // the last "max_to_keep" checkpoints, an additional checkpoint will be kept + // for every n hours of training. + KeepCheckpointEveryNHours float32 `protobuf:"fixed32,6,opt,name=keep_checkpoint_every_n_hours,json=keepCheckpointEveryNHours,proto3" json:"keep_checkpoint_every_n_hours,omitempty"` + Version SaverDef_CheckpointFormatVersion `protobuf:"varint,7,opt,name=version,proto3,enum=tensorflow.SaverDef_CheckpointFormatVersion" json:"version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *SaverDef) Reset() { *m = SaverDef{} } +func (m *SaverDef) String() string { return proto.CompactTextString(m) } +func (*SaverDef) ProtoMessage() {} +func (*SaverDef) Descriptor() ([]byte, []int) { + return fileDescriptor_5551ea1a7581c104, []int{0} +} + +func (m *SaverDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_SaverDef.Unmarshal(m, b) +} +func (m *SaverDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_SaverDef.Marshal(b, m, deterministic) +} +func (m *SaverDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_SaverDef.Merge(m, src) +} +func (m *SaverDef) XXX_Size() int { + return xxx_messageInfo_SaverDef.Size(m) +} +func (m *SaverDef) XXX_DiscardUnknown() { + xxx_messageInfo_SaverDef.DiscardUnknown(m) +} + +var xxx_messageInfo_SaverDef proto.InternalMessageInfo + +func (m *SaverDef) GetFilenameTensorName() string { + if m != nil { + return m.FilenameTensorName + } + return "" +} + +func (m *SaverDef) GetSaveTensorName() string { + if m != nil { + return m.SaveTensorName + } + return "" +} + +func (m *SaverDef) GetRestoreOpName() string { + if m != nil { + return m.RestoreOpName + } + return "" +} + +func (m *SaverDef) GetMaxToKeep() int32 { + if m != nil { + return m.MaxToKeep + } + return 0 +} + +func (m *SaverDef) GetSharded() bool { + if m != nil { + return m.Sharded + } + return false +} + +func (m *SaverDef) GetKeepCheckpointEveryNHours() float32 { + if m != nil { + return m.KeepCheckpointEveryNHours + } + return 0 +} + +func (m *SaverDef) GetVersion() SaverDef_CheckpointFormatVersion { + if m != nil { + return m.Version + } + return SaverDef_LEGACY +} + +func init() { + proto.RegisterEnum("tensorflow.SaverDef_CheckpointFormatVersion", SaverDef_CheckpointFormatVersion_name, SaverDef_CheckpointFormatVersion_value) + proto.RegisterType((*SaverDef)(nil), "tensorflow.SaverDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/saver.proto", fileDescriptor_5551ea1a7581c104) +} + +var fileDescriptor_5551ea1a7581c104 = []byte{ + // 371 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x74, 0x92, 0xcf, 0xab, 0xd3, 0x40, + 0x10, 0xc7, 0xdd, 0x3c, 0x5f, 0xfa, 0xba, 0xc5, 0x1a, 0x56, 0xc1, 0x78, 0x50, 0x42, 0x11, 0xc9, + 0x41, 0x12, 0xad, 0x78, 0xd7, 0xd6, 0x56, 0x41, 0xa9, 0x25, 0xd6, 0x82, 0x5e, 0x96, 0x34, 0x9d, + 0xfc, 0xa0, 0x4d, 0x26, 0xec, 0x6e, 0x6a, 0xbd, 0x78, 0xf7, 0x3f, 0xf6, 0x28, 0xbb, 0x31, 0xb6, + 0x8a, 0xef, 0x94, 0xef, 0x7c, 0xe7, 0xf3, 0xcd, 0xc0, 0xcc, 0xd2, 0x47, 0x0a, 0x2a, 0x89, 0x22, + 0xdd, 0xe3, 0xd7, 0x30, 0x41, 0x01, 0x61, 0x2d, 0x50, 0xe1, 0xa6, 0x49, 0x43, 0x19, 0x1f, 0x40, + 0x04, 0xa6, 0x64, 0xf4, 0x44, 0x8d, 0x7e, 0x5c, 0xd0, 0xab, 0x8f, 0xba, 0xf7, 0x1a, 0x52, 0xf6, + 0x94, 0xde, 0x4d, 0x8b, 0x3d, 0x54, 0x71, 0x09, 0xbc, 0x65, 0xb8, 0xd6, 0x2e, 0xf1, 0x88, 0xdf, + 0x8f, 0x58, 0xd7, 0x5b, 0x99, 0xd6, 0x22, 0x2e, 0x81, 0xf9, 0xd4, 0xd1, 0x7f, 0xfe, 0x8b, 0xb6, + 0x0c, 0x3d, 0xd4, 0xfe, 0x19, 0xf9, 0x98, 0xde, 0x16, 0x20, 0x15, 0x0a, 0xe0, 0x58, 0xb7, 0xe0, + 0x85, 0x01, 0x6f, 0xfd, 0xb6, 0x3f, 0xd4, 0x86, 0x7b, 0x48, 0x07, 0x65, 0x7c, 0xe4, 0x0a, 0xf9, + 0x0e, 0xa0, 0x76, 0x6f, 0x7a, 0xc4, 0xbf, 0x8c, 0xfa, 0x65, 0x7c, 0x5c, 0xe1, 0x3b, 0x80, 0x9a, + 0xb9, 0xb4, 0x27, 0xf3, 0x58, 0x6c, 0x61, 0xeb, 0x5e, 0x7a, 0xc4, 0xbf, 0x8a, 0xba, 0x92, 0xbd, + 0xa4, 0x0f, 0x74, 0x84, 0x27, 0x39, 0x24, 0xbb, 0x1a, 0x8b, 0x4a, 0x71, 0x38, 0x80, 0xf8, 0xc6, + 0x2b, 0x9e, 0x63, 0x23, 0xa4, 0x6b, 0x7b, 0xc4, 0xb7, 0xa2, 0xfb, 0x1a, 0x9a, 0xfe, 0x61, 0x66, + 0x1a, 0x59, 0xbc, 0xd5, 0x00, 0x9b, 0xd3, 0xde, 0x01, 0x84, 0x2c, 0xb0, 0x72, 0x7b, 0x1e, 0xf1, + 0x87, 0xe3, 0x27, 0xc1, 0x69, 0x55, 0x41, 0xb7, 0xa6, 0xe0, 0x14, 0x9e, 0xa3, 0x28, 0x63, 0xb5, + 0x6e, 0x33, 0x51, 0x17, 0x1e, 0xbd, 0xa0, 0xf7, 0xae, 0x61, 0x18, 0xa5, 0xf6, 0xfb, 0xd9, 0x9b, + 0x57, 0xd3, 0xcf, 0xce, 0x0d, 0x66, 0x53, 0x6b, 0xfd, 0xcc, 0x21, 0xe6, 0x3b, 0x76, 0xac, 0xc9, + 0x77, 0x7a, 0x07, 0x45, 0x76, 0x3e, 0xb2, 0x51, 0xc5, 0x7e, 0x32, 0x30, 0x83, 0x97, 0xfa, 0x74, + 0x72, 0x49, 0xbe, 0x7c, 0xca, 0x0a, 0x95, 0x37, 0x9b, 0x20, 0xc1, 0x32, 0x3c, 0x3b, 0xf7, 0xff, + 0x65, 0x86, 0xff, 0xbc, 0x83, 0x14, 0x05, 0xd7, 0x0e, 0x37, 0x8e, 0xe4, 0x19, 0xb6, 0xea, 0x27, + 0x21, 0x1b, 0xdb, 0xa8, 0xe7, 0xbf, 0x02, 0x00, 0x00, 0xff, 0xff, 0x0a, 0x34, 0x50, 0xe1, 0x46, + 0x02, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/struct.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/struct.pb.go new file mode 100644 index 0000000..760b644 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/struct.pb.go @@ -0,0 +1,886 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/struct.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type TypeSpecProto_TypeSpecClass int32 + +const ( + TypeSpecProto_UNKNOWN TypeSpecProto_TypeSpecClass = 0 + TypeSpecProto_SPARSE_TENSOR_SPEC TypeSpecProto_TypeSpecClass = 1 + TypeSpecProto_INDEXED_SLICES_SPEC TypeSpecProto_TypeSpecClass = 2 + TypeSpecProto_RAGGED_TENSOR_SPEC TypeSpecProto_TypeSpecClass = 3 + TypeSpecProto_TENSOR_ARRAY_SPEC TypeSpecProto_TypeSpecClass = 4 + TypeSpecProto_DATA_DATASET_SPEC TypeSpecProto_TypeSpecClass = 5 + TypeSpecProto_DATA_ITERATOR_SPEC TypeSpecProto_TypeSpecClass = 6 + TypeSpecProto_OPTIONAL_SPEC TypeSpecProto_TypeSpecClass = 7 + TypeSpecProto_PER_REPLICA_SPEC TypeSpecProto_TypeSpecClass = 8 + TypeSpecProto_VARIABLE_SPEC TypeSpecProto_TypeSpecClass = 9 + TypeSpecProto_ROW_PARTITION_SPEC TypeSpecProto_TypeSpecClass = 10 + TypeSpecProto_NDARRAY_SPEC TypeSpecProto_TypeSpecClass = 11 +) + +var TypeSpecProto_TypeSpecClass_name = map[int32]string{ + 0: "UNKNOWN", + 1: "SPARSE_TENSOR_SPEC", + 2: "INDEXED_SLICES_SPEC", + 3: "RAGGED_TENSOR_SPEC", + 4: "TENSOR_ARRAY_SPEC", + 5: "DATA_DATASET_SPEC", + 6: "DATA_ITERATOR_SPEC", + 7: "OPTIONAL_SPEC", + 8: "PER_REPLICA_SPEC", + 9: "VARIABLE_SPEC", + 10: "ROW_PARTITION_SPEC", + 11: "NDARRAY_SPEC", +} + +var TypeSpecProto_TypeSpecClass_value = map[string]int32{ + "UNKNOWN": 0, + "SPARSE_TENSOR_SPEC": 1, + "INDEXED_SLICES_SPEC": 2, + "RAGGED_TENSOR_SPEC": 3, + "TENSOR_ARRAY_SPEC": 4, + "DATA_DATASET_SPEC": 5, + "DATA_ITERATOR_SPEC": 6, + "OPTIONAL_SPEC": 7, + "PER_REPLICA_SPEC": 8, + "VARIABLE_SPEC": 9, + "ROW_PARTITION_SPEC": 10, + "NDARRAY_SPEC": 11, +} + +func (x TypeSpecProto_TypeSpecClass) String() string { + return proto.EnumName(TypeSpecProto_TypeSpecClass_name, int32(x)) +} + +func (TypeSpecProto_TypeSpecClass) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{9, 0} +} + +// `StructuredValue` represents a dynamically typed value representing various +// data structures that are inspired by Python data structures typically used in +// TensorFlow functions as inputs and outputs. +// +// For example when saving a Layer there may be a `training` argument. If the +// user passes a boolean True/False, that switches between two concrete +// TensorFlow functions. In order to switch between them in the same way after +// loading the SavedModel, we need to represent "True" and "False". +// +// A more advanced example might be a function which takes a list of +// dictionaries mapping from strings to Tensors. In order to map from +// user-specified arguments `[{"a": tf.constant(1.)}, {"q": tf.constant(3.)}]` +// after load to the right saved TensorFlow function, we need to represent the +// nested structure and the strings, recording that we have a trace for anything +// matching `[{"a": tf.TensorSpec(None, tf.float32)}, {"q": tf.TensorSpec([], +// tf.float64)}]` as an example. +// +// Likewise functions may return nested structures of Tensors, for example +// returning a dictionary mapping from strings to Tensors. In order for the +// loaded function to return the same structure we need to serialize it. +// +// This is an ergonomic aid for working with loaded SavedModels, not a promise +// to serialize all possible function signatures. For example we do not expect +// to pickle generic Python objects, and ideally we'd stay language-agnostic. +type StructuredValue struct { + // The kind of value. + // + // Types that are valid to be assigned to Kind: + // *StructuredValue_NoneValue + // *StructuredValue_Float64Value + // *StructuredValue_Int64Value + // *StructuredValue_StringValue + // *StructuredValue_BoolValue + // *StructuredValue_TensorShapeValue + // *StructuredValue_TensorDtypeValue + // *StructuredValue_TensorSpecValue + // *StructuredValue_TypeSpecValue + // *StructuredValue_BoundedTensorSpecValue + // *StructuredValue_ListValue + // *StructuredValue_TupleValue + // *StructuredValue_DictValue + // *StructuredValue_NamedTupleValue + Kind isStructuredValue_Kind `protobuf_oneof:"kind"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *StructuredValue) Reset() { *m = StructuredValue{} } +func (m *StructuredValue) String() string { return proto.CompactTextString(m) } +func (*StructuredValue) ProtoMessage() {} +func (*StructuredValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{0} +} + +func (m *StructuredValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_StructuredValue.Unmarshal(m, b) +} +func (m *StructuredValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_StructuredValue.Marshal(b, m, deterministic) +} +func (m *StructuredValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_StructuredValue.Merge(m, src) +} +func (m *StructuredValue) XXX_Size() int { + return xxx_messageInfo_StructuredValue.Size(m) +} +func (m *StructuredValue) XXX_DiscardUnknown() { + xxx_messageInfo_StructuredValue.DiscardUnknown(m) +} + +var xxx_messageInfo_StructuredValue proto.InternalMessageInfo + +type isStructuredValue_Kind interface { + isStructuredValue_Kind() +} + +type StructuredValue_NoneValue struct { + NoneValue *NoneValue `protobuf:"bytes,1,opt,name=none_value,json=noneValue,proto3,oneof"` +} + +type StructuredValue_Float64Value struct { + Float64Value float64 `protobuf:"fixed64,11,opt,name=float64_value,json=float64Value,proto3,oneof"` +} + +type StructuredValue_Int64Value struct { + Int64Value int64 `protobuf:"zigzag64,12,opt,name=int64_value,json=int64Value,proto3,oneof"` +} + +type StructuredValue_StringValue struct { + StringValue string `protobuf:"bytes,13,opt,name=string_value,json=stringValue,proto3,oneof"` +} + +type StructuredValue_BoolValue struct { + BoolValue bool `protobuf:"varint,14,opt,name=bool_value,json=boolValue,proto3,oneof"` +} + +type StructuredValue_TensorShapeValue struct { + TensorShapeValue *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,31,opt,name=tensor_shape_value,json=tensorShapeValue,proto3,oneof"` +} + +type StructuredValue_TensorDtypeValue struct { + TensorDtypeValue types_go_proto.DataType `protobuf:"varint,32,opt,name=tensor_dtype_value,json=tensorDtypeValue,proto3,enum=tensorflow.DataType,oneof"` +} + +type StructuredValue_TensorSpecValue struct { + TensorSpecValue *TensorSpecProto `protobuf:"bytes,33,opt,name=tensor_spec_value,json=tensorSpecValue,proto3,oneof"` +} + +type StructuredValue_TypeSpecValue struct { + TypeSpecValue *TypeSpecProto `protobuf:"bytes,34,opt,name=type_spec_value,json=typeSpecValue,proto3,oneof"` +} + +type StructuredValue_BoundedTensorSpecValue struct { + BoundedTensorSpecValue *BoundedTensorSpecProto `protobuf:"bytes,35,opt,name=bounded_tensor_spec_value,json=boundedTensorSpecValue,proto3,oneof"` +} + +type StructuredValue_ListValue struct { + ListValue *ListValue `protobuf:"bytes,51,opt,name=list_value,json=listValue,proto3,oneof"` +} + +type StructuredValue_TupleValue struct { + TupleValue *TupleValue `protobuf:"bytes,52,opt,name=tuple_value,json=tupleValue,proto3,oneof"` +} + +type StructuredValue_DictValue struct { + DictValue *DictValue `protobuf:"bytes,53,opt,name=dict_value,json=dictValue,proto3,oneof"` +} + +type StructuredValue_NamedTupleValue struct { + NamedTupleValue *NamedTupleValue `protobuf:"bytes,54,opt,name=named_tuple_value,json=namedTupleValue,proto3,oneof"` +} + +func (*StructuredValue_NoneValue) isStructuredValue_Kind() {} + +func (*StructuredValue_Float64Value) isStructuredValue_Kind() {} + +func (*StructuredValue_Int64Value) isStructuredValue_Kind() {} + +func (*StructuredValue_StringValue) isStructuredValue_Kind() {} + +func (*StructuredValue_BoolValue) isStructuredValue_Kind() {} + +func (*StructuredValue_TensorShapeValue) isStructuredValue_Kind() {} + +func (*StructuredValue_TensorDtypeValue) isStructuredValue_Kind() {} + +func (*StructuredValue_TensorSpecValue) isStructuredValue_Kind() {} + +func (*StructuredValue_TypeSpecValue) isStructuredValue_Kind() {} + +func (*StructuredValue_BoundedTensorSpecValue) isStructuredValue_Kind() {} + +func (*StructuredValue_ListValue) isStructuredValue_Kind() {} + +func (*StructuredValue_TupleValue) isStructuredValue_Kind() {} + +func (*StructuredValue_DictValue) isStructuredValue_Kind() {} + +func (*StructuredValue_NamedTupleValue) isStructuredValue_Kind() {} + +func (m *StructuredValue) GetKind() isStructuredValue_Kind { + if m != nil { + return m.Kind + } + return nil +} + +func (m *StructuredValue) GetNoneValue() *NoneValue { + if x, ok := m.GetKind().(*StructuredValue_NoneValue); ok { + return x.NoneValue + } + return nil +} + +func (m *StructuredValue) GetFloat64Value() float64 { + if x, ok := m.GetKind().(*StructuredValue_Float64Value); ok { + return x.Float64Value + } + return 0 +} + +func (m *StructuredValue) GetInt64Value() int64 { + if x, ok := m.GetKind().(*StructuredValue_Int64Value); ok { + return x.Int64Value + } + return 0 +} + +func (m *StructuredValue) GetStringValue() string { + if x, ok := m.GetKind().(*StructuredValue_StringValue); ok { + return x.StringValue + } + return "" +} + +func (m *StructuredValue) GetBoolValue() bool { + if x, ok := m.GetKind().(*StructuredValue_BoolValue); ok { + return x.BoolValue + } + return false +} + +func (m *StructuredValue) GetTensorShapeValue() *tensor_shape_go_proto.TensorShapeProto { + if x, ok := m.GetKind().(*StructuredValue_TensorShapeValue); ok { + return x.TensorShapeValue + } + return nil +} + +func (m *StructuredValue) GetTensorDtypeValue() types_go_proto.DataType { + if x, ok := m.GetKind().(*StructuredValue_TensorDtypeValue); ok { + return x.TensorDtypeValue + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *StructuredValue) GetTensorSpecValue() *TensorSpecProto { + if x, ok := m.GetKind().(*StructuredValue_TensorSpecValue); ok { + return x.TensorSpecValue + } + return nil +} + +func (m *StructuredValue) GetTypeSpecValue() *TypeSpecProto { + if x, ok := m.GetKind().(*StructuredValue_TypeSpecValue); ok { + return x.TypeSpecValue + } + return nil +} + +func (m *StructuredValue) GetBoundedTensorSpecValue() *BoundedTensorSpecProto { + if x, ok := m.GetKind().(*StructuredValue_BoundedTensorSpecValue); ok { + return x.BoundedTensorSpecValue + } + return nil +} + +func (m *StructuredValue) GetListValue() *ListValue { + if x, ok := m.GetKind().(*StructuredValue_ListValue); ok { + return x.ListValue + } + return nil +} + +func (m *StructuredValue) GetTupleValue() *TupleValue { + if x, ok := m.GetKind().(*StructuredValue_TupleValue); ok { + return x.TupleValue + } + return nil +} + +func (m *StructuredValue) GetDictValue() *DictValue { + if x, ok := m.GetKind().(*StructuredValue_DictValue); ok { + return x.DictValue + } + return nil +} + +func (m *StructuredValue) GetNamedTupleValue() *NamedTupleValue { + if x, ok := m.GetKind().(*StructuredValue_NamedTupleValue); ok { + return x.NamedTupleValue + } + return nil +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*StructuredValue) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*StructuredValue_NoneValue)(nil), + (*StructuredValue_Float64Value)(nil), + (*StructuredValue_Int64Value)(nil), + (*StructuredValue_StringValue)(nil), + (*StructuredValue_BoolValue)(nil), + (*StructuredValue_TensorShapeValue)(nil), + (*StructuredValue_TensorDtypeValue)(nil), + (*StructuredValue_TensorSpecValue)(nil), + (*StructuredValue_TypeSpecValue)(nil), + (*StructuredValue_BoundedTensorSpecValue)(nil), + (*StructuredValue_ListValue)(nil), + (*StructuredValue_TupleValue)(nil), + (*StructuredValue_DictValue)(nil), + (*StructuredValue_NamedTupleValue)(nil), + } +} + +// Represents None. +type NoneValue struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NoneValue) Reset() { *m = NoneValue{} } +func (m *NoneValue) String() string { return proto.CompactTextString(m) } +func (*NoneValue) ProtoMessage() {} +func (*NoneValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{1} +} + +func (m *NoneValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NoneValue.Unmarshal(m, b) +} +func (m *NoneValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NoneValue.Marshal(b, m, deterministic) +} +func (m *NoneValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_NoneValue.Merge(m, src) +} +func (m *NoneValue) XXX_Size() int { + return xxx_messageInfo_NoneValue.Size(m) +} +func (m *NoneValue) XXX_DiscardUnknown() { + xxx_messageInfo_NoneValue.DiscardUnknown(m) +} + +var xxx_messageInfo_NoneValue proto.InternalMessageInfo + +// Represents a Python list. +type ListValue struct { + Values []*StructuredValue `protobuf:"bytes,1,rep,name=values,proto3" json:"values,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ListValue) Reset() { *m = ListValue{} } +func (m *ListValue) String() string { return proto.CompactTextString(m) } +func (*ListValue) ProtoMessage() {} +func (*ListValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{2} +} + +func (m *ListValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ListValue.Unmarshal(m, b) +} +func (m *ListValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ListValue.Marshal(b, m, deterministic) +} +func (m *ListValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_ListValue.Merge(m, src) +} +func (m *ListValue) XXX_Size() int { + return xxx_messageInfo_ListValue.Size(m) +} +func (m *ListValue) XXX_DiscardUnknown() { + xxx_messageInfo_ListValue.DiscardUnknown(m) +} + +var xxx_messageInfo_ListValue proto.InternalMessageInfo + +func (m *ListValue) GetValues() []*StructuredValue { + if m != nil { + return m.Values + } + return nil +} + +// Represents a Python tuple. +type TupleValue struct { + Values []*StructuredValue `protobuf:"bytes,1,rep,name=values,proto3" json:"values,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TupleValue) Reset() { *m = TupleValue{} } +func (m *TupleValue) String() string { return proto.CompactTextString(m) } +func (*TupleValue) ProtoMessage() {} +func (*TupleValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{3} +} + +func (m *TupleValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TupleValue.Unmarshal(m, b) +} +func (m *TupleValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TupleValue.Marshal(b, m, deterministic) +} +func (m *TupleValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_TupleValue.Merge(m, src) +} +func (m *TupleValue) XXX_Size() int { + return xxx_messageInfo_TupleValue.Size(m) +} +func (m *TupleValue) XXX_DiscardUnknown() { + xxx_messageInfo_TupleValue.DiscardUnknown(m) +} + +var xxx_messageInfo_TupleValue proto.InternalMessageInfo + +func (m *TupleValue) GetValues() []*StructuredValue { + if m != nil { + return m.Values + } + return nil +} + +// Represents a Python dict keyed by `str`. +// The comment on Unicode from Value.string_value applies analogously. +type DictValue struct { + Fields map[string]*StructuredValue `protobuf:"bytes,1,rep,name=fields,proto3" json:"fields,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DictValue) Reset() { *m = DictValue{} } +func (m *DictValue) String() string { return proto.CompactTextString(m) } +func (*DictValue) ProtoMessage() {} +func (*DictValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{4} +} + +func (m *DictValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DictValue.Unmarshal(m, b) +} +func (m *DictValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DictValue.Marshal(b, m, deterministic) +} +func (m *DictValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_DictValue.Merge(m, src) +} +func (m *DictValue) XXX_Size() int { + return xxx_messageInfo_DictValue.Size(m) +} +func (m *DictValue) XXX_DiscardUnknown() { + xxx_messageInfo_DictValue.DiscardUnknown(m) +} + +var xxx_messageInfo_DictValue proto.InternalMessageInfo + +func (m *DictValue) GetFields() map[string]*StructuredValue { + if m != nil { + return m.Fields + } + return nil +} + +// Represents a (key, value) pair. +type PairValue struct { + Key string `protobuf:"bytes,1,opt,name=key,proto3" json:"key,omitempty"` + Value *StructuredValue `protobuf:"bytes,2,opt,name=value,proto3" json:"value,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *PairValue) Reset() { *m = PairValue{} } +func (m *PairValue) String() string { return proto.CompactTextString(m) } +func (*PairValue) ProtoMessage() {} +func (*PairValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{5} +} + +func (m *PairValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_PairValue.Unmarshal(m, b) +} +func (m *PairValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_PairValue.Marshal(b, m, deterministic) +} +func (m *PairValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_PairValue.Merge(m, src) +} +func (m *PairValue) XXX_Size() int { + return xxx_messageInfo_PairValue.Size(m) +} +func (m *PairValue) XXX_DiscardUnknown() { + xxx_messageInfo_PairValue.DiscardUnknown(m) +} + +var xxx_messageInfo_PairValue proto.InternalMessageInfo + +func (m *PairValue) GetKey() string { + if m != nil { + return m.Key + } + return "" +} + +func (m *PairValue) GetValue() *StructuredValue { + if m != nil { + return m.Value + } + return nil +} + +// Represents Python's namedtuple. +type NamedTupleValue struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Values []*PairValue `protobuf:"bytes,2,rep,name=values,proto3" json:"values,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *NamedTupleValue) Reset() { *m = NamedTupleValue{} } +func (m *NamedTupleValue) String() string { return proto.CompactTextString(m) } +func (*NamedTupleValue) ProtoMessage() {} +func (*NamedTupleValue) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{6} +} + +func (m *NamedTupleValue) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_NamedTupleValue.Unmarshal(m, b) +} +func (m *NamedTupleValue) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_NamedTupleValue.Marshal(b, m, deterministic) +} +func (m *NamedTupleValue) XXX_Merge(src proto.Message) { + xxx_messageInfo_NamedTupleValue.Merge(m, src) +} +func (m *NamedTupleValue) XXX_Size() int { + return xxx_messageInfo_NamedTupleValue.Size(m) +} +func (m *NamedTupleValue) XXX_DiscardUnknown() { + xxx_messageInfo_NamedTupleValue.DiscardUnknown(m) +} + +var xxx_messageInfo_NamedTupleValue proto.InternalMessageInfo + +func (m *NamedTupleValue) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *NamedTupleValue) GetValues() []*PairValue { + if m != nil { + return m.Values + } + return nil +} + +// A protobuf to represent tf.TensorSpec. +type TensorSpecProto struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + Dtype types_go_proto.DataType `protobuf:"varint,3,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorSpecProto) Reset() { *m = TensorSpecProto{} } +func (m *TensorSpecProto) String() string { return proto.CompactTextString(m) } +func (*TensorSpecProto) ProtoMessage() {} +func (*TensorSpecProto) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{7} +} + +func (m *TensorSpecProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorSpecProto.Unmarshal(m, b) +} +func (m *TensorSpecProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorSpecProto.Marshal(b, m, deterministic) +} +func (m *TensorSpecProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorSpecProto.Merge(m, src) +} +func (m *TensorSpecProto) XXX_Size() int { + return xxx_messageInfo_TensorSpecProto.Size(m) +} +func (m *TensorSpecProto) XXX_DiscardUnknown() { + xxx_messageInfo_TensorSpecProto.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorSpecProto proto.InternalMessageInfo + +func (m *TensorSpecProto) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *TensorSpecProto) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *TensorSpecProto) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +// A protobuf to represent tf.BoundedTensorSpec. +type BoundedTensorSpecProto struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + Dtype types_go_proto.DataType `protobuf:"varint,3,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + Minimum *tensor_go_proto.TensorProto `protobuf:"bytes,4,opt,name=minimum,proto3" json:"minimum,omitempty"` + Maximum *tensor_go_proto.TensorProto `protobuf:"bytes,5,opt,name=maximum,proto3" json:"maximum,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *BoundedTensorSpecProto) Reset() { *m = BoundedTensorSpecProto{} } +func (m *BoundedTensorSpecProto) String() string { return proto.CompactTextString(m) } +func (*BoundedTensorSpecProto) ProtoMessage() {} +func (*BoundedTensorSpecProto) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{8} +} + +func (m *BoundedTensorSpecProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_BoundedTensorSpecProto.Unmarshal(m, b) +} +func (m *BoundedTensorSpecProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_BoundedTensorSpecProto.Marshal(b, m, deterministic) +} +func (m *BoundedTensorSpecProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_BoundedTensorSpecProto.Merge(m, src) +} +func (m *BoundedTensorSpecProto) XXX_Size() int { + return xxx_messageInfo_BoundedTensorSpecProto.Size(m) +} +func (m *BoundedTensorSpecProto) XXX_DiscardUnknown() { + xxx_messageInfo_BoundedTensorSpecProto.DiscardUnknown(m) +} + +var xxx_messageInfo_BoundedTensorSpecProto proto.InternalMessageInfo + +func (m *BoundedTensorSpecProto) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *BoundedTensorSpecProto) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *BoundedTensorSpecProto) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *BoundedTensorSpecProto) GetMinimum() *tensor_go_proto.TensorProto { + if m != nil { + return m.Minimum + } + return nil +} + +func (m *BoundedTensorSpecProto) GetMaximum() *tensor_go_proto.TensorProto { + if m != nil { + return m.Maximum + } + return nil +} + +// Represents a tf.TypeSpec +type TypeSpecProto struct { + TypeSpecClass TypeSpecProto_TypeSpecClass `protobuf:"varint,1,opt,name=type_spec_class,json=typeSpecClass,proto3,enum=tensorflow.TypeSpecProto_TypeSpecClass" json:"type_spec_class,omitempty"` + // The value returned by TypeSpec._serialize(). + TypeState *StructuredValue `protobuf:"bytes,2,opt,name=type_state,json=typeState,proto3" json:"type_state,omitempty"` + // This is currently redundant with the type_spec_class enum, and is only + // used for error reporting. In particular, if you use an older binary to + // load a newer model, and the model uses a TypeSpecClass that the older + // binary doesn't support, then this lets us display a useful error message. + TypeSpecClassName string `protobuf:"bytes,3,opt,name=type_spec_class_name,json=typeSpecClassName,proto3" json:"type_spec_class_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TypeSpecProto) Reset() { *m = TypeSpecProto{} } +func (m *TypeSpecProto) String() string { return proto.CompactTextString(m) } +func (*TypeSpecProto) ProtoMessage() {} +func (*TypeSpecProto) Descriptor() ([]byte, []int) { + return fileDescriptor_8f6f8fd91d5fa722, []int{9} +} + +func (m *TypeSpecProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TypeSpecProto.Unmarshal(m, b) +} +func (m *TypeSpecProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TypeSpecProto.Marshal(b, m, deterministic) +} +func (m *TypeSpecProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_TypeSpecProto.Merge(m, src) +} +func (m *TypeSpecProto) XXX_Size() int { + return xxx_messageInfo_TypeSpecProto.Size(m) +} +func (m *TypeSpecProto) XXX_DiscardUnknown() { + xxx_messageInfo_TypeSpecProto.DiscardUnknown(m) +} + +var xxx_messageInfo_TypeSpecProto proto.InternalMessageInfo + +func (m *TypeSpecProto) GetTypeSpecClass() TypeSpecProto_TypeSpecClass { + if m != nil { + return m.TypeSpecClass + } + return TypeSpecProto_UNKNOWN +} + +func (m *TypeSpecProto) GetTypeState() *StructuredValue { + if m != nil { + return m.TypeState + } + return nil +} + +func (m *TypeSpecProto) GetTypeSpecClassName() string { + if m != nil { + return m.TypeSpecClassName + } + return "" +} + +func init() { + proto.RegisterEnum("tensorflow.TypeSpecProto_TypeSpecClass", TypeSpecProto_TypeSpecClass_name, TypeSpecProto_TypeSpecClass_value) + proto.RegisterType((*StructuredValue)(nil), "tensorflow.StructuredValue") + proto.RegisterType((*NoneValue)(nil), "tensorflow.NoneValue") + proto.RegisterType((*ListValue)(nil), "tensorflow.ListValue") + proto.RegisterType((*TupleValue)(nil), "tensorflow.TupleValue") + proto.RegisterType((*DictValue)(nil), "tensorflow.DictValue") + proto.RegisterMapType((map[string]*StructuredValue)(nil), "tensorflow.DictValue.FieldsEntry") + proto.RegisterType((*PairValue)(nil), "tensorflow.PairValue") + proto.RegisterType((*NamedTupleValue)(nil), "tensorflow.NamedTupleValue") + proto.RegisterType((*TensorSpecProto)(nil), "tensorflow.TensorSpecProto") + proto.RegisterType((*BoundedTensorSpecProto)(nil), "tensorflow.BoundedTensorSpecProto") + proto.RegisterType((*TypeSpecProto)(nil), "tensorflow.TypeSpecProto") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/struct.proto", fileDescriptor_8f6f8fd91d5fa722) +} + +var fileDescriptor_8f6f8fd91d5fa722 = []byte{ + // 945 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 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+++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/tensor_bundle.pb.go @@ -0,0 +1,260 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/tensor_bundle.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + tensor_slice_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_slice_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + versions_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// An enum indicating the endianness of the platform that produced this +// bundle. A bundle can only be read by a platform with matching endianness. +// Defaults to LITTLE, as most modern platforms are little-endian. +// +// Affects the binary tensor data bytes only, not the metadata in protobufs. +type BundleHeaderProto_Endianness int32 + +const ( + BundleHeaderProto_LITTLE BundleHeaderProto_Endianness = 0 + BundleHeaderProto_BIG BundleHeaderProto_Endianness = 1 +) + +var BundleHeaderProto_Endianness_name = map[int32]string{ + 0: "LITTLE", + 1: "BIG", +} + +var BundleHeaderProto_Endianness_value = map[string]int32{ + "LITTLE": 0, + "BIG": 1, +} + +func (x BundleHeaderProto_Endianness) String() string { + return proto.EnumName(BundleHeaderProto_Endianness_name, int32(x)) +} + +func (BundleHeaderProto_Endianness) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_9ab648e6509929dc, []int{0, 0} +} + +// Special header that is associated with a bundle. +// +// TODO(zongheng,zhifengc): maybe in the future, we can add information about +// which binary produced this checkpoint, timestamp, etc. Sometime, these can be +// valuable debugging information. And if needed, these can be used as defensive +// information ensuring reader (binary version) of the checkpoint and the writer +// (binary version) must match within certain range, etc. +type BundleHeaderProto struct { + // Number of data files in the bundle. + NumShards int32 `protobuf:"varint,1,opt,name=num_shards,json=numShards,proto3" json:"num_shards,omitempty"` + Endianness BundleHeaderProto_Endianness `protobuf:"varint,2,opt,name=endianness,proto3,enum=tensorflow.BundleHeaderProto_Endianness" json:"endianness,omitempty"` + // Versioning of the tensor bundle format. + Version *versions_go_proto.VersionDef `protobuf:"bytes,3,opt,name=version,proto3" json:"version,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *BundleHeaderProto) Reset() { *m = BundleHeaderProto{} } +func (m *BundleHeaderProto) String() string { return proto.CompactTextString(m) } +func (*BundleHeaderProto) ProtoMessage() {} +func (*BundleHeaderProto) Descriptor() ([]byte, []int) { + return fileDescriptor_9ab648e6509929dc, []int{0} +} + +func (m *BundleHeaderProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_BundleHeaderProto.Unmarshal(m, b) +} +func (m *BundleHeaderProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_BundleHeaderProto.Marshal(b, m, deterministic) +} +func (m *BundleHeaderProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_BundleHeaderProto.Merge(m, src) +} +func (m *BundleHeaderProto) XXX_Size() int { + return xxx_messageInfo_BundleHeaderProto.Size(m) +} +func (m *BundleHeaderProto) XXX_DiscardUnknown() { + xxx_messageInfo_BundleHeaderProto.DiscardUnknown(m) +} + +var xxx_messageInfo_BundleHeaderProto proto.InternalMessageInfo + +func (m *BundleHeaderProto) GetNumShards() int32 { + if m != nil { + return m.NumShards + } + return 0 +} + +func (m *BundleHeaderProto) GetEndianness() BundleHeaderProto_Endianness { + if m != nil { + return m.Endianness + } + return BundleHeaderProto_LITTLE +} + +func (m *BundleHeaderProto) GetVersion() *versions_go_proto.VersionDef { + if m != nil { + return m.Version + } + return nil +} + +// Describes the metadata related to a checkpointed tensor. +type BundleEntryProto struct { + // The tensor dtype and shape. + Dtype types_go_proto.DataType `protobuf:"varint,1,opt,name=dtype,proto3,enum=tensorflow.DataType" json:"dtype,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,2,opt,name=shape,proto3" json:"shape,omitempty"` + // The binary content of the tensor lies in: + // File "shard_id": bytes [offset, offset + size). + ShardId int32 `protobuf:"varint,3,opt,name=shard_id,json=shardId,proto3" json:"shard_id,omitempty"` + Offset int64 `protobuf:"varint,4,opt,name=offset,proto3" json:"offset,omitempty"` + Size int64 `protobuf:"varint,5,opt,name=size,proto3" json:"size,omitempty"` + // The CRC32C checksum of the tensor bytes. + Crc32C uint32 `protobuf:"fixed32,6,opt,name=crc32c,proto3" json:"crc32c,omitempty"` + // Iff present, this entry represents a partitioned tensor. The previous + // fields are interpreted as follows: + // + // "dtype", "shape": describe the full tensor. + // "shard_id", "offset", "size", "crc32c": all IGNORED. + // These information for each slice can be looked up in their own + // BundleEntryProto, keyed by each "slice_name". + Slices []*tensor_slice_go_proto.TensorSliceProto `protobuf:"bytes,7,rep,name=slices,proto3" json:"slices,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *BundleEntryProto) Reset() { *m = BundleEntryProto{} } +func (m *BundleEntryProto) String() string { return proto.CompactTextString(m) } +func (*BundleEntryProto) ProtoMessage() {} +func (*BundleEntryProto) Descriptor() ([]byte, []int) { + return fileDescriptor_9ab648e6509929dc, []int{1} +} + +func (m *BundleEntryProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_BundleEntryProto.Unmarshal(m, b) +} +func (m *BundleEntryProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_BundleEntryProto.Marshal(b, m, deterministic) +} +func (m *BundleEntryProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_BundleEntryProto.Merge(m, src) +} +func (m *BundleEntryProto) XXX_Size() int { + return xxx_messageInfo_BundleEntryProto.Size(m) +} +func (m *BundleEntryProto) XXX_DiscardUnknown() { + xxx_messageInfo_BundleEntryProto.DiscardUnknown(m) +} + +var xxx_messageInfo_BundleEntryProto proto.InternalMessageInfo + +func (m *BundleEntryProto) GetDtype() types_go_proto.DataType { + if m != nil { + return m.Dtype + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *BundleEntryProto) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *BundleEntryProto) GetShardId() int32 { + if m != nil { + return m.ShardId + } + return 0 +} + +func (m *BundleEntryProto) GetOffset() int64 { + if m != nil { + return m.Offset + } + return 0 +} + +func (m *BundleEntryProto) GetSize() int64 { + if m != nil { + return m.Size + } + return 0 +} + +func (m *BundleEntryProto) GetCrc32C() uint32 { + if m != nil { + return m.Crc32C + } + return 0 +} + +func (m *BundleEntryProto) GetSlices() []*tensor_slice_go_proto.TensorSliceProto { + if m != nil { + return m.Slices + } + return nil +} + +func init() { + proto.RegisterEnum("tensorflow.BundleHeaderProto_Endianness", BundleHeaderProto_Endianness_name, BundleHeaderProto_Endianness_value) + proto.RegisterType((*BundleHeaderProto)(nil), "tensorflow.BundleHeaderProto") + proto.RegisterType((*BundleEntryProto)(nil), "tensorflow.BundleEntryProto") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/tensor_bundle.proto", fileDescriptor_9ab648e6509929dc) +} + +var fileDescriptor_9ab648e6509929dc = []byte{ + // 439 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x8c, 0x93, 0xdf, 0x6b, 0xd4, 0x40, + 0x10, 0xc7, 0xdd, 0x5e, 0x93, 0xe8, 0x14, 0xca, 0xb9, 0x4a, 0x89, 0xa2, 0x10, 0x0f, 0x84, 0x20, + 0x92, 0x93, 0xd4, 0xbf, 0xe0, 0xe8, 0x61, 0x0f, 0xfa, 0x50, 0xb6, 0xd1, 0x07, 0x5f, 0x42, 0x7e, + 0x6c, 0xd2, 0x60, 0xb2, 0x1b, 0x76, 0x13, 0xcb, 0xf9, 0x2e, 0xfe, 0x7d, 0xfe, 0x37, 0x3e, 0x4a, + 0x66, 0x73, 0x5e, 0xb0, 0x56, 0xfa, 0x36, 0x3b, 0xf3, 0xf9, 0xce, 0xce, 0x77, 0xb2, 0x81, 0xb7, + 0x1d, 0x17, 0x5a, 0xaa, 0xa2, 0x96, 0x37, 0xcb, 0x4c, 0x2a, 0xbe, 0x6c, 0x95, 0xec, 0x64, 0xda, + 0x17, 0x4b, 0x53, 0x88, 0xd3, 0x5e, 0xe4, 0x35, 0x0f, 0x30, 0x4d, 0x61, 0x4f, 0x3f, 0xbf, 0xa5, + 0x2c, 0x54, 0xd2, 0xf0, 0x1b, 0xa9, 0xbe, 0xec, 0xa4, 0xfa, 0x3a, 0x69, 0x47, 0xe5, 0x7d, 0xe8, + 0xba, 0xca, 0x76, 0xf4, 0xeb, 0xff, 0xd0, 0xdb, 0x96, 0xeb, 0x11, 0xf3, 0xef, 0xc6, 0xbe, 0x72, + 0xa5, 0x2b, 0x29, 0x46, 0x72, 0xf1, 0x93, 0xc0, 0xe3, 0x15, 0x3a, 0x39, 0xe7, 0x49, 0xce, 0xd5, + 0x25, 0xda, 0x79, 0x09, 0x20, 0xfa, 0x66, 0x98, 0x53, 0xe5, 0xda, 0x25, 0x1e, 0xf1, 0x2d, 0xf6, + 0x48, 0xf4, 0xcd, 0x15, 0x26, 0xe8, 0x39, 0x00, 0x17, 0x79, 0x95, 0x08, 0xc1, 0xb5, 0x76, 0x0f, + 0x3c, 0xe2, 0x1f, 0x87, 0x7e, 0xb0, 0xbf, 0x33, 0xb8, 0xd5, 0x31, 0x58, 0xff, 0xe1, 0xd9, 0x44, + 0x4b, 0xdf, 0x81, 0x33, 0x0e, 0xe4, 0xce, 0x3c, 0xe2, 0x1f, 0x85, 0x27, 0xd3, 0x36, 0x9f, 0x4c, + 0xe9, 0x8c, 0x17, 0x6c, 0x87, 0x2d, 0x5e, 0x01, 0xec, 0x7b, 0x51, 0x00, 0xfb, 0x62, 0x13, 0x45, + 0x17, 0xeb, 0xf9, 0x03, 0xea, 0xc0, 0x6c, 0xb5, 0xf9, 0x30, 0x27, 0x8b, 0x1f, 0x07, 0x30, 0x37, + 0x13, 0xac, 0x45, 0xa7, 0xb6, 0xc6, 0xd2, 0x1b, 0xb0, 0xf2, 0x61, 0x45, 0xe8, 0xe6, 0x38, 0x7c, + 0x3a, 0xbd, 0xe7, 0x2c, 0xe9, 0x92, 0x68, 0xdb, 0x72, 0x66, 0x10, 0x1a, 0x82, 0x85, 0x9f, 0x08, + 0xad, 0x1d, 0x85, 0x2f, 0xa6, 0x6c, 0x84, 0xe1, 0xd5, 0x50, 0xc6, 0xc6, 0xcc, 0xa0, 0xf4, 0x19, + 0x3c, 0xc4, 0x75, 0xc5, 0x55, 0x8e, 0x56, 0x2c, 0xe6, 0xe0, 0x79, 0x93, 0xd3, 0x13, 0xb0, 0x65, + 0x51, 0x68, 0xde, 0xb9, 0x87, 0x1e, 0xf1, 0x67, 0x6c, 0x3c, 0x51, 0x0a, 0x87, 0xba, 0xfa, 0xc6, + 0x5d, 0x0b, 0xb3, 0x18, 0x0f, 0x6c, 0xa6, 0xb2, 0xd3, 0x30, 0x73, 0x6d, 0x8f, 0xf8, 0x0e, 0x1b, + 0x4f, 0xf4, 0x3d, 0xd8, 0xf8, 0x0e, 0xb4, 0xeb, 0x78, 0xb3, 0x3b, 0x66, 0x1a, 0xea, 0x66, 0xa6, + 0x91, 0x5d, 0x7d, 0x27, 0xf0, 0x44, 0xaa, 0x72, 0xca, 0xf6, 0x5d, 0x55, 0xaf, 0xa8, 0x51, 0x98, + 0x25, 0xa1, 0x44, 0x5f, 0x92, 0xcf, 0x1f, 0xcb, 0xaa, 0xbb, 0xee, 0xd3, 0x20, 0x93, 0xcd, 0x72, + 0xf2, 0x80, 0xfe, 0x1d, 0x96, 0xf2, 0xaf, 0xdf, 0xa2, 0x90, 0x2a, 0x1e, 0x32, 0x31, 0x66, 0x74, + 0x5c, 0x4a, 0x13, 0xfd, 0x22, 0x24, 0xb5, 0x31, 0x3a, 0xfd, 0x1d, 0x00, 0x00, 0xff, 0xff, 0x97, + 0xe0, 0x3d, 0x88, 0x55, 0x03, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/tensorflow_server.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/tensorflow_server.pb.go new file mode 100644 index 0000000..e965df9 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/tensorflow_server.pb.go @@ -0,0 +1,160 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/tensorflow_server.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Defines the configuration of a single TensorFlow server. +type ServerDef struct { + // The cluster of which this server is a member. + Cluster *ClusterDef `protobuf:"bytes,1,opt,name=cluster,proto3" json:"cluster,omitempty"` + // The name of the job of which this server is a member. + // + // NOTE(mrry): The `cluster` field must contain a `JobDef` with a `name` field + // that matches this name. + JobName string `protobuf:"bytes,2,opt,name=job_name,json=jobName,proto3" json:"job_name,omitempty"` + // The task index of this server in its job. + // + // NOTE: The `cluster` field must contain a `JobDef` with a matching `name` + // and a mapping in its `tasks` field for this index. + TaskIndex int32 `protobuf:"varint,3,opt,name=task_index,json=taskIndex,proto3" json:"task_index,omitempty"` + // The default configuration for sessions that run on this server. + DefaultSessionConfig *ConfigProto `protobuf:"bytes,4,opt,name=default_session_config,json=defaultSessionConfig,proto3" json:"default_session_config,omitempty"` + // The protocol to be used by this server. + // + // Acceptable values include: "grpc", "grpc+verbs". + Protocol string `protobuf:"bytes,5,opt,name=protocol,proto3" json:"protocol,omitempty"` + // The server port. If not set, then we identify the port from the job_name. + Port int32 `protobuf:"varint,6,opt,name=port,proto3" json:"port,omitempty"` + // Device filters for remote tasks in the cluster. + // NOTE: This is an experimental feature and only effective in TensorFlow 2.x. + ClusterDeviceFilters *ClusterDeviceFilters `protobuf:"bytes,7,opt,name=cluster_device_filters,json=clusterDeviceFilters,proto3" json:"cluster_device_filters,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ServerDef) Reset() { *m = ServerDef{} } +func (m *ServerDef) String() string { return proto.CompactTextString(m) } +func (*ServerDef) ProtoMessage() {} +func (*ServerDef) Descriptor() ([]byte, []int) { + return fileDescriptor_7f0f8cbd85b669e4, []int{0} +} + +func (m *ServerDef) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ServerDef.Unmarshal(m, b) +} +func (m *ServerDef) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ServerDef.Marshal(b, m, deterministic) +} +func (m *ServerDef) XXX_Merge(src proto.Message) { + xxx_messageInfo_ServerDef.Merge(m, src) +} +func (m *ServerDef) XXX_Size() int { + return xxx_messageInfo_ServerDef.Size(m) +} +func (m *ServerDef) XXX_DiscardUnknown() { + xxx_messageInfo_ServerDef.DiscardUnknown(m) +} + +var xxx_messageInfo_ServerDef proto.InternalMessageInfo + +func (m *ServerDef) GetCluster() *ClusterDef { + if m != nil { + return m.Cluster + } + return nil +} + +func (m *ServerDef) GetJobName() string { + if m != nil { + return m.JobName + } + return "" +} + +func (m *ServerDef) GetTaskIndex() int32 { + if m != nil { + return m.TaskIndex + } + return 0 +} + +func (m *ServerDef) GetDefaultSessionConfig() *ConfigProto { + if m != nil { + return m.DefaultSessionConfig + } + return nil +} + +func (m *ServerDef) GetProtocol() string { + if m != nil { + return m.Protocol + } + return "" +} + +func (m *ServerDef) GetPort() int32 { + if m != nil { + return m.Port + } + return 0 +} + +func (m *ServerDef) GetClusterDeviceFilters() *ClusterDeviceFilters { + if m != nil { + return m.ClusterDeviceFilters + } + return nil +} + +func init() { + proto.RegisterType((*ServerDef)(nil), "tensorflow.ServerDef") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/tensorflow_server.proto", fileDescriptor_7f0f8cbd85b669e4) +} + +var fileDescriptor_7f0f8cbd85b669e4 = []byte{ + // 354 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x74, 0x52, 0xcd, 0x4a, 0xeb, 0x40, + 0x14, 0x66, 0x7a, 0xfb, 0x3b, 0xf7, 0xae, 0x86, 0xd2, 0x1b, 0x03, 0x42, 0x10, 0x94, 0x6e, 0x4c, + 0x8b, 0xbe, 0x41, 0x2d, 0x82, 0x0b, 0xa5, 0xa4, 0xe8, 0xc2, 0xcd, 0x90, 0x9f, 0x99, 0x38, 0x35, + 0xc9, 0x29, 0x33, 0x93, 0xea, 0x13, 0xf8, 0x78, 0x3e, 0x8f, 0x4b, 0xc9, 0x4c, 0x6a, 0x5b, 0x69, + 0x76, 0x27, 0xdf, 0x4f, 0xce, 0xf7, 0x9d, 0x04, 0x4f, 0x35, 0x2b, 0x14, 0x48, 0x9e, 0xc1, 0xdb, + 0x24, 0x06, 0xc9, 0x26, 0x6b, 0x09, 0x1a, 0xa2, 0x92, 0x4f, 0x76, 0x04, 0x55, 0x4c, 0x6e, 0x98, + 0xf4, 0x0d, 0x45, 0xf0, 0x8e, 0x70, 0x2f, 0x1a, 0xdd, 0x71, 0x56, 0x2a, 0xbd, 0xf5, 0xb8, 0xe7, + 0xcd, 0x3a, 0x28, 0xb8, 0x48, 0x6b, 0xd9, 0x65, 0xa3, 0x2c, 0x61, 0x1b, 0x11, 0x33, 0xca, 0x45, + 0xa6, 0x99, 0x54, 0x56, 0x7e, 0xf6, 0xd9, 0xc2, 0x83, 0xa5, 0x89, 0x36, 0x67, 0x9c, 0x4c, 0x71, + 0xaf, 0x5e, 0xea, 0x20, 0x0f, 0x8d, 0xff, 0x5e, 0x8d, 0xfc, 0xdd, 0xeb, 0xfc, 0x1b, 0x4b, 0xcd, + 0x19, 0x0f, 0xb6, 0x32, 0x72, 0x82, 0xfb, 0x2b, 0x88, 0x68, 0x11, 0xe6, 0xcc, 0x69, 0x79, 0x68, + 0x3c, 0x08, 0x7a, 0x2b, 0x88, 0x1e, 0xc2, 0x9c, 0x91, 0x53, 0x8c, 0x75, 0xa8, 0x5e, 0xa9, 0x28, + 0x12, 0xf6, 0xee, 0xfc, 0xf1, 0xd0, 0xb8, 0x13, 0x0c, 0x2a, 0xe4, 0xae, 0x02, 0xc8, 0x3d, 0x1e, + 0x25, 0x8c, 0x87, 0x65, 0xa6, 0xa9, 0x62, 0x4a, 0x09, 0x28, 0xa8, 0x2d, 0xe2, 0xb4, 0xcd, 0xea, + 0xff, 0x07, 0xab, 0x0d, 0xb3, 0xa8, 0x22, 0x07, 0xc3, 0xda, 0xb6, 0xb4, 0x2e, 0x4b, 0x11, 0x17, + 0xf7, 0x4d, 0xa3, 0x18, 0x32, 0xa7, 0x63, 0x82, 0xfc, 0x3c, 0x13, 0x82, 0xdb, 0x6b, 0x90, 0xda, + 0xe9, 0x9a, 0x0c, 0x66, 0x26, 0x4f, 0x78, 0x54, 0x77, 0xa0, 0x87, 0x87, 0x71, 0x7a, 0x66, 0xbd, + 0x77, 0xb4, 0x79, 0x25, 0xbc, 0xb5, 0xba, 0x60, 0x18, 0x1f, 0x41, 0x67, 0x1f, 0x08, 0xbb, 0x20, + 0xd3, 0x7d, 0x77, 0x22, 0x94, 0x96, 0x65, 0xa1, 0x45, 0xce, 0x66, 0xff, 0xec, 0xb1, 0x4d, 0x13, + 0xb5, 0x40, 0xcf, 0x8f, 0xa9, 0xd0, 0x2f, 0x65, 0xe4, 0xc7, 0x90, 0xef, 0xfd, 0x2d, 0x0d, 0x63, + 0x0a, 0xbf, 0x3e, 0x29, 0x07, 0x49, 0x2b, 0x84, 0x1a, 0x44, 0xd1, 0x14, 0xec, 0xf4, 0x85, 0x50, + 0xd4, 0x35, 0xd3, 0xf5, 0x77, 0x00, 0x00, 0x00, 0xff, 0xff, 0x58, 0x6a, 0x8b, 0xf0, 0x9e, 0x02, + 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/trackable_object_graph.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/trackable_object_graph.pb.go new file mode 100644 index 0000000..3d0c29c --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/trackable_object_graph.pb.go @@ -0,0 +1,359 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/trackable_object_graph.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type TrackableObjectGraph struct { + Nodes []*TrackableObjectGraph_TrackableObject `protobuf:"bytes,1,rep,name=nodes,proto3" json:"nodes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TrackableObjectGraph) Reset() { *m = TrackableObjectGraph{} } +func (m *TrackableObjectGraph) String() string { return proto.CompactTextString(m) } +func (*TrackableObjectGraph) ProtoMessage() {} +func (*TrackableObjectGraph) Descriptor() ([]byte, []int) { + return fileDescriptor_120a5309f807e789, []int{0} +} + +func (m *TrackableObjectGraph) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TrackableObjectGraph.Unmarshal(m, b) +} +func (m *TrackableObjectGraph) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TrackableObjectGraph.Marshal(b, m, deterministic) +} +func (m *TrackableObjectGraph) XXX_Merge(src proto.Message) { + xxx_messageInfo_TrackableObjectGraph.Merge(m, src) +} +func (m *TrackableObjectGraph) XXX_Size() int { + return xxx_messageInfo_TrackableObjectGraph.Size(m) +} +func (m *TrackableObjectGraph) XXX_DiscardUnknown() { + xxx_messageInfo_TrackableObjectGraph.DiscardUnknown(m) +} + +var xxx_messageInfo_TrackableObjectGraph proto.InternalMessageInfo + +func (m *TrackableObjectGraph) GetNodes() []*TrackableObjectGraph_TrackableObject { + if m != nil { + return m.Nodes + } + return nil +} + +type TrackableObjectGraph_TrackableObject struct { + // Objects which this object depends on. + Children []*TrackableObjectGraph_TrackableObject_ObjectReference `protobuf:"bytes,1,rep,name=children,proto3" json:"children,omitempty"` + // Serialized data specific to this object. + Attributes []*TrackableObjectGraph_TrackableObject_SerializedTensor `protobuf:"bytes,2,rep,name=attributes,proto3" json:"attributes,omitempty"` + // Slot variables owned by this object. + SlotVariables []*TrackableObjectGraph_TrackableObject_SlotVariableReference `protobuf:"bytes,3,rep,name=slot_variables,json=slotVariables,proto3" json:"slot_variables,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TrackableObjectGraph_TrackableObject) Reset() { *m = TrackableObjectGraph_TrackableObject{} } +func (m *TrackableObjectGraph_TrackableObject) String() string { return proto.CompactTextString(m) } +func (*TrackableObjectGraph_TrackableObject) ProtoMessage() {} +func (*TrackableObjectGraph_TrackableObject) Descriptor() ([]byte, []int) { + return fileDescriptor_120a5309f807e789, []int{0, 0} +} + +func (m *TrackableObjectGraph_TrackableObject) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject.Unmarshal(m, b) +} +func (m *TrackableObjectGraph_TrackableObject) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject.Marshal(b, m, deterministic) +} +func (m *TrackableObjectGraph_TrackableObject) XXX_Merge(src proto.Message) { + xxx_messageInfo_TrackableObjectGraph_TrackableObject.Merge(m, src) +} +func (m *TrackableObjectGraph_TrackableObject) XXX_Size() int { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject.Size(m) +} +func (m *TrackableObjectGraph_TrackableObject) XXX_DiscardUnknown() { + xxx_messageInfo_TrackableObjectGraph_TrackableObject.DiscardUnknown(m) +} + +var xxx_messageInfo_TrackableObjectGraph_TrackableObject proto.InternalMessageInfo + +func (m *TrackableObjectGraph_TrackableObject) GetChildren() []*TrackableObjectGraph_TrackableObject_ObjectReference { + if m != nil { + return m.Children + } + return nil +} + +func (m *TrackableObjectGraph_TrackableObject) GetAttributes() []*TrackableObjectGraph_TrackableObject_SerializedTensor { + if m != nil { + return m.Attributes + } + return nil +} + +func (m *TrackableObjectGraph_TrackableObject) GetSlotVariables() []*TrackableObjectGraph_TrackableObject_SlotVariableReference { + if m != nil { + return m.SlotVariables + } + return nil +} + +type TrackableObjectGraph_TrackableObject_ObjectReference struct { + // An index into `TrackableObjectGraph.nodes`, indicating the object + // being referenced. + NodeId int32 `protobuf:"varint,1,opt,name=node_id,json=nodeId,proto3" json:"node_id,omitempty"` + // A user-provided name for the edge. + LocalName string `protobuf:"bytes,2,opt,name=local_name,json=localName,proto3" json:"local_name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) Reset() { + *m = TrackableObjectGraph_TrackableObject_ObjectReference{} +} +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) String() string { + return proto.CompactTextString(m) +} +func (*TrackableObjectGraph_TrackableObject_ObjectReference) ProtoMessage() {} +func (*TrackableObjectGraph_TrackableObject_ObjectReference) Descriptor() ([]byte, []int) { + return fileDescriptor_120a5309f807e789, []int{0, 0, 0} +} + +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_ObjectReference.Unmarshal(m, b) +} +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_ObjectReference.Marshal(b, m, deterministic) +} +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) XXX_Merge(src proto.Message) { + xxx_messageInfo_TrackableObjectGraph_TrackableObject_ObjectReference.Merge(m, src) +} +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) XXX_Size() int { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_ObjectReference.Size(m) +} +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) XXX_DiscardUnknown() { + xxx_messageInfo_TrackableObjectGraph_TrackableObject_ObjectReference.DiscardUnknown(m) +} + +var xxx_messageInfo_TrackableObjectGraph_TrackableObject_ObjectReference proto.InternalMessageInfo + +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) GetNodeId() int32 { + if m != nil { + return m.NodeId + } + return 0 +} + +func (m *TrackableObjectGraph_TrackableObject_ObjectReference) GetLocalName() string { + if m != nil { + return m.LocalName + } + return "" +} + +type TrackableObjectGraph_TrackableObject_SerializedTensor struct { + // A name for the Tensor. Simple variables have only one + // `SerializedTensor` named "VARIABLE_VALUE" by convention. This value may + // be restored on object creation as an optimization. + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + // The full name of the variable/tensor, if applicable. Used to allow + // name-based loading of checkpoints which were saved using an + // object-based API. Should match the checkpoint key which would have been + // assigned by tf.train.Saver. + FullName string `protobuf:"bytes,2,opt,name=full_name,json=fullName,proto3" json:"full_name,omitempty"` + // The generated name of the Tensor in the checkpoint. + CheckpointKey string `protobuf:"bytes,3,opt,name=checkpoint_key,json=checkpointKey,proto3" json:"checkpoint_key,omitempty"` + // Whether checkpoints should be considered as matching even without this + // value restored. Used for non-critical values which don't affect the + // TensorFlow graph, such as layer configurations. + OptionalRestore bool `protobuf:"varint,4,opt,name=optional_restore,json=optionalRestore,proto3" json:"optional_restore,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) Reset() { + *m = TrackableObjectGraph_TrackableObject_SerializedTensor{} +} +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) String() string { + return proto.CompactTextString(m) +} +func (*TrackableObjectGraph_TrackableObject_SerializedTensor) ProtoMessage() {} +func (*TrackableObjectGraph_TrackableObject_SerializedTensor) Descriptor() ([]byte, []int) { + return fileDescriptor_120a5309f807e789, []int{0, 0, 1} +} + +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_SerializedTensor.Unmarshal(m, b) +} +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_SerializedTensor.Marshal(b, m, deterministic) +} +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) XXX_Merge(src proto.Message) { + xxx_messageInfo_TrackableObjectGraph_TrackableObject_SerializedTensor.Merge(m, src) +} +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) XXX_Size() int { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_SerializedTensor.Size(m) +} +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) XXX_DiscardUnknown() { + xxx_messageInfo_TrackableObjectGraph_TrackableObject_SerializedTensor.DiscardUnknown(m) +} + +var xxx_messageInfo_TrackableObjectGraph_TrackableObject_SerializedTensor proto.InternalMessageInfo + +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) GetFullName() string { + if m != nil { + return m.FullName + } + return "" +} + +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) GetCheckpointKey() string { + if m != nil { + return m.CheckpointKey + } + return "" +} + +func (m *TrackableObjectGraph_TrackableObject_SerializedTensor) GetOptionalRestore() bool { + if m != nil { + return m.OptionalRestore + } + return false +} + +type TrackableObjectGraph_TrackableObject_SlotVariableReference struct { + // An index into `TrackableObjectGraph.nodes`, indicating the + // variable object this slot was created for. + OriginalVariableNodeId int32 `protobuf:"varint,1,opt,name=original_variable_node_id,json=originalVariableNodeId,proto3" json:"original_variable_node_id,omitempty"` + // The name of the slot (e.g. "m"/"v"). + SlotName string `protobuf:"bytes,2,opt,name=slot_name,json=slotName,proto3" json:"slot_name,omitempty"` + // An index into `TrackableObjectGraph.nodes`, indicating the + // `Object` with the value of the slot variable. + SlotVariableNodeId int32 `protobuf:"varint,3,opt,name=slot_variable_node_id,json=slotVariableNodeId,proto3" json:"slot_variable_node_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) Reset() { + *m = TrackableObjectGraph_TrackableObject_SlotVariableReference{} +} +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) String() string { + return proto.CompactTextString(m) +} +func (*TrackableObjectGraph_TrackableObject_SlotVariableReference) ProtoMessage() {} +func (*TrackableObjectGraph_TrackableObject_SlotVariableReference) Descriptor() ([]byte, []int) { + return fileDescriptor_120a5309f807e789, []int{0, 0, 2} +} + +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_SlotVariableReference.Unmarshal(m, b) +} +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_SlotVariableReference.Marshal(b, m, deterministic) +} +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) XXX_Merge(src proto.Message) { + xxx_messageInfo_TrackableObjectGraph_TrackableObject_SlotVariableReference.Merge(m, src) +} +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) XXX_Size() int { + return xxx_messageInfo_TrackableObjectGraph_TrackableObject_SlotVariableReference.Size(m) +} +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) XXX_DiscardUnknown() { + xxx_messageInfo_TrackableObjectGraph_TrackableObject_SlotVariableReference.DiscardUnknown(m) +} + +var xxx_messageInfo_TrackableObjectGraph_TrackableObject_SlotVariableReference proto.InternalMessageInfo + +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) GetOriginalVariableNodeId() int32 { + if m != nil { + return m.OriginalVariableNodeId + } + return 0 +} + +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) GetSlotName() string { + if m != nil { + return m.SlotName + } + return "" +} + +func (m *TrackableObjectGraph_TrackableObject_SlotVariableReference) GetSlotVariableNodeId() int32 { + if m != nil { + return m.SlotVariableNodeId + } + return 0 +} + +func init() { + proto.RegisterType((*TrackableObjectGraph)(nil), "tensorflow.TrackableObjectGraph") + proto.RegisterType((*TrackableObjectGraph_TrackableObject)(nil), "tensorflow.TrackableObjectGraph.TrackableObject") + proto.RegisterType((*TrackableObjectGraph_TrackableObject_ObjectReference)(nil), "tensorflow.TrackableObjectGraph.TrackableObject.ObjectReference") + proto.RegisterType((*TrackableObjectGraph_TrackableObject_SerializedTensor)(nil), "tensorflow.TrackableObjectGraph.TrackableObject.SerializedTensor") + proto.RegisterType((*TrackableObjectGraph_TrackableObject_SlotVariableReference)(nil), "tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReference") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/trackable_object_graph.proto", fileDescriptor_120a5309f807e789) +} + +var fileDescriptor_120a5309f807e789 = []byte{ + // 460 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x94, 0x93, 0x4f, 0x8b, 0xd3, 0x40, + 0x18, 0xc6, 0x99, 0xed, 0xb6, 0xb6, 0xaf, 0xec, 0x76, 0x19, 0x5c, 0x8d, 0x5d, 0x84, 0x22, 0x08, + 0xf5, 0x92, 0xfa, 0x07, 0x0f, 0xde, 0xd4, 0xc3, 0xca, 0x22, 0xac, 0x10, 0x57, 0x0f, 0x8b, 0x30, + 0x4c, 0x92, 0x37, 0xe9, 0xd8, 0x69, 0xde, 0x32, 0x99, 0x2a, 0xeb, 0xf7, 0xf0, 0x0b, 0x78, 0xf4, + 0x9b, 0xf8, 0x8d, 0x3c, 0xca, 0x4c, 0x9a, 0x6d, 0x1a, 0x7a, 0xe9, 0xed, 0xcd, 0x93, 0xe7, 0xf9, + 0x0d, 0xcf, 0x3b, 0x0c, 0xbc, 0xb2, 0x58, 0x94, 0x64, 0x32, 0x4d, 0x3f, 0xa6, 0x09, 0x19, 0x9c, + 0x2e, 0x0d, 0x59, 0x8a, 0x57, 0xd9, 0xd4, 0x1a, 0x99, 0xcc, 0x65, 0xac, 0x51, 0x50, 0xfc, 0x0d, + 0x13, 0x2b, 0x72, 0x23, 0x97, 0xb3, 0xd0, 0xff, 0xe7, 0xb0, 0x89, 0x3d, 0xfe, 0xd3, 0x83, 0x7b, + 0x57, 0xb5, 0xf9, 0xa3, 0xf7, 0xbe, 0x77, 0x56, 0x7e, 0x0e, 0xdd, 0x82, 0x52, 0x2c, 0x03, 0x36, + 0xee, 0x4c, 0xee, 0xbe, 0x78, 0x16, 0x6e, 0x42, 0xe1, 0xae, 0x40, 0x5b, 0x8c, 0xaa, 0xf8, 0xe8, + 0x6f, 0x17, 0x86, 0xad, 0x5f, 0xfc, 0x2b, 0xf4, 0x93, 0x99, 0xd2, 0xa9, 0xc1, 0x62, 0x8d, 0x7f, + 0xb3, 0x2f, 0x3e, 0x5c, 0x9f, 0x82, 0x19, 0x1a, 0x2c, 0x12, 0x8c, 0x6e, 0x89, 0x5c, 0x02, 0x48, + 0x6b, 0x8d, 0x8a, 0x57, 0x16, 0xcb, 0xe0, 0xc0, 0xf3, 0xdf, 0xee, 0xcd, 0xff, 0x84, 0x46, 0x49, + 0xad, 0x7e, 0x62, 0x7a, 0xe5, 0x93, 0x51, 0x03, 0xca, 0x17, 0x70, 0x5c, 0x6a, 0xb2, 0xe2, 0xbb, + 0x34, 0xca, 0x65, 0xca, 0xa0, 0xe3, 0x8f, 0x39, 0xdf, 0xff, 0x18, 0x4d, 0xf6, 0xcb, 0x9a, 0xb2, + 0x29, 0x73, 0x54, 0x36, 0xe4, 0x72, 0x74, 0x01, 0xc3, 0x56, 0x5d, 0xfe, 0x00, 0xee, 0xb8, 0xfd, + 0x0a, 0x95, 0x06, 0x6c, 0xcc, 0x26, 0xdd, 0xa8, 0xe7, 0x3e, 0x2f, 0x52, 0xfe, 0x08, 0x40, 0x53, + 0x22, 0xb5, 0x28, 0xe4, 0x02, 0x83, 0x83, 0x31, 0x9b, 0x0c, 0xa2, 0x81, 0x57, 0x2e, 0xe5, 0x02, + 0x47, 0xbf, 0x18, 0x9c, 0xb4, 0xab, 0x71, 0x0e, 0x87, 0xde, 0xcd, 0xbc, 0xdb, 0xcf, 0xfc, 0x0c, + 0x06, 0xd9, 0x4a, 0x6f, 0x61, 0xfa, 0x4e, 0x70, 0x14, 0xfe, 0x04, 0x8e, 0x93, 0x19, 0x26, 0xf3, + 0x25, 0xa9, 0xc2, 0x8a, 0x39, 0xde, 0x04, 0x1d, 0xef, 0x38, 0xda, 0xa8, 0x1f, 0xf0, 0x86, 0x3f, + 0x85, 0x13, 0x5a, 0x5a, 0x45, 0x85, 0xd4, 0xc2, 0x60, 0x69, 0xc9, 0x60, 0x70, 0x38, 0x66, 0x93, + 0x7e, 0x34, 0xac, 0xf5, 0xa8, 0x92, 0x47, 0xbf, 0x19, 0x9c, 0xee, 0xdc, 0x05, 0x7f, 0x0d, 0x0f, + 0xc9, 0xa8, 0x5c, 0x39, 0x48, 0xbd, 0x6f, 0xb1, 0xdd, 0xfd, 0x7e, 0x6d, 0xa8, 0xd3, 0x97, 0xd5, + 0x2e, 0xce, 0x60, 0xe0, 0xaf, 0xa9, 0xd9, 0xc1, 0x09, 0xbe, 0xc3, 0x73, 0x38, 0xdd, 0xba, 0xc3, + 0x5b, 0x66, 0xc7, 0x33, 0x79, 0xf3, 0x0a, 0x2a, 0xde, 0xbb, 0xeb, 0xeb, 0xcf, 0xb9, 0xb2, 0xb3, + 0x55, 0x1c, 0x26, 0xb4, 0x98, 0x36, 0x1e, 0xdf, 0xee, 0x31, 0xa7, 0xd6, 0xab, 0xcc, 0xc8, 0x08, + 0xa7, 0x08, 0xaf, 0x94, 0x22, 0xa7, 0x6a, 0xfa, 0xc7, 0x58, 0xdc, 0xf3, 0xd3, 0xcb, 0xff, 0x01, + 0x00, 0x00, 0xff, 0xff, 0x0d, 0xd9, 0xd2, 0xc4, 0xd4, 0x03, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/transport_options.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/transport_options.pb.go new file mode 100644 index 0000000..31e8d39 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/transport_options.pb.go @@ -0,0 +1,84 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/transport_options.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Extra data needed on a non-RDMA RecvBufResponse. +type RecvBufRespExtra struct { + TensorContent [][]byte `protobuf:"bytes,1,rep,name=tensor_content,json=tensorContent,proto3" json:"tensor_content,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RecvBufRespExtra) Reset() { *m = RecvBufRespExtra{} } +func (m *RecvBufRespExtra) String() string { return proto.CompactTextString(m) } +func (*RecvBufRespExtra) ProtoMessage() {} +func (*RecvBufRespExtra) Descriptor() ([]byte, []int) { + return fileDescriptor_527891df7bab7653, []int{0} +} + +func (m *RecvBufRespExtra) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RecvBufRespExtra.Unmarshal(m, b) +} +func (m *RecvBufRespExtra) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RecvBufRespExtra.Marshal(b, m, deterministic) +} +func (m *RecvBufRespExtra) XXX_Merge(src proto.Message) { + xxx_messageInfo_RecvBufRespExtra.Merge(m, src) +} +func (m *RecvBufRespExtra) XXX_Size() int { + return xxx_messageInfo_RecvBufRespExtra.Size(m) +} +func (m *RecvBufRespExtra) XXX_DiscardUnknown() { + xxx_messageInfo_RecvBufRespExtra.DiscardUnknown(m) +} + +var xxx_messageInfo_RecvBufRespExtra proto.InternalMessageInfo + +func (m *RecvBufRespExtra) GetTensorContent() [][]byte { + if m != nil { + return m.TensorContent + } + return nil +} + +func init() { + proto.RegisterType((*RecvBufRespExtra)(nil), "tensorflow.RecvBufRespExtra") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/transport_options.proto", fileDescriptor_527891df7bab7653) +} + +var fileDescriptor_527891df7bab7653 = []byte{ + // 164 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xe2, 0x32, 0x28, 0x49, 0xcd, 0x2b, + 0xce, 0x2f, 0x4a, 0xcb, 0xc9, 0x2f, 0xd7, 0x4f, 0xce, 0x2f, 0x4a, 0xd5, 0x2f, 0x28, 0xca, 0x2f, + 0xc9, 0x4f, 0x2a, 0x4d, 0xd3, 0x2f, 0x29, 0x4a, 0xcc, 0x2b, 0x2e, 0xc8, 0x2f, 0x2a, 0x89, 0xcf, + 0x2f, 0x28, 0xc9, 0xcc, 0xcf, 0x2b, 0xd6, 0x03, 0x4b, 0x09, 0x71, 0x21, 0x74, 0x28, 0x59, 0x72, + 0x09, 0x04, 0xa5, 0x26, 0x97, 0x39, 0x95, 0xa6, 0x05, 0xa5, 0x16, 0x17, 0xb8, 0x56, 0x94, 0x14, + 0x25, 0x0a, 0xa9, 0x72, 0xf1, 0x41, 0x54, 0xc4, 0x27, 0xe7, 0xe7, 0x95, 0xa4, 0xe6, 0x95, 0x48, + 0x30, 0x2a, 0x30, 0x6b, 0xf0, 0x04, 0xf1, 0x42, 0x44, 0x9d, 0x21, 0x82, 0x4e, 0xe1, 0x51, 0xa1, + 0xe9, 0x99, 0x25, 0x19, 0xa5, 0x49, 0x7a, 0xc9, 0xf9, 0xb9, 0xfa, 0x48, 0xae, 0xc0, 0xce, 0x4c, + 0xcf, 0x47, 0x73, 0x5e, 0x1a, 0xd8, 0x82, 0xa2, 0xd4, 0x78, 0xb0, 0x48, 0x71, 0x7c, 0x7a, 0x3e, + 0x84, 0x95, 0xc4, 0x06, 0xa6, 0x8c, 0x01, 0x01, 0x00, 0x00, 0xff, 0xff, 0xc3, 0x7e, 0xad, 0x50, + 0xda, 0x00, 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/verifier_config.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/verifier_config.pb.go new file mode 100644 index 0000000..521de3c --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/verifier_config.pb.go @@ -0,0 +1,131 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/verifier_config.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type VerifierConfig_Toggle int32 + +const ( + VerifierConfig_DEFAULT VerifierConfig_Toggle = 0 + VerifierConfig_ON VerifierConfig_Toggle = 1 + VerifierConfig_OFF VerifierConfig_Toggle = 2 +) + +var VerifierConfig_Toggle_name = map[int32]string{ + 0: "DEFAULT", + 1: "ON", + 2: "OFF", +} + +var VerifierConfig_Toggle_value = map[string]int32{ + "DEFAULT": 0, + "ON": 1, + "OFF": 2, +} + +func (x VerifierConfig_Toggle) String() string { + return proto.EnumName(VerifierConfig_Toggle_name, int32(x)) +} + +func (VerifierConfig_Toggle) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_5049fcf5d8bb3c3c, []int{0, 0} +} + +// The config for graph verifiers. +type VerifierConfig struct { + // Deadline for completion of all verification i.e. all the Toggle ON + // verifiers must complete execution within this time. + VerificationTimeoutInMs int64 `protobuf:"varint,1,opt,name=verification_timeout_in_ms,json=verificationTimeoutInMs,proto3" json:"verification_timeout_in_ms,omitempty"` + // Perform structural validation on a tensorflow graph. Default is OFF. + StructureVerifier VerifierConfig_Toggle `protobuf:"varint,2,opt,name=structure_verifier,json=structureVerifier,proto3,enum=tensorflow.VerifierConfig_Toggle" json:"structure_verifier,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *VerifierConfig) Reset() { *m = VerifierConfig{} } +func (m *VerifierConfig) String() string { return proto.CompactTextString(m) } +func (*VerifierConfig) ProtoMessage() {} +func (*VerifierConfig) Descriptor() ([]byte, []int) { + return fileDescriptor_5049fcf5d8bb3c3c, []int{0} +} + +func (m *VerifierConfig) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_VerifierConfig.Unmarshal(m, b) +} +func (m *VerifierConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_VerifierConfig.Marshal(b, m, deterministic) +} +func (m *VerifierConfig) XXX_Merge(src proto.Message) { + xxx_messageInfo_VerifierConfig.Merge(m, src) +} +func (m *VerifierConfig) XXX_Size() int { + return xxx_messageInfo_VerifierConfig.Size(m) +} +func (m *VerifierConfig) XXX_DiscardUnknown() { + xxx_messageInfo_VerifierConfig.DiscardUnknown(m) +} + +var xxx_messageInfo_VerifierConfig proto.InternalMessageInfo + +func (m *VerifierConfig) GetVerificationTimeoutInMs() int64 { + if m != nil { + return m.VerificationTimeoutInMs + } + return 0 +} + +func (m *VerifierConfig) GetStructureVerifier() VerifierConfig_Toggle { + if m != nil { + return m.StructureVerifier + } + return VerifierConfig_DEFAULT +} + +func init() { + proto.RegisterEnum("tensorflow.VerifierConfig_Toggle", VerifierConfig_Toggle_name, VerifierConfig_Toggle_value) + proto.RegisterType((*VerifierConfig)(nil), "tensorflow.VerifierConfig") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/verifier_config.proto", fileDescriptor_5049fcf5d8bb3c3c) +} + +var fileDescriptor_5049fcf5d8bb3c3c = []byte{ + // 274 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x6c, 0x90, 0x41, 0x4b, 0xc3, 0x30, + 0x18, 0x86, 0xcd, 0x06, 0x1d, 0x7c, 0xc2, 0xa8, 0x41, 0xb0, 0x78, 0x9a, 0x3b, 0xc8, 0x4e, 0x2d, + 0xe8, 0xd1, 0x93, 0x53, 0x0b, 0x82, 0xba, 0x32, 0x3a, 0x0f, 0x5e, 0xc2, 0x5a, 0x92, 0x18, 0x5c, + 0xfb, 0xc9, 0x97, 0xc4, 0xfd, 0x09, 0xff, 0x97, 0x7f, 0xc9, 0xa3, 0xac, 0x75, 0xda, 0x89, 0xb7, + 0x97, 0xbc, 0xcf, 0xc7, 0x1b, 0x1e, 0x88, 0x9d, 0xac, 0x2d, 0x92, 0x5a, 0xe1, 0x3a, 0x29, 0x91, + 0x64, 0xf2, 0x4a, 0xe8, 0xb0, 0xf0, 0x2a, 0x79, 0x93, 0x64, 0x94, 0x91, 0x24, 0x4a, 0xac, 0x95, + 0xd1, 0x71, 0x53, 0x70, 0xf8, 0xe5, 0xc7, 0x1f, 0x0c, 0x86, 0x8f, 0xdf, 0xd4, 0x55, 0x03, 0xf1, + 0x0b, 0x38, 0x6e, 0xef, 0xca, 0xa5, 0x33, 0x58, 0x0b, 0x67, 0x2a, 0x89, 0xde, 0x09, 0x53, 0x8b, + 0xca, 0x46, 0x6c, 0xc4, 0x26, 0xfd, 0xf9, 0x51, 0x97, 0xc8, 0x5b, 0xe0, 0xb6, 0xbe, 0xb7, 0x3c, + 0x03, 0x6e, 0x1d, 0xf9, 0xd2, 0x79, 0x92, 0x62, 0x3b, 0x1f, 0xf5, 0x46, 0x6c, 0x32, 0x3c, 0x3b, + 0xe9, 0x7c, 0x34, 0xde, 0x1d, 0x8d, 0x73, 0xd4, 0x7a, 0x25, 0xe7, 0x07, 0x3f, 0xc7, 0xdb, 0x7e, + 0x7c, 0x0a, 0x41, 0x5b, 0xf2, 0x7d, 0x18, 0x5c, 0xdf, 0xa4, 0x97, 0x8b, 0xbb, 0x3c, 0xdc, 0xe3, + 0x01, 0xf4, 0x66, 0x0f, 0x21, 0xe3, 0x03, 0xe8, 0xcf, 0xd2, 0x34, 0xec, 0x4d, 0xdf, 0x19, 0x44, + 0x48, 0xba, 0xbb, 0xa1, 0x68, 0x59, 0xc9, 0x35, 0xd2, 0xcb, 0xf4, 0x70, 0x77, 0x2e, 0xdb, 0x78, + 0xb0, 0x19, 0x7b, 0x5a, 0x68, 0xe3, 0x9e, 0x7d, 0x11, 0x97, 0x58, 0x25, 0x1d, 0x8b, 0xff, 0x47, + 0x8d, 0x7f, 0xf4, 0x2a, 0xdc, 0x98, 0x25, 0x29, 0x9a, 0x17, 0x2b, 0x34, 0xb6, 0xe9, 0x93, 0xb1, + 0x22, 0x68, 0xd2, 0xf9, 0x57, 0x00, 0x00, 0x00, 0xff, 0xff, 0xcf, 0x60, 0x77, 0xe4, 0x9d, 0x01, + 0x00, 0x00, +} diff --git a/tensorflow/go/core/protobuf/for_core_protos_go_proto/worker.pb.go b/tensorflow/go/core/protobuf/for_core_protos_go_proto/worker.pb.go new file mode 100644 index 0000000..5da644b --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/worker.pb.go @@ -0,0 +1,2425 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/worker.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + any "github.com/golang/protobuf/ptypes/any" + cost_graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/cost_graph_go_proto" + device_attributes_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/device_attributes_go_proto" + graph_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto" + step_stats_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/step_stats_go_proto" + tensor_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto" + tensor_shape_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + types_go_proto "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +type GetStatusRequest struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GetStatusRequest) Reset() { *m = GetStatusRequest{} } +func (m *GetStatusRequest) String() string { return proto.CompactTextString(m) } +func (*GetStatusRequest) ProtoMessage() {} +func (*GetStatusRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{0} +} + +func (m *GetStatusRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GetStatusRequest.Unmarshal(m, b) +} +func (m *GetStatusRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GetStatusRequest.Marshal(b, m, deterministic) +} +func (m *GetStatusRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_GetStatusRequest.Merge(m, src) +} +func (m *GetStatusRequest) XXX_Size() int { + return xxx_messageInfo_GetStatusRequest.Size(m) +} +func (m *GetStatusRequest) XXX_DiscardUnknown() { + xxx_messageInfo_GetStatusRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_GetStatusRequest proto.InternalMessageInfo + +type GetStatusResponse struct { + DeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,1,rep,name=device_attributes,json=deviceAttributes,proto3" json:"device_attributes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GetStatusResponse) Reset() { *m = GetStatusResponse{} } +func (m *GetStatusResponse) String() string { return proto.CompactTextString(m) } +func (*GetStatusResponse) ProtoMessage() {} +func (*GetStatusResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{1} +} + +func (m *GetStatusResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GetStatusResponse.Unmarshal(m, b) +} +func (m *GetStatusResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GetStatusResponse.Marshal(b, m, deterministic) +} +func (m *GetStatusResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_GetStatusResponse.Merge(m, src) +} +func (m *GetStatusResponse) XXX_Size() int { + return xxx_messageInfo_GetStatusResponse.Size(m) +} +func (m *GetStatusResponse) XXX_DiscardUnknown() { + xxx_messageInfo_GetStatusResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_GetStatusResponse proto.InternalMessageInfo + +func (m *GetStatusResponse) GetDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.DeviceAttributes + } + return nil +} + +type CreateWorkerSessionRequest struct { + // Sessions are identified by a given handle. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Defines the configuration of a TensorFlow worker. + ServerDef *ServerDef `protobuf:"bytes,2,opt,name=server_def,json=serverDef,proto3" json:"server_def,omitempty"` + // If true, any resources such as Variables used in the session will not be + // shared with other sessions. + IsolateSessionState bool `protobuf:"varint,3,opt,name=isolate_session_state,json=isolateSessionState,proto3" json:"isolate_session_state,omitempty"` + // The device attributes of all the devices in the cluster. + ClusterDeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,4,rep,name=cluster_device_attributes,json=clusterDeviceAttributes,proto3" json:"cluster_device_attributes,omitempty"` + // The master task name from which the request is sent. + MasterTask string `protobuf:"bytes,5,opt,name=master_task,json=masterTask,proto3" json:"master_task,omitempty"` + // The incarnation ID of the master task local CPU device. + // If the target worker already has a WorkerSession created previously with + // the same master task name but a different incarnation, it usually indicates + // that the previous master failed before deleting the WorkerSession on the + // worker. To prevent memory leaks, the worker should garbage collect the old + // WorkerSessions. + MasterIncarnation int64 `protobuf:"varint,6,opt,name=master_incarnation,json=masterIncarnation,proto3" json:"master_incarnation,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CreateWorkerSessionRequest) Reset() { *m = CreateWorkerSessionRequest{} } +func (m *CreateWorkerSessionRequest) String() string { return proto.CompactTextString(m) } +func (*CreateWorkerSessionRequest) ProtoMessage() {} +func (*CreateWorkerSessionRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{2} +} + +func (m *CreateWorkerSessionRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CreateWorkerSessionRequest.Unmarshal(m, b) +} +func (m *CreateWorkerSessionRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CreateWorkerSessionRequest.Marshal(b, m, deterministic) +} +func (m *CreateWorkerSessionRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CreateWorkerSessionRequest.Merge(m, src) +} +func (m *CreateWorkerSessionRequest) XXX_Size() int { + return xxx_messageInfo_CreateWorkerSessionRequest.Size(m) +} +func (m *CreateWorkerSessionRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CreateWorkerSessionRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CreateWorkerSessionRequest proto.InternalMessageInfo + +func (m *CreateWorkerSessionRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *CreateWorkerSessionRequest) GetServerDef() *ServerDef { + if m != nil { + return m.ServerDef + } + return nil +} + +func (m *CreateWorkerSessionRequest) GetIsolateSessionState() bool { + if m != nil { + return m.IsolateSessionState + } + return false +} + +func (m *CreateWorkerSessionRequest) GetClusterDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.ClusterDeviceAttributes + } + return nil +} + +func (m *CreateWorkerSessionRequest) GetMasterTask() string { + if m != nil { + return m.MasterTask + } + return "" +} + +func (m *CreateWorkerSessionRequest) GetMasterIncarnation() int64 { + if m != nil { + return m.MasterIncarnation + } + return 0 +} + +type CreateWorkerSessionResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CreateWorkerSessionResponse) Reset() { *m = CreateWorkerSessionResponse{} } +func (m *CreateWorkerSessionResponse) String() string { return proto.CompactTextString(m) } +func (*CreateWorkerSessionResponse) ProtoMessage() {} +func (*CreateWorkerSessionResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{3} +} + +func (m *CreateWorkerSessionResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CreateWorkerSessionResponse.Unmarshal(m, b) +} +func (m *CreateWorkerSessionResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CreateWorkerSessionResponse.Marshal(b, m, deterministic) +} +func (m *CreateWorkerSessionResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CreateWorkerSessionResponse.Merge(m, src) +} +func (m *CreateWorkerSessionResponse) XXX_Size() int { + return xxx_messageInfo_CreateWorkerSessionResponse.Size(m) +} +func (m *CreateWorkerSessionResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CreateWorkerSessionResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CreateWorkerSessionResponse proto.InternalMessageInfo + +type DeleteWorkerSessionRequest struct { + // Sessions are identified by a given handle. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeleteWorkerSessionRequest) Reset() { *m = DeleteWorkerSessionRequest{} } +func (m *DeleteWorkerSessionRequest) String() string { return proto.CompactTextString(m) } +func (*DeleteWorkerSessionRequest) ProtoMessage() {} +func (*DeleteWorkerSessionRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{4} +} + +func (m *DeleteWorkerSessionRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeleteWorkerSessionRequest.Unmarshal(m, b) +} +func (m *DeleteWorkerSessionRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeleteWorkerSessionRequest.Marshal(b, m, deterministic) +} +func (m *DeleteWorkerSessionRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeleteWorkerSessionRequest.Merge(m, src) +} +func (m *DeleteWorkerSessionRequest) XXX_Size() int { + return xxx_messageInfo_DeleteWorkerSessionRequest.Size(m) +} +func (m *DeleteWorkerSessionRequest) XXX_DiscardUnknown() { + xxx_messageInfo_DeleteWorkerSessionRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_DeleteWorkerSessionRequest proto.InternalMessageInfo + +func (m *DeleteWorkerSessionRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +type DeleteWorkerSessionResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeleteWorkerSessionResponse) Reset() { *m = DeleteWorkerSessionResponse{} } +func (m *DeleteWorkerSessionResponse) String() string { return proto.CompactTextString(m) } +func (*DeleteWorkerSessionResponse) ProtoMessage() {} +func (*DeleteWorkerSessionResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{5} +} + +func (m *DeleteWorkerSessionResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeleteWorkerSessionResponse.Unmarshal(m, b) +} +func (m *DeleteWorkerSessionResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeleteWorkerSessionResponse.Marshal(b, m, deterministic) +} +func (m *DeleteWorkerSessionResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeleteWorkerSessionResponse.Merge(m, src) +} +func (m *DeleteWorkerSessionResponse) XXX_Size() int { + return xxx_messageInfo_DeleteWorkerSessionResponse.Size(m) +} +func (m *DeleteWorkerSessionResponse) XXX_DiscardUnknown() { + xxx_messageInfo_DeleteWorkerSessionResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_DeleteWorkerSessionResponse proto.InternalMessageInfo + +type RegisterGraphRequest struct { + // Subgraphs are scoped within one session. + SessionHandle string `protobuf:"bytes,1,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Set to true if `CreateWorkerSession` was called for `session_handle`. + CreateWorkerSessionCalled bool `protobuf:"varint,6,opt,name=create_worker_session_called,json=createWorkerSessionCalled,proto3" json:"create_worker_session_called,omitempty"` + // "graph_def" has the subgraph of nodes for this worker, with each node + // having its device_name filled in. + GraphDef *graph_go_proto.GraphDef `protobuf:"bytes,2,opt,name=graph_def,json=graphDef,proto3" json:"graph_def,omitempty"` + // True iff the graph (before partitioning) contains control flow nodes. + // + // As of 01/11/2015, this is no longer set by clients. + HasControlFlow bool `protobuf:"varint,3,opt,name=has_control_flow,json=hasControlFlow,proto3" json:"has_control_flow,omitempty"` // Deprecated: Do not use. + // Configuration options for the session in which this graph was created. + GraphOptions *GraphOptions `protobuf:"bytes,4,opt,name=graph_options,json=graphOptions,proto3" json:"graph_options,omitempty"` + // Field(s) used by TensorFlow Debugger (tfdbg). + DebugOptions *DebugOptions `protobuf:"bytes,5,opt,name=debug_options,json=debugOptions,proto3" json:"debug_options,omitempty"` + // If graph_def contains any collective ops this must be a positive + // integer used to coordinate execution with other graphs. All + // graphs in a distributed execution with the same + // collective_graph_key will coordinate to use the same step_id + // concurrently so that BufRendezvous entries will make the correct + // values accessible. + CollectiveGraphKey int64 `protobuf:"varint,7,opt,name=collective_graph_key,json=collectiveGraphKey,proto3" json:"collective_graph_key,omitempty"` + // ConfigProto from the session in which this graph was created. + // Contains additional parameters beyond graph_options, including + // the name of the requested executor. + ConfigProto *ConfigProto `protobuf:"bytes,8,opt,name=config_proto,json=configProto,proto3" json:"config_proto,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RegisterGraphRequest) Reset() { *m = RegisterGraphRequest{} } +func (m *RegisterGraphRequest) String() string { return proto.CompactTextString(m) } +func (*RegisterGraphRequest) ProtoMessage() {} +func (*RegisterGraphRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{6} +} + +func (m *RegisterGraphRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RegisterGraphRequest.Unmarshal(m, b) +} +func (m *RegisterGraphRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RegisterGraphRequest.Marshal(b, m, deterministic) +} +func (m *RegisterGraphRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RegisterGraphRequest.Merge(m, src) +} +func (m *RegisterGraphRequest) XXX_Size() int { + return xxx_messageInfo_RegisterGraphRequest.Size(m) +} +func (m *RegisterGraphRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RegisterGraphRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RegisterGraphRequest proto.InternalMessageInfo + +func (m *RegisterGraphRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *RegisterGraphRequest) GetCreateWorkerSessionCalled() bool { + if m != nil { + return m.CreateWorkerSessionCalled + } + return false +} + +func (m *RegisterGraphRequest) GetGraphDef() *graph_go_proto.GraphDef { + if m != nil { + return m.GraphDef + } + return nil +} + +// Deprecated: Do not use. +func (m *RegisterGraphRequest) GetHasControlFlow() bool { + if m != nil { + return m.HasControlFlow + } + return false +} + +func (m *RegisterGraphRequest) GetGraphOptions() *GraphOptions { + if m != nil { + return m.GraphOptions + } + return nil +} + +func (m *RegisterGraphRequest) GetDebugOptions() *DebugOptions { + if m != nil { + return m.DebugOptions + } + return nil +} + +func (m *RegisterGraphRequest) GetCollectiveGraphKey() int64 { + if m != nil { + return m.CollectiveGraphKey + } + return 0 +} + +func (m *RegisterGraphRequest) GetConfigProto() *ConfigProto { + if m != nil { + return m.ConfigProto + } + return nil +} + +type RegisterGraphResponse struct { + // If the registration succeeds, returns an opaque graph_handle to + // the master. The master calls RunGraph with graph_handle to + // compute different steps. + GraphHandle string `protobuf:"bytes,1,opt,name=graph_handle,json=graphHandle,proto3" json:"graph_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RegisterGraphResponse) Reset() { *m = RegisterGraphResponse{} } +func (m *RegisterGraphResponse) String() string { return proto.CompactTextString(m) } +func (*RegisterGraphResponse) ProtoMessage() {} +func (*RegisterGraphResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{7} +} + +func (m *RegisterGraphResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RegisterGraphResponse.Unmarshal(m, b) +} +func (m *RegisterGraphResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RegisterGraphResponse.Marshal(b, m, deterministic) +} +func (m *RegisterGraphResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RegisterGraphResponse.Merge(m, src) +} +func (m *RegisterGraphResponse) XXX_Size() int { + return xxx_messageInfo_RegisterGraphResponse.Size(m) +} +func (m *RegisterGraphResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RegisterGraphResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RegisterGraphResponse proto.InternalMessageInfo + +func (m *RegisterGraphResponse) GetGraphHandle() string { + if m != nil { + return m.GraphHandle + } + return "" +} + +type DeregisterGraphRequest struct { + // The session_handle used when registering the graph. If session_handle is + // empty, a single global namespace is used. + SessionHandle string `protobuf:"bytes,2,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Set to true if `CreateWorkerSession` was called for `session_handle`. + CreateWorkerSessionCalled bool `protobuf:"varint,3,opt,name=create_worker_session_called,json=createWorkerSessionCalled,proto3" json:"create_worker_session_called,omitempty"` + // REQUIRED: graph_handle must be returned by a RegisterGraph call + // to the same WorkerService. + GraphHandle string `protobuf:"bytes,1,opt,name=graph_handle,json=graphHandle,proto3" json:"graph_handle,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeregisterGraphRequest) Reset() { *m = DeregisterGraphRequest{} } +func (m *DeregisterGraphRequest) String() string { return proto.CompactTextString(m) } +func (*DeregisterGraphRequest) ProtoMessage() {} +func (*DeregisterGraphRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{8} +} + +func (m *DeregisterGraphRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeregisterGraphRequest.Unmarshal(m, b) +} +func (m *DeregisterGraphRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeregisterGraphRequest.Marshal(b, m, deterministic) +} +func (m *DeregisterGraphRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeregisterGraphRequest.Merge(m, src) +} +func (m *DeregisterGraphRequest) XXX_Size() int { + return xxx_messageInfo_DeregisterGraphRequest.Size(m) +} +func (m *DeregisterGraphRequest) XXX_DiscardUnknown() { + xxx_messageInfo_DeregisterGraphRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_DeregisterGraphRequest proto.InternalMessageInfo + +func (m *DeregisterGraphRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *DeregisterGraphRequest) GetCreateWorkerSessionCalled() bool { + if m != nil { + return m.CreateWorkerSessionCalled + } + return false +} + +func (m *DeregisterGraphRequest) GetGraphHandle() string { + if m != nil { + return m.GraphHandle + } + return "" +} + +type DeregisterGraphResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *DeregisterGraphResponse) Reset() { *m = DeregisterGraphResponse{} } +func (m *DeregisterGraphResponse) String() string { return proto.CompactTextString(m) } +func (*DeregisterGraphResponse) ProtoMessage() {} +func (*DeregisterGraphResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{9} +} + +func (m *DeregisterGraphResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_DeregisterGraphResponse.Unmarshal(m, b) +} +func (m *DeregisterGraphResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_DeregisterGraphResponse.Marshal(b, m, deterministic) +} +func (m *DeregisterGraphResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_DeregisterGraphResponse.Merge(m, src) +} +func (m *DeregisterGraphResponse) XXX_Size() int { + return xxx_messageInfo_DeregisterGraphResponse.Size(m) +} +func (m *DeregisterGraphResponse) XXX_DiscardUnknown() { + xxx_messageInfo_DeregisterGraphResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_DeregisterGraphResponse proto.InternalMessageInfo + +type CleanupAllRequest struct { + // A list of container names. + // + // If 'container' is not empty, releases resources in the given + // containers in all devices. + // + // If 'container' is empty, releases resources in the default + // container in all devices. + Container []string `protobuf:"bytes,1,rep,name=container,proto3" json:"container,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CleanupAllRequest) Reset() { *m = CleanupAllRequest{} } +func (m *CleanupAllRequest) String() string { return proto.CompactTextString(m) } +func (*CleanupAllRequest) ProtoMessage() {} +func (*CleanupAllRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{10} +} + +func (m *CleanupAllRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CleanupAllRequest.Unmarshal(m, b) +} +func (m *CleanupAllRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CleanupAllRequest.Marshal(b, m, deterministic) +} +func (m *CleanupAllRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CleanupAllRequest.Merge(m, src) +} +func (m *CleanupAllRequest) XXX_Size() int { + return xxx_messageInfo_CleanupAllRequest.Size(m) +} +func (m *CleanupAllRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CleanupAllRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CleanupAllRequest proto.InternalMessageInfo + +func (m *CleanupAllRequest) GetContainer() []string { + if m != nil { + return m.Container + } + return nil +} + +type CleanupAllResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CleanupAllResponse) Reset() { *m = CleanupAllResponse{} } +func (m *CleanupAllResponse) String() string { return proto.CompactTextString(m) } +func (*CleanupAllResponse) ProtoMessage() {} +func (*CleanupAllResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{11} +} + +func (m *CleanupAllResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CleanupAllResponse.Unmarshal(m, b) +} +func (m *CleanupAllResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CleanupAllResponse.Marshal(b, m, deterministic) +} +func (m *CleanupAllResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CleanupAllResponse.Merge(m, src) +} +func (m *CleanupAllResponse) XXX_Size() int { + return xxx_messageInfo_CleanupAllResponse.Size(m) +} +func (m *CleanupAllResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CleanupAllResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CleanupAllResponse proto.InternalMessageInfo + +// Options specific to the execution of a single step. +type ExecutorOpts struct { + RecordCosts bool `protobuf:"varint,1,opt,name=record_costs,json=recordCosts,proto3" json:"record_costs,omitempty"` + RecordTimeline bool `protobuf:"varint,3,opt,name=record_timeline,json=recordTimeline,proto3" json:"record_timeline,omitempty"` + RecordPartitionGraphs bool `protobuf:"varint,4,opt,name=record_partition_graphs,json=recordPartitionGraphs,proto3" json:"record_partition_graphs,omitempty"` + ReportTensorAllocationsUponOom bool `protobuf:"varint,5,opt,name=report_tensor_allocations_upon_oom,json=reportTensorAllocationsUponOom,proto3" json:"report_tensor_allocations_upon_oom,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ExecutorOpts) Reset() { *m = ExecutorOpts{} } +func (m *ExecutorOpts) String() string { return proto.CompactTextString(m) } +func (*ExecutorOpts) ProtoMessage() {} +func (*ExecutorOpts) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{12} +} + +func (m *ExecutorOpts) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ExecutorOpts.Unmarshal(m, b) +} +func (m *ExecutorOpts) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ExecutorOpts.Marshal(b, m, deterministic) +} +func (m *ExecutorOpts) XXX_Merge(src proto.Message) { + xxx_messageInfo_ExecutorOpts.Merge(m, src) +} +func (m *ExecutorOpts) XXX_Size() int { + return xxx_messageInfo_ExecutorOpts.Size(m) +} +func (m *ExecutorOpts) XXX_DiscardUnknown() { + xxx_messageInfo_ExecutorOpts.DiscardUnknown(m) +} + +var xxx_messageInfo_ExecutorOpts proto.InternalMessageInfo + +func (m *ExecutorOpts) GetRecordCosts() bool { + if m != nil { + return m.RecordCosts + } + return false +} + +func (m *ExecutorOpts) GetRecordTimeline() bool { + if m != nil { + return m.RecordTimeline + } + return false +} + +func (m *ExecutorOpts) GetRecordPartitionGraphs() bool { + if m != nil { + return m.RecordPartitionGraphs + } + return false +} + +func (m *ExecutorOpts) GetReportTensorAllocationsUponOom() bool { + if m != nil { + return m.ReportTensorAllocationsUponOom + } + return false +} + +type RunGraphRequest struct { + // session_handle is the master-generated unique id for this session. + // If session_handle is non-empty, it must be the same as used when + // registering the graph. If it is empty, a single global namespace is used to + // search for the graph_handle. + SessionHandle string `protobuf:"bytes,8,opt,name=session_handle,json=sessionHandle,proto3" json:"session_handle,omitempty"` + // Set to true if `CreateWorkerSession` was called for `session_handle`. + CreateWorkerSessionCalled bool `protobuf:"varint,10,opt,name=create_worker_session_called,json=createWorkerSessionCalled,proto3" json:"create_worker_session_called,omitempty"` + // REQUIRED: graph_handle must be returned by a RegisterGraph call + // to the same WorkerService. + GraphHandle string `protobuf:"bytes,1,opt,name=graph_handle,json=graphHandle,proto3" json:"graph_handle,omitempty"` + // A unique ID to distinguish different runs of the same graph. + // + // The master generates a global unique `step_id` to distinguish + // different runs of the graph computation. Subgraphs communicate + // (e.g., send/recv ops) with each other using `step_id` to + // distinguish tensors generated by different runs. + StepId int64 `protobuf:"varint,2,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Options for this step. + ExecOpts *ExecutorOpts `protobuf:"bytes,5,opt,name=exec_opts,json=execOpts,proto3" json:"exec_opts,omitempty"` + // Runs the graph. + // + // Sends the tensors in "send" into the graph before the run and + // fetches the keys into `RunGraphResponse.recv` after the run. + Send []*NamedTensorProto `protobuf:"bytes,3,rep,name=send,proto3" json:"send,omitempty"` + RecvKey []string `protobuf:"bytes,4,rep,name=recv_key,json=recvKey,proto3" json:"recv_key,omitempty"` + // True if the RunGraphRequest is a partial run request. + IsPartial bool `protobuf:"varint,6,opt,name=is_partial,json=isPartial,proto3" json:"is_partial,omitempty"` + // True if this is the last partial run request in a sequence of requests. + IsLastPartialRun bool `protobuf:"varint,7,opt,name=is_last_partial_run,json=isLastPartialRun,proto3" json:"is_last_partial_run,omitempty"` + // If true then some errors, e.g., execution errors that have long + // error messages, may return an OK RunGraphResponse with the actual + // error saved in the status_code/status_error_message fields of the + // response body. This is a workaround since the RPC subsystem may + // truncate long metadata messages. + StoreErrorsInResponseBody bool `protobuf:"varint,9,opt,name=store_errors_in_response_body,json=storeErrorsInResponseBody,proto3" json:"store_errors_in_response_body,omitempty"` + // Unique identifier for this request. Every RunGraphRequest must have a + // unique request_id, and retried RunGraphRequests must have the same + // request_id. If request_id is zero, retry detection is disabled. + // + // Retried RunGraphRequests are problematic because they may issue a + // RecvTensor that will have no corresponding sender and will wait forever. + // Workers use request_ids to reject retried RunGraph requests instead of + // waiting forever. + RequestId int64 `protobuf:"varint,11,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunGraphRequest) Reset() { *m = RunGraphRequest{} } +func (m *RunGraphRequest) String() string { return proto.CompactTextString(m) } +func (*RunGraphRequest) ProtoMessage() {} +func (*RunGraphRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{13} +} + +func (m *RunGraphRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunGraphRequest.Unmarshal(m, b) +} +func (m *RunGraphRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunGraphRequest.Marshal(b, m, deterministic) +} +func (m *RunGraphRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunGraphRequest.Merge(m, src) +} +func (m *RunGraphRequest) XXX_Size() int { + return xxx_messageInfo_RunGraphRequest.Size(m) +} +func (m *RunGraphRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RunGraphRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RunGraphRequest proto.InternalMessageInfo + +func (m *RunGraphRequest) GetSessionHandle() string { + if m != nil { + return m.SessionHandle + } + return "" +} + +func (m *RunGraphRequest) GetCreateWorkerSessionCalled() bool { + if m != nil { + return m.CreateWorkerSessionCalled + } + return false +} + +func (m *RunGraphRequest) GetGraphHandle() string { + if m != nil { + return m.GraphHandle + } + return "" +} + +func (m *RunGraphRequest) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *RunGraphRequest) GetExecOpts() *ExecutorOpts { + if m != nil { + return m.ExecOpts + } + return nil +} + +func (m *RunGraphRequest) GetSend() []*NamedTensorProto { + if m != nil { + return m.Send + } + return nil +} + +func (m *RunGraphRequest) GetRecvKey() []string { + if m != nil { + return m.RecvKey + } + return nil +} + +func (m *RunGraphRequest) GetIsPartial() bool { + if m != nil { + return m.IsPartial + } + return false +} + +func (m *RunGraphRequest) GetIsLastPartialRun() bool { + if m != nil { + return m.IsLastPartialRun + } + return false +} + +func (m *RunGraphRequest) GetStoreErrorsInResponseBody() bool { + if m != nil { + return m.StoreErrorsInResponseBody + } + return false +} + +func (m *RunGraphRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type RunGraphResponse struct { + // A list of tensors corresponding to those requested by + // `RunGraphRequest.recv_key`. + Recv []*NamedTensorProto `protobuf:"bytes,1,rep,name=recv,proto3" json:"recv,omitempty"` + // If the request asked for execution stats, the cost graph, or the partition + // graphs, these are returned here. + // TODO(suharshs): Package these in a RunMetadata instead. + StepStats *step_stats_go_proto.StepStats `protobuf:"bytes,2,opt,name=step_stats,json=stepStats,proto3" json:"step_stats,omitempty"` + CostGraph *cost_graph_go_proto.CostGraphDef `protobuf:"bytes,3,opt,name=cost_graph,json=costGraph,proto3" json:"cost_graph,omitempty"` + PartitionGraph []*graph_go_proto.GraphDef `protobuf:"bytes,4,rep,name=partition_graph,json=partitionGraph,proto3" json:"partition_graph,omitempty"` + // If store_errors_in_response_body is true in the request, then + // optionally the server may return an OK status for the RPC and + // fill the true status into the fields below, to allow for messages + // that are too long to fit in metadata. + StatusCode Code `protobuf:"varint,5,opt,name=status_code,json=statusCode,proto3,enum=tensorflow.error.Code" json:"status_code,omitempty"` + StatusErrorMessage string `protobuf:"bytes,6,opt,name=status_error_message,json=statusErrorMessage,proto3" json:"status_error_message,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RunGraphResponse) Reset() { *m = RunGraphResponse{} } +func (m *RunGraphResponse) String() string { return proto.CompactTextString(m) } +func (*RunGraphResponse) ProtoMessage() {} +func (*RunGraphResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{14} +} + +func (m *RunGraphResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RunGraphResponse.Unmarshal(m, b) +} +func (m *RunGraphResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RunGraphResponse.Marshal(b, m, deterministic) +} +func (m *RunGraphResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RunGraphResponse.Merge(m, src) +} +func (m *RunGraphResponse) XXX_Size() int { + return xxx_messageInfo_RunGraphResponse.Size(m) +} +func (m *RunGraphResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RunGraphResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RunGraphResponse proto.InternalMessageInfo + +func (m *RunGraphResponse) GetRecv() []*NamedTensorProto { + if m != nil { + return m.Recv + } + return nil +} + +func (m *RunGraphResponse) GetStepStats() *step_stats_go_proto.StepStats { + if m != nil { + return m.StepStats + } + return nil +} + +func (m *RunGraphResponse) GetCostGraph() *cost_graph_go_proto.CostGraphDef { + if m != nil { + return m.CostGraph + } + return nil +} + +func (m *RunGraphResponse) GetPartitionGraph() []*graph_go_proto.GraphDef { + if m != nil { + return m.PartitionGraph + } + return nil +} + +func (m *RunGraphResponse) GetStatusCode() Code { + if m != nil { + return m.StatusCode + } + return Code_OK +} + +func (m *RunGraphResponse) GetStatusErrorMessage() string { + if m != nil { + return m.StatusErrorMessage + } + return "" +} + +type CleanupGraphRequest struct { + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CleanupGraphRequest) Reset() { *m = CleanupGraphRequest{} } +func (m *CleanupGraphRequest) String() string { return proto.CompactTextString(m) } +func (*CleanupGraphRequest) ProtoMessage() {} +func (*CleanupGraphRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{15} +} + +func (m *CleanupGraphRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CleanupGraphRequest.Unmarshal(m, b) +} +func (m *CleanupGraphRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CleanupGraphRequest.Marshal(b, m, deterministic) +} +func (m *CleanupGraphRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CleanupGraphRequest.Merge(m, src) +} +func (m *CleanupGraphRequest) XXX_Size() int { + return xxx_messageInfo_CleanupGraphRequest.Size(m) +} +func (m *CleanupGraphRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CleanupGraphRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CleanupGraphRequest proto.InternalMessageInfo + +func (m *CleanupGraphRequest) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +type CleanupGraphResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CleanupGraphResponse) Reset() { *m = CleanupGraphResponse{} } +func (m *CleanupGraphResponse) String() string { return proto.CompactTextString(m) } +func (*CleanupGraphResponse) ProtoMessage() {} +func (*CleanupGraphResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{16} +} + +func (m *CleanupGraphResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CleanupGraphResponse.Unmarshal(m, b) +} +func (m *CleanupGraphResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CleanupGraphResponse.Marshal(b, m, deterministic) +} +func (m *CleanupGraphResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CleanupGraphResponse.Merge(m, src) +} +func (m *CleanupGraphResponse) XXX_Size() int { + return xxx_messageInfo_CleanupGraphResponse.Size(m) +} +func (m *CleanupGraphResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CleanupGraphResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CleanupGraphResponse proto.InternalMessageInfo + +type RecvTensorRequest struct { + // The step in which the tensor will be produced. + // + // REQUIRED: This must eventually correspond to the `step_id` passed + // into a RunGraph call on the same WorkerService. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // A key identifying the channel to receive tensors from. A RecvTensor request + // retrieves one tensor from the channel, but multiple tensors can be sent and + // received over the same channel with multiple RecvTensor requests. See + // rendezvous.h for details. + RendezvousKey string `protobuf:"bytes,2,opt,name=rendezvous_key,json=rendezvousKey,proto3" json:"rendezvous_key,omitempty"` + // If true, use an out-of-band DMA mechanism to transfer the + // received tensor. + DmaOk bool `protobuf:"varint,3,opt,name=dma_ok,json=dmaOk,proto3" json:"dma_ok,omitempty"` + // Optional information on client-side device locality. + ClientLocality *device_attributes_go_proto.DeviceLocality `protobuf:"bytes,4,opt,name=client_locality,json=clientLocality,proto3" json:"client_locality,omitempty"` + // Optional information on server-side device locality. + ServerLocality *device_attributes_go_proto.DeviceLocality `protobuf:"bytes,5,opt,name=server_locality,json=serverLocality,proto3" json:"server_locality,omitempty"` + // Optional information needed by the RPC subsystem. + TransportOptions *any.Any `protobuf:"bytes,6,opt,name=transport_options,json=transportOptions,proto3" json:"transport_options,omitempty"` + // Unique identifier for this request. Every RecvTensorRequest must have a + // unique request_id, and retried RecvTensorRequests must have the same + // request_id. If request_id is zero, retry detection and response cache + // are disabled. + // + // Retried RecvTensorRequests are problematic because a RecvTensor with no + // corresponding sender will wait forever, and the tensor may have been + // delivered to a previous retry. Workers use request_ids to reject retried + // RecvTensor requests instead of waiting forever. + RequestId int64 `protobuf:"varint,7,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RecvTensorRequest) Reset() { *m = RecvTensorRequest{} } +func (m *RecvTensorRequest) String() string { return proto.CompactTextString(m) } +func (*RecvTensorRequest) ProtoMessage() {} +func (*RecvTensorRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{17} +} + +func (m *RecvTensorRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RecvTensorRequest.Unmarshal(m, b) +} +func (m *RecvTensorRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RecvTensorRequest.Marshal(b, m, deterministic) +} +func (m *RecvTensorRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RecvTensorRequest.Merge(m, src) +} +func (m *RecvTensorRequest) XXX_Size() int { + return xxx_messageInfo_RecvTensorRequest.Size(m) +} +func (m *RecvTensorRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RecvTensorRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RecvTensorRequest proto.InternalMessageInfo + +func (m *RecvTensorRequest) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *RecvTensorRequest) GetRendezvousKey() string { + if m != nil { + return m.RendezvousKey + } + return "" +} + +func (m *RecvTensorRequest) GetDmaOk() bool { + if m != nil { + return m.DmaOk + } + return false +} + +func (m *RecvTensorRequest) GetClientLocality() *device_attributes_go_proto.DeviceLocality { + if m != nil { + return m.ClientLocality + } + return nil +} + +func (m *RecvTensorRequest) GetServerLocality() *device_attributes_go_proto.DeviceLocality { + if m != nil { + return m.ServerLocality + } + return nil +} + +func (m *RecvTensorRequest) GetTransportOptions() *any.Any { + if m != nil { + return m.TransportOptions + } + return nil +} + +func (m *RecvTensorRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type RecvTensorResponse struct { + // The tensor as a proto. + Tensor *tensor_go_proto.TensorProto `protobuf:"bytes,1,opt,name=tensor,proto3" json:"tensor,omitempty"` + // If true, this tensor was the output of a dead node, and the + // content is invalid. + IsDead bool `protobuf:"varint,2,opt,name=is_dead,json=isDead,proto3" json:"is_dead,omitempty"` + // The time at which tensor was available and started to be returned. + SendStartMicros int64 `protobuf:"varint,3,opt,name=send_start_micros,json=sendStartMicros,proto3" json:"send_start_micros,omitempty"` + // Optional additional information about how to receive the tensor, + // e.g. in the event that `RecvTensorRequest.dma_ok` was true. + TransportOptions *any.Any `protobuf:"bytes,4,opt,name=transport_options,json=transportOptions,proto3" json:"transport_options,omitempty"` + // Whether the receiver should send a MarkRecvFinishedRequest to the sender + // to ack the message. + RequireAck bool `protobuf:"varint,5,opt,name=require_ack,json=requireAck,proto3" json:"require_ack,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RecvTensorResponse) Reset() { *m = RecvTensorResponse{} } +func (m *RecvTensorResponse) String() string { return proto.CompactTextString(m) } +func (*RecvTensorResponse) ProtoMessage() {} +func (*RecvTensorResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{18} +} + +func (m *RecvTensorResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RecvTensorResponse.Unmarshal(m, b) +} +func (m *RecvTensorResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RecvTensorResponse.Marshal(b, m, deterministic) +} +func (m *RecvTensorResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RecvTensorResponse.Merge(m, src) +} +func (m *RecvTensorResponse) XXX_Size() int { + return xxx_messageInfo_RecvTensorResponse.Size(m) +} +func (m *RecvTensorResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RecvTensorResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RecvTensorResponse proto.InternalMessageInfo + +func (m *RecvTensorResponse) GetTensor() *tensor_go_proto.TensorProto { + if m != nil { + return m.Tensor + } + return nil +} + +func (m *RecvTensorResponse) GetIsDead() bool { + if m != nil { + return m.IsDead + } + return false +} + +func (m *RecvTensorResponse) GetSendStartMicros() int64 { + if m != nil { + return m.SendStartMicros + } + return 0 +} + +func (m *RecvTensorResponse) GetTransportOptions() *any.Any { + if m != nil { + return m.TransportOptions + } + return nil +} + +func (m *RecvTensorResponse) GetRequireAck() bool { + if m != nil { + return m.RequireAck + } + return false +} + +// Message for managing the response cache maintained on the sender side. +// Currently only used by the gRPC worker service. +type MarkRecvFinishedRequest struct { + RequestId int64 `protobuf:"varint,1,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MarkRecvFinishedRequest) Reset() { *m = MarkRecvFinishedRequest{} } +func (m *MarkRecvFinishedRequest) String() string { return proto.CompactTextString(m) } +func (*MarkRecvFinishedRequest) ProtoMessage() {} +func (*MarkRecvFinishedRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{19} +} + +func (m *MarkRecvFinishedRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MarkRecvFinishedRequest.Unmarshal(m, b) +} +func (m *MarkRecvFinishedRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MarkRecvFinishedRequest.Marshal(b, m, deterministic) +} +func (m *MarkRecvFinishedRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_MarkRecvFinishedRequest.Merge(m, src) +} +func (m *MarkRecvFinishedRequest) XXX_Size() int { + return xxx_messageInfo_MarkRecvFinishedRequest.Size(m) +} +func (m *MarkRecvFinishedRequest) XXX_DiscardUnknown() { + xxx_messageInfo_MarkRecvFinishedRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_MarkRecvFinishedRequest proto.InternalMessageInfo + +func (m *MarkRecvFinishedRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +type MarkRecvFinishedResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *MarkRecvFinishedResponse) Reset() { *m = MarkRecvFinishedResponse{} } +func (m *MarkRecvFinishedResponse) String() string { return proto.CompactTextString(m) } +func (*MarkRecvFinishedResponse) ProtoMessage() {} +func (*MarkRecvFinishedResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{20} +} + +func (m *MarkRecvFinishedResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_MarkRecvFinishedResponse.Unmarshal(m, b) +} +func (m *MarkRecvFinishedResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_MarkRecvFinishedResponse.Marshal(b, m, deterministic) +} +func (m *MarkRecvFinishedResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_MarkRecvFinishedResponse.Merge(m, src) +} +func (m *MarkRecvFinishedResponse) XXX_Size() int { + return xxx_messageInfo_MarkRecvFinishedResponse.Size(m) +} +func (m *MarkRecvFinishedResponse) XXX_DiscardUnknown() { + xxx_messageInfo_MarkRecvFinishedResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_MarkRecvFinishedResponse proto.InternalMessageInfo + +// Out-of-band request to begin or end logging, or +// to retrieve logs for particular steps. +type LoggingRequest struct { + // If true, RPC logging will be enabled. + EnableRpcLogging bool `protobuf:"varint,1,opt,name=enable_rpc_logging,json=enableRpcLogging,proto3" json:"enable_rpc_logging,omitempty"` + // If true, RPC logging will be disabled. + DisableRpcLogging bool `protobuf:"varint,4,opt,name=disable_rpc_logging,json=disableRpcLogging,proto3" json:"disable_rpc_logging,omitempty"` + // If true, discard any saved logging data (for all steps). + Clear bool `protobuf:"varint,2,opt,name=clear,proto3" json:"clear,omitempty"` + // When set, requests all saved log data pertaining to the step. + // Any log data retrieved is eliminated from the store and cannot be + // retrieved again. + FetchStepId []int64 `protobuf:"varint,3,rep,packed,name=fetch_step_id,json=fetchStepId,proto3" json:"fetch_step_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *LoggingRequest) Reset() { *m = LoggingRequest{} } +func (m *LoggingRequest) String() string { return proto.CompactTextString(m) } +func (*LoggingRequest) ProtoMessage() {} +func (*LoggingRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{21} +} + +func (m *LoggingRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_LoggingRequest.Unmarshal(m, b) +} +func (m *LoggingRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_LoggingRequest.Marshal(b, m, deterministic) +} +func (m *LoggingRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_LoggingRequest.Merge(m, src) +} +func (m *LoggingRequest) XXX_Size() int { + return xxx_messageInfo_LoggingRequest.Size(m) +} +func (m *LoggingRequest) XXX_DiscardUnknown() { + xxx_messageInfo_LoggingRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_LoggingRequest proto.InternalMessageInfo + +func (m *LoggingRequest) GetEnableRpcLogging() bool { + if m != nil { + return m.EnableRpcLogging + } + return false +} + +func (m *LoggingRequest) GetDisableRpcLogging() bool { + if m != nil { + return m.DisableRpcLogging + } + return false +} + +func (m *LoggingRequest) GetClear() bool { + if m != nil { + return m.Clear + } + return false +} + +func (m *LoggingRequest) GetFetchStepId() []int64 { + if m != nil { + return m.FetchStepId + } + return nil +} + +type LabeledStepStats struct { + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + StepStats *step_stats_go_proto.StepStats `protobuf:"bytes,2,opt,name=step_stats,json=stepStats,proto3" json:"step_stats,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *LabeledStepStats) Reset() { *m = LabeledStepStats{} } +func (m *LabeledStepStats) String() string { return proto.CompactTextString(m) } +func (*LabeledStepStats) ProtoMessage() {} +func (*LabeledStepStats) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{22} +} + +func (m *LabeledStepStats) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_LabeledStepStats.Unmarshal(m, b) +} +func (m *LabeledStepStats) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_LabeledStepStats.Marshal(b, m, deterministic) +} +func (m *LabeledStepStats) XXX_Merge(src proto.Message) { + xxx_messageInfo_LabeledStepStats.Merge(m, src) +} +func (m *LabeledStepStats) XXX_Size() int { + return xxx_messageInfo_LabeledStepStats.Size(m) +} +func (m *LabeledStepStats) XXX_DiscardUnknown() { + xxx_messageInfo_LabeledStepStats.DiscardUnknown(m) +} + +var xxx_messageInfo_LabeledStepStats proto.InternalMessageInfo + +func (m *LabeledStepStats) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *LabeledStepStats) GetStepStats() *step_stats_go_proto.StepStats { + if m != nil { + return m.StepStats + } + return nil +} + +type LoggingResponse struct { + Step []*LabeledStepStats `protobuf:"bytes,1,rep,name=step,proto3" json:"step,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *LoggingResponse) Reset() { *m = LoggingResponse{} } +func (m *LoggingResponse) String() string { return proto.CompactTextString(m) } +func (*LoggingResponse) ProtoMessage() {} +func (*LoggingResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{23} +} + +func (m *LoggingResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_LoggingResponse.Unmarshal(m, b) +} +func (m *LoggingResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_LoggingResponse.Marshal(b, m, deterministic) +} +func (m *LoggingResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_LoggingResponse.Merge(m, src) +} +func (m *LoggingResponse) XXX_Size() int { + return xxx_messageInfo_LoggingResponse.Size(m) +} +func (m *LoggingResponse) XXX_DiscardUnknown() { + xxx_messageInfo_LoggingResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_LoggingResponse proto.InternalMessageInfo + +func (m *LoggingResponse) GetStep() []*LabeledStepStats { + if m != nil { + return m.Step + } + return nil +} + +type TraceOpts struct { + // Length of the trace to be taken, in seconds. + Duration float64 `protobuf:"fixed64,1,opt,name=duration,proto3" json:"duration,omitempty"` + // If true, capture step profile locally in each worker. Currently + // unimplemented. + UseStepProfiler bool `protobuf:"varint,2,opt,name=use_step_profiler,json=useStepProfiler,proto3" json:"use_step_profiler,omitempty"` + // If true, capture kernel events from each worker. + UseKernelProfiler bool `protobuf:"varint,3,opt,name=use_kernel_profiler,json=useKernelProfiler,proto3" json:"use_kernel_profiler,omitempty"` + // If true, capture extended profiling events from TensorFlow process. + UseExtendedProfiler bool `protobuf:"varint,4,opt,name=use_extended_profiler,json=useExtendedProfiler,proto3" json:"use_extended_profiler,omitempty"` + // If true, capture GPU profiling events locally on each + // machine. Currently unimplemented. + UseGpuProfiler bool `protobuf:"varint,5,opt,name=use_gpu_profiler,json=useGpuProfiler,proto3" json:"use_gpu_profiler,omitempty"` + // If true, collect sampled profile events. Currently unimplemented. + UseSampleProfiler bool `protobuf:"varint,6,opt,name=use_sample_profiler,json=useSampleProfiler,proto3" json:"use_sample_profiler,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TraceOpts) Reset() { *m = TraceOpts{} } +func (m *TraceOpts) String() string { return proto.CompactTextString(m) } +func (*TraceOpts) ProtoMessage() {} +func (*TraceOpts) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{24} +} + +func (m *TraceOpts) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TraceOpts.Unmarshal(m, b) +} +func (m *TraceOpts) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TraceOpts.Marshal(b, m, deterministic) +} +func (m *TraceOpts) XXX_Merge(src proto.Message) { + xxx_messageInfo_TraceOpts.Merge(m, src) +} +func (m *TraceOpts) XXX_Size() int { + return xxx_messageInfo_TraceOpts.Size(m) +} +func (m *TraceOpts) XXX_DiscardUnknown() { + xxx_messageInfo_TraceOpts.DiscardUnknown(m) +} + +var xxx_messageInfo_TraceOpts proto.InternalMessageInfo + +func (m *TraceOpts) GetDuration() float64 { + if m != nil { + return m.Duration + } + return 0 +} + +func (m *TraceOpts) GetUseStepProfiler() bool { + if m != nil { + return m.UseStepProfiler + } + return false +} + +func (m *TraceOpts) GetUseKernelProfiler() bool { + if m != nil { + return m.UseKernelProfiler + } + return false +} + +func (m *TraceOpts) GetUseExtendedProfiler() bool { + if m != nil { + return m.UseExtendedProfiler + } + return false +} + +func (m *TraceOpts) GetUseGpuProfiler() bool { + if m != nil { + return m.UseGpuProfiler + } + return false +} + +func (m *TraceOpts) GetUseSampleProfiler() bool { + if m != nil { + return m.UseSampleProfiler + } + return false +} + +// Out-of-band request to configure distributed tracing. +type TracingRequest struct { + Options *TraceOpts `protobuf:"bytes,1,opt,name=options,proto3" json:"options,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TracingRequest) Reset() { *m = TracingRequest{} } +func (m *TracingRequest) String() string { return proto.CompactTextString(m) } +func (*TracingRequest) ProtoMessage() {} +func (*TracingRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{25} +} + +func (m *TracingRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TracingRequest.Unmarshal(m, b) +} +func (m *TracingRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TracingRequest.Marshal(b, m, deterministic) +} +func (m *TracingRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_TracingRequest.Merge(m, src) +} +func (m *TracingRequest) XXX_Size() int { + return xxx_messageInfo_TracingRequest.Size(m) +} +func (m *TracingRequest) XXX_DiscardUnknown() { + xxx_messageInfo_TracingRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_TracingRequest proto.InternalMessageInfo + +func (m *TracingRequest) GetOptions() *TraceOpts { + if m != nil { + return m.Options + } + return nil +} + +type TracingResponse struct { + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TracingResponse) Reset() { *m = TracingResponse{} } +func (m *TracingResponse) String() string { return proto.CompactTextString(m) } +func (*TracingResponse) ProtoMessage() {} +func (*TracingResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{26} +} + +func (m *TracingResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TracingResponse.Unmarshal(m, b) +} +func (m *TracingResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TracingResponse.Marshal(b, m, deterministic) +} +func (m *TracingResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_TracingResponse.Merge(m, src) +} +func (m *TracingResponse) XXX_Size() int { + return xxx_messageInfo_TracingResponse.Size(m) +} +func (m *TracingResponse) XXX_DiscardUnknown() { + xxx_messageInfo_TracingResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_TracingResponse proto.InternalMessageInfo + +type RecvBufRequest struct { + // Used at server side to find the correct BufRendezvous. + StepId int64 `protobuf:"varint,1,opt,name=step_id,json=stepId,proto3" json:"step_id,omitempty"` + // Arbitrary string identifying a BufRendezvous entry. + BufRendezvousKey string `protobuf:"bytes,2,opt,name=buf_rendezvous_key,json=bufRendezvousKey,proto3" json:"buf_rendezvous_key,omitempty"` + // Size of value expected, must agree with BufRendezvous entry. + NumBytes int64 `protobuf:"varint,3,opt,name=num_bytes,json=numBytes,proto3" json:"num_bytes,omitempty"` + // When RDMA is in use, address of destination field on client. + BufPtr uint64 `protobuf:"fixed64,4,opt,name=buf_ptr,json=bufPtr,proto3" json:"buf_ptr,omitempty"` + // Optional information on client-side device locality. + ClientLocality *device_attributes_go_proto.DeviceLocality `protobuf:"bytes,5,opt,name=client_locality,json=clientLocality,proto3" json:"client_locality,omitempty"` + // Optional information on server-side device locality. + ServerLocality *device_attributes_go_proto.DeviceLocality `protobuf:"bytes,6,opt,name=server_locality,json=serverLocality,proto3" json:"server_locality,omitempty"` + // Optional, implementation-specific data. + TransportOptions *any.Any `protobuf:"bytes,7,opt,name=transport_options,json=transportOptions,proto3" json:"transport_options,omitempty"` + // For annotating timeline and device incarnation check. + SrcDevice string `protobuf:"bytes,8,opt,name=src_device,json=srcDevice,proto3" json:"src_device,omitempty"` + // Optional, for annotating the timeline. + DstDevice string `protobuf:"bytes,9,opt,name=dst_device,json=dstDevice,proto3" json:"dst_device,omitempty"` + // Depending on the RPC system in use, it may be necessary to set this + // id to detect resends of RPCs where the server is not aware that + // the prior RPC failed. + RequestId int64 `protobuf:"varint,10,opt,name=request_id,json=requestId,proto3" json:"request_id,omitempty"` + // Incarnation number of the source device, used to detect worker failures. + SrcIncarnation uint64 `protobuf:"varint,11,opt,name=src_incarnation,json=srcIncarnation,proto3" json:"src_incarnation,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RecvBufRequest) Reset() { *m = RecvBufRequest{} } +func (m *RecvBufRequest) String() string { return proto.CompactTextString(m) } +func (*RecvBufRequest) ProtoMessage() {} +func (*RecvBufRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{27} +} + +func (m *RecvBufRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RecvBufRequest.Unmarshal(m, b) +} +func (m *RecvBufRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RecvBufRequest.Marshal(b, m, deterministic) +} +func (m *RecvBufRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_RecvBufRequest.Merge(m, src) +} +func (m *RecvBufRequest) XXX_Size() int { + return xxx_messageInfo_RecvBufRequest.Size(m) +} +func (m *RecvBufRequest) XXX_DiscardUnknown() { + xxx_messageInfo_RecvBufRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_RecvBufRequest proto.InternalMessageInfo + +func (m *RecvBufRequest) GetStepId() int64 { + if m != nil { + return m.StepId + } + return 0 +} + +func (m *RecvBufRequest) GetBufRendezvousKey() string { + if m != nil { + return m.BufRendezvousKey + } + return "" +} + +func (m *RecvBufRequest) GetNumBytes() int64 { + if m != nil { + return m.NumBytes + } + return 0 +} + +func (m *RecvBufRequest) GetBufPtr() uint64 { + if m != nil { + return m.BufPtr + } + return 0 +} + +func (m *RecvBufRequest) GetClientLocality() *device_attributes_go_proto.DeviceLocality { + if m != nil { + return m.ClientLocality + } + return nil +} + +func (m *RecvBufRequest) GetServerLocality() *device_attributes_go_proto.DeviceLocality { + if m != nil { + return m.ServerLocality + } + return nil +} + +func (m *RecvBufRequest) GetTransportOptions() *any.Any { + if m != nil { + return m.TransportOptions + } + return nil +} + +func (m *RecvBufRequest) GetSrcDevice() string { + if m != nil { + return m.SrcDevice + } + return "" +} + +func (m *RecvBufRequest) GetDstDevice() string { + if m != nil { + return m.DstDevice + } + return "" +} + +func (m *RecvBufRequest) GetRequestId() int64 { + if m != nil { + return m.RequestId + } + return 0 +} + +func (m *RecvBufRequest) GetSrcIncarnation() uint64 { + if m != nil { + return m.SrcIncarnation + } + return 0 +} + +type RecvBufResponse struct { + BufPtr uint64 `protobuf:"fixed64,1,opt,name=buf_ptr,json=bufPtr,proto3" json:"buf_ptr,omitempty"` + NumBytes int64 `protobuf:"varint,2,opt,name=num_bytes,json=numBytes,proto3" json:"num_bytes,omitempty"` + IsDead bool `protobuf:"varint,3,opt,name=is_dead,json=isDead,proto3" json:"is_dead,omitempty"` + // Optional, implementation-specific data. + TransportOptions *any.Any `protobuf:"bytes,4,opt,name=transport_options,json=transportOptions,proto3" json:"transport_options,omitempty"` + // Optional, for timeline. + SendStartMicros int64 `protobuf:"varint,5,opt,name=send_start_micros,json=sendStartMicros,proto3" json:"send_start_micros,omitempty"` + // Whether the receiver should send a MarkRecvFinishedRequest to the sender + // to ack the message. + RequireAck bool `protobuf:"varint,6,opt,name=require_ack,json=requireAck,proto3" json:"require_ack,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *RecvBufResponse) Reset() { *m = RecvBufResponse{} } +func (m *RecvBufResponse) String() string { return proto.CompactTextString(m) } +func (*RecvBufResponse) ProtoMessage() {} +func (*RecvBufResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{28} +} + +func (m *RecvBufResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_RecvBufResponse.Unmarshal(m, b) +} +func (m *RecvBufResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_RecvBufResponse.Marshal(b, m, deterministic) +} +func (m *RecvBufResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_RecvBufResponse.Merge(m, src) +} +func (m *RecvBufResponse) XXX_Size() int { + return xxx_messageInfo_RecvBufResponse.Size(m) +} +func (m *RecvBufResponse) XXX_DiscardUnknown() { + xxx_messageInfo_RecvBufResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_RecvBufResponse proto.InternalMessageInfo + +func (m *RecvBufResponse) GetBufPtr() uint64 { + if m != nil { + return m.BufPtr + } + return 0 +} + +func (m *RecvBufResponse) GetNumBytes() int64 { + if m != nil { + return m.NumBytes + } + return 0 +} + +func (m *RecvBufResponse) GetIsDead() bool { + if m != nil { + return m.IsDead + } + return false +} + +func (m *RecvBufResponse) GetTransportOptions() *any.Any { + if m != nil { + return m.TransportOptions + } + return nil +} + +func (m *RecvBufResponse) GetSendStartMicros() int64 { + if m != nil { + return m.SendStartMicros + } + return 0 +} + +func (m *RecvBufResponse) GetRequireAck() bool { + if m != nil { + return m.RequireAck + } + return false +} + +// Supplies one or more device names as members of the group identified by +// group_key. Service will respond when all group_size devices become known. +// All devices in group must have same type. +type CompleteGroupRequest struct { + GroupKey int32 `protobuf:"varint,1,opt,name=group_key,json=groupKey,proto3" json:"group_key,omitempty"` + GroupSize int32 `protobuf:"varint,2,opt,name=group_size,json=groupSize,proto3" json:"group_size,omitempty"` + DeviceType string `protobuf:"bytes,3,opt,name=device_type,json=deviceType,proto3" json:"device_type,omitempty"` + CollectiveType int32 `protobuf:"varint,5,opt,name=collective_type,json=collectiveType,proto3" json:"collective_type,omitempty"` + DeviceAttributes *device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,6,opt,name=device_attributes,json=deviceAttributes,proto3" json:"device_attributes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CompleteGroupRequest) Reset() { *m = CompleteGroupRequest{} } +func (m *CompleteGroupRequest) String() string { return proto.CompactTextString(m) } +func (*CompleteGroupRequest) ProtoMessage() {} +func (*CompleteGroupRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{29} +} + +func (m *CompleteGroupRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CompleteGroupRequest.Unmarshal(m, b) +} +func (m *CompleteGroupRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CompleteGroupRequest.Marshal(b, m, deterministic) +} +func (m *CompleteGroupRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CompleteGroupRequest.Merge(m, src) +} +func (m *CompleteGroupRequest) XXX_Size() int { + return xxx_messageInfo_CompleteGroupRequest.Size(m) +} +func (m *CompleteGroupRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CompleteGroupRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CompleteGroupRequest proto.InternalMessageInfo + +func (m *CompleteGroupRequest) GetGroupKey() int32 { + if m != nil { + return m.GroupKey + } + return 0 +} + +func (m *CompleteGroupRequest) GetGroupSize() int32 { + if m != nil { + return m.GroupSize + } + return 0 +} + +func (m *CompleteGroupRequest) GetDeviceType() string { + if m != nil { + return m.DeviceType + } + return "" +} + +func (m *CompleteGroupRequest) GetCollectiveType() int32 { + if m != nil { + return m.CollectiveType + } + return 0 +} + +func (m *CompleteGroupRequest) GetDeviceAttributes() *device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.DeviceAttributes + } + return nil +} + +// Gives the complete membership of the group identified by group_key. +type CompleteGroupResponse struct { + GroupKey int32 `protobuf:"varint,1,opt,name=group_key,json=groupKey,proto3" json:"group_key,omitempty"` + GroupSize int32 `protobuf:"varint,2,opt,name=group_size,json=groupSize,proto3" json:"group_size,omitempty"` + DeviceType string `protobuf:"bytes,3,opt,name=device_type,json=deviceType,proto3" json:"device_type,omitempty"` + NumTasks int32 `protobuf:"varint,4,opt,name=num_tasks,json=numTasks,proto3" json:"num_tasks,omitempty"` + CommunicatorKey []byte `protobuf:"bytes,7,opt,name=communicator_key,json=communicatorKey,proto3" json:"communicator_key,omitempty"` + DeviceAttributes []*device_attributes_go_proto.DeviceAttributes `protobuf:"bytes,8,rep,name=device_attributes,json=deviceAttributes,proto3" json:"device_attributes,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CompleteGroupResponse) Reset() { *m = CompleteGroupResponse{} } +func (m *CompleteGroupResponse) String() string { return proto.CompactTextString(m) } +func (*CompleteGroupResponse) ProtoMessage() {} +func (*CompleteGroupResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{30} +} + +func (m *CompleteGroupResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CompleteGroupResponse.Unmarshal(m, b) +} +func (m *CompleteGroupResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CompleteGroupResponse.Marshal(b, m, deterministic) +} +func (m *CompleteGroupResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CompleteGroupResponse.Merge(m, src) +} +func (m *CompleteGroupResponse) XXX_Size() int { + return xxx_messageInfo_CompleteGroupResponse.Size(m) +} +func (m *CompleteGroupResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CompleteGroupResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CompleteGroupResponse proto.InternalMessageInfo + +func (m *CompleteGroupResponse) GetGroupKey() int32 { + if m != nil { + return m.GroupKey + } + return 0 +} + +func (m *CompleteGroupResponse) GetGroupSize() int32 { + if m != nil { + return m.GroupSize + } + return 0 +} + +func (m *CompleteGroupResponse) GetDeviceType() string { + if m != nil { + return m.DeviceType + } + return "" +} + +func (m *CompleteGroupResponse) GetNumTasks() int32 { + if m != nil { + return m.NumTasks + } + return 0 +} + +func (m *CompleteGroupResponse) GetCommunicatorKey() []byte { + if m != nil { + return m.CommunicatorKey + } + return nil +} + +func (m *CompleteGroupResponse) GetDeviceAttributes() []*device_attributes_go_proto.DeviceAttributes { + if m != nil { + return m.DeviceAttributes + } + return nil +} + +// Supplies data about one collective op belonging to the instance identified +// by instance_key. Service will respond when all group_size ops have +// become known. Most of the data being sent is for correctness checking, +// to ensure that all ops in the instance share common attributes. +type CompleteInstanceRequest struct { + Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` + Type int32 `protobuf:"varint,2,opt,name=type,proto3" json:"type,omitempty"` + DataType types_go_proto.DataType `protobuf:"varint,3,opt,name=data_type,json=dataType,proto3,enum=tensorflow.DataType" json:"data_type,omitempty"` + Shape *tensor_shape_go_proto.TensorShapeProto `protobuf:"bytes,4,opt,name=shape,proto3" json:"shape,omitempty"` + GroupKey int32 `protobuf:"varint,5,opt,name=group_key,json=groupKey,proto3" json:"group_key,omitempty"` + GroupSize int32 `protobuf:"varint,6,opt,name=group_size,json=groupSize,proto3" json:"group_size,omitempty"` + InstanceKey int32 `protobuf:"varint,7,opt,name=instance_key,json=instanceKey,proto3" json:"instance_key,omitempty"` + DeviceType string `protobuf:"bytes,8,opt,name=device_type,json=deviceType,proto3" json:"device_type,omitempty"` + SubdivOffset []int32 `protobuf:"varint,9,rep,packed,name=subdiv_offset,json=subdivOffset,proto3" json:"subdiv_offset,omitempty"` + Device string `protobuf:"bytes,10,opt,name=device,proto3" json:"device,omitempty"` + IsSource bool `protobuf:"varint,11,opt,name=is_source,json=isSource,proto3" json:"is_source,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CompleteInstanceRequest) Reset() { *m = CompleteInstanceRequest{} } +func (m *CompleteInstanceRequest) String() string { return proto.CompactTextString(m) } +func (*CompleteInstanceRequest) ProtoMessage() {} +func (*CompleteInstanceRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{31} +} + +func (m *CompleteInstanceRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CompleteInstanceRequest.Unmarshal(m, b) +} +func (m *CompleteInstanceRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CompleteInstanceRequest.Marshal(b, m, deterministic) +} +func (m *CompleteInstanceRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_CompleteInstanceRequest.Merge(m, src) +} +func (m *CompleteInstanceRequest) XXX_Size() int { + return xxx_messageInfo_CompleteInstanceRequest.Size(m) +} +func (m *CompleteInstanceRequest) XXX_DiscardUnknown() { + xxx_messageInfo_CompleteInstanceRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_CompleteInstanceRequest proto.InternalMessageInfo + +func (m *CompleteInstanceRequest) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func (m *CompleteInstanceRequest) GetType() int32 { + if m != nil { + return m.Type + } + return 0 +} + +func (m *CompleteInstanceRequest) GetDataType() types_go_proto.DataType { + if m != nil { + return m.DataType + } + return types_go_proto.DataType_DT_INVALID +} + +func (m *CompleteInstanceRequest) GetShape() *tensor_shape_go_proto.TensorShapeProto { + if m != nil { + return m.Shape + } + return nil +} + +func (m *CompleteInstanceRequest) GetGroupKey() int32 { + if m != nil { + return m.GroupKey + } + return 0 +} + +func (m *CompleteInstanceRequest) GetGroupSize() int32 { + if m != nil { + return m.GroupSize + } + return 0 +} + +func (m *CompleteInstanceRequest) GetInstanceKey() int32 { + if m != nil { + return m.InstanceKey + } + return 0 +} + +func (m *CompleteInstanceRequest) GetDeviceType() string { + if m != nil { + return m.DeviceType + } + return "" +} + +func (m *CompleteInstanceRequest) GetSubdivOffset() []int32 { + if m != nil { + return m.SubdivOffset + } + return nil +} + +func (m *CompleteInstanceRequest) GetDevice() string { + if m != nil { + return m.Device + } + return "" +} + +func (m *CompleteInstanceRequest) GetIsSource() bool { + if m != nil { + return m.IsSource + } + return false +} + +// Confirms that every op in the instance has consistently declared itself. +// Also gives the source_rank in case of broadcast. +type CompleteInstanceResponse struct { + InstanceKey int32 `protobuf:"varint,1,opt,name=instance_key,json=instanceKey,proto3" json:"instance_key,omitempty"` + SourceRank int32 `protobuf:"varint,2,opt,name=source_rank,json=sourceRank,proto3" json:"source_rank,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *CompleteInstanceResponse) Reset() { *m = CompleteInstanceResponse{} } +func (m *CompleteInstanceResponse) String() string { return proto.CompactTextString(m) } +func (*CompleteInstanceResponse) ProtoMessage() {} +func (*CompleteInstanceResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{32} +} + +func (m *CompleteInstanceResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_CompleteInstanceResponse.Unmarshal(m, b) +} +func (m *CompleteInstanceResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_CompleteInstanceResponse.Marshal(b, m, deterministic) +} +func (m *CompleteInstanceResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_CompleteInstanceResponse.Merge(m, src) +} +func (m *CompleteInstanceResponse) XXX_Size() int { + return xxx_messageInfo_CompleteInstanceResponse.Size(m) +} +func (m *CompleteInstanceResponse) XXX_DiscardUnknown() { + xxx_messageInfo_CompleteInstanceResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_CompleteInstanceResponse proto.InternalMessageInfo + +func (m *CompleteInstanceResponse) GetInstanceKey() int32 { + if m != nil { + return m.InstanceKey + } + return 0 +} + +func (m *CompleteInstanceResponse) GetSourceRank() int32 { + if m != nil { + return m.SourceRank + } + return 0 +} + +// Request for next agreed-upon step_id for the specified graph_keys. +// This is used to enable multiple graphs containing nodes from +// a common collective instance to coordinate using the same step_ids. +type GetStepSequenceRequest struct { + GraphKey []int64 `protobuf:"varint,1,rep,packed,name=graph_key,json=graphKey,proto3" json:"graph_key,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GetStepSequenceRequest) Reset() { *m = GetStepSequenceRequest{} } +func (m *GetStepSequenceRequest) String() string { return proto.CompactTextString(m) } +func (*GetStepSequenceRequest) ProtoMessage() {} +func (*GetStepSequenceRequest) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{33} +} + +func (m *GetStepSequenceRequest) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GetStepSequenceRequest.Unmarshal(m, b) +} +func (m *GetStepSequenceRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GetStepSequenceRequest.Marshal(b, m, deterministic) +} +func (m *GetStepSequenceRequest) XXX_Merge(src proto.Message) { + xxx_messageInfo_GetStepSequenceRequest.Merge(m, src) +} +func (m *GetStepSequenceRequest) XXX_Size() int { + return xxx_messageInfo_GetStepSequenceRequest.Size(m) +} +func (m *GetStepSequenceRequest) XXX_DiscardUnknown() { + xxx_messageInfo_GetStepSequenceRequest.DiscardUnknown(m) +} + +var xxx_messageInfo_GetStepSequenceRequest proto.InternalMessageInfo + +func (m *GetStepSequenceRequest) GetGraphKey() []int64 { + if m != nil { + return m.GraphKey + } + return nil +} + +type StepSequence struct { + GraphKey int64 `protobuf:"varint,1,opt,name=graph_key,json=graphKey,proto3" json:"graph_key,omitempty"` + NextStepId int64 `protobuf:"varint,2,opt,name=next_step_id,json=nextStepId,proto3" json:"next_step_id,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *StepSequence) Reset() { *m = StepSequence{} } +func (m *StepSequence) String() string { return proto.CompactTextString(m) } +func (*StepSequence) ProtoMessage() {} +func (*StepSequence) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{34} +} + +func (m *StepSequence) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_StepSequence.Unmarshal(m, b) +} +func (m *StepSequence) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_StepSequence.Marshal(b, m, deterministic) +} +func (m *StepSequence) XXX_Merge(src proto.Message) { + xxx_messageInfo_StepSequence.Merge(m, src) +} +func (m *StepSequence) XXX_Size() int { + return xxx_messageInfo_StepSequence.Size(m) +} +func (m *StepSequence) XXX_DiscardUnknown() { + xxx_messageInfo_StepSequence.DiscardUnknown(m) +} + +var xxx_messageInfo_StepSequence proto.InternalMessageInfo + +func (m *StepSequence) GetGraphKey() int64 { + if m != nil { + return m.GraphKey + } + return 0 +} + +func (m *StepSequence) GetNextStepId() int64 { + if m != nil { + return m.NextStepId + } + return 0 +} + +// Next valid step_ids for one or more graph_keys. +type GetStepSequenceResponse struct { + StepSequence []*StepSequence `protobuf:"bytes,1,rep,name=step_sequence,json=stepSequence,proto3" json:"step_sequence,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *GetStepSequenceResponse) Reset() { *m = GetStepSequenceResponse{} } +func (m *GetStepSequenceResponse) String() string { return proto.CompactTextString(m) } +func (*GetStepSequenceResponse) ProtoMessage() {} +func (*GetStepSequenceResponse) Descriptor() ([]byte, []int) { + return fileDescriptor_f24b6dc95cbd078c, []int{35} +} + +func (m *GetStepSequenceResponse) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_GetStepSequenceResponse.Unmarshal(m, b) +} +func (m *GetStepSequenceResponse) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_GetStepSequenceResponse.Marshal(b, m, deterministic) +} +func (m *GetStepSequenceResponse) XXX_Merge(src proto.Message) { + xxx_messageInfo_GetStepSequenceResponse.Merge(m, src) +} +func (m *GetStepSequenceResponse) XXX_Size() int { + return xxx_messageInfo_GetStepSequenceResponse.Size(m) +} +func (m *GetStepSequenceResponse) XXX_DiscardUnknown() { + xxx_messageInfo_GetStepSequenceResponse.DiscardUnknown(m) +} + +var xxx_messageInfo_GetStepSequenceResponse proto.InternalMessageInfo + +func (m *GetStepSequenceResponse) GetStepSequence() []*StepSequence { + if m != nil { + return m.StepSequence + } + return nil +} + +func init() { + proto.RegisterType((*GetStatusRequest)(nil), "tensorflow.GetStatusRequest") + proto.RegisterType((*GetStatusResponse)(nil), "tensorflow.GetStatusResponse") + proto.RegisterType((*CreateWorkerSessionRequest)(nil), "tensorflow.CreateWorkerSessionRequest") + proto.RegisterType((*CreateWorkerSessionResponse)(nil), "tensorflow.CreateWorkerSessionResponse") + proto.RegisterType((*DeleteWorkerSessionRequest)(nil), "tensorflow.DeleteWorkerSessionRequest") + proto.RegisterType((*DeleteWorkerSessionResponse)(nil), "tensorflow.DeleteWorkerSessionResponse") + proto.RegisterType((*RegisterGraphRequest)(nil), "tensorflow.RegisterGraphRequest") + proto.RegisterType((*RegisterGraphResponse)(nil), "tensorflow.RegisterGraphResponse") + proto.RegisterType((*DeregisterGraphRequest)(nil), "tensorflow.DeregisterGraphRequest") + proto.RegisterType((*DeregisterGraphResponse)(nil), "tensorflow.DeregisterGraphResponse") + proto.RegisterType((*CleanupAllRequest)(nil), "tensorflow.CleanupAllRequest") + proto.RegisterType((*CleanupAllResponse)(nil), "tensorflow.CleanupAllResponse") + proto.RegisterType((*ExecutorOpts)(nil), "tensorflow.ExecutorOpts") + proto.RegisterType((*RunGraphRequest)(nil), "tensorflow.RunGraphRequest") + proto.RegisterType((*RunGraphResponse)(nil), "tensorflow.RunGraphResponse") + proto.RegisterType((*CleanupGraphRequest)(nil), "tensorflow.CleanupGraphRequest") + proto.RegisterType((*CleanupGraphResponse)(nil), "tensorflow.CleanupGraphResponse") + proto.RegisterType((*RecvTensorRequest)(nil), "tensorflow.RecvTensorRequest") + proto.RegisterType((*RecvTensorResponse)(nil), "tensorflow.RecvTensorResponse") + proto.RegisterType((*MarkRecvFinishedRequest)(nil), "tensorflow.MarkRecvFinishedRequest") + proto.RegisterType((*MarkRecvFinishedResponse)(nil), "tensorflow.MarkRecvFinishedResponse") + proto.RegisterType((*LoggingRequest)(nil), "tensorflow.LoggingRequest") + proto.RegisterType((*LabeledStepStats)(nil), "tensorflow.LabeledStepStats") + proto.RegisterType((*LoggingResponse)(nil), "tensorflow.LoggingResponse") + proto.RegisterType((*TraceOpts)(nil), "tensorflow.TraceOpts") + proto.RegisterType((*TracingRequest)(nil), "tensorflow.TracingRequest") + proto.RegisterType((*TracingResponse)(nil), "tensorflow.TracingResponse") + proto.RegisterType((*RecvBufRequest)(nil), "tensorflow.RecvBufRequest") + proto.RegisterType((*RecvBufResponse)(nil), "tensorflow.RecvBufResponse") + proto.RegisterType((*CompleteGroupRequest)(nil), "tensorflow.CompleteGroupRequest") + proto.RegisterType((*CompleteGroupResponse)(nil), "tensorflow.CompleteGroupResponse") + proto.RegisterType((*CompleteInstanceRequest)(nil), "tensorflow.CompleteInstanceRequest") + proto.RegisterType((*CompleteInstanceResponse)(nil), "tensorflow.CompleteInstanceResponse") + proto.RegisterType((*GetStepSequenceRequest)(nil), "tensorflow.GetStepSequenceRequest") + proto.RegisterType((*StepSequence)(nil), "tensorflow.StepSequence") + proto.RegisterType((*GetStepSequenceResponse)(nil), "tensorflow.GetStepSequenceResponse") +} + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/worker.proto", fileDescriptor_f24b6dc95cbd078c) +} + +var fileDescriptor_f24b6dc95cbd078c = []byte{ + // 2286 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xb4, 0x58, 0xcd, 0x73, 0x1c, 0x39, + 0x15, 0xaf, 0xf6, 0x7c, 0xb8, 0xe7, 0x8d, 0x33, 0x1e, 0x2b, 0x76, 0x3c, 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b/tensorflow/go/core/protobuf/for_core_protos_go_proto/worker_service.pb.go new file mode 100644 index 0000000..247b985 --- /dev/null +++ b/tensorflow/go/core/protobuf/for_core_protos_go_proto/worker_service.pb.go @@ -0,0 +1,60 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/core/protobuf/worker_service.proto + +package for_core_protos_go_proto + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +func init() { + proto.RegisterFile("tensorflow/core/protobuf/worker_service.proto", fileDescriptor_cf631a047edac54b) +} + +var fileDescriptor_cf631a047edac54b = []byte{ + // 484 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x94, 0xdd, 0x6e, 0xd3, 0x40, + 0x10, 0x85, 0xe9, 0x0d, 0xa1, 0x23, 0xaa, 0xa0, 0xed, 0x5d, 0xda, 0x42, 0x69, 0xf9, 0xb9, 0x22, + 0x91, 0xe0, 0x09, 0xea, 0x04, 0x05, 0x04, 0x48, 0x25, 0x09, 0x42, 0x8a, 0x84, 0x22, 0xc7, 0x8c, + 0xb7, 0x16, 0xce, 0xae, 0x99, 0xdd, 0x6d, 0xdf, 0x81, 0xa7, 0xe5, 0x11, 0x2a, 0xdb, 0xbb, 0x89, + 0x37, 0xdd, 0x38, 0x57, 0xb1, 0xce, 0x39, 0xf3, 0xcd, 0x44, 0xf6, 0x0c, 0xbc, 0xd3, 0x28, 0x94, + 0xa4, 0x34, 0x97, 0x77, 0x83, 0x44, 0x12, 0x0e, 0x0a, 0x92, 0x5a, 0x2e, 0x4d, 0x3a, 0xb8, 0x93, + 0xf4, 0x07, 0x69, 0xa1, 0x90, 0x6e, 0xb3, 0x04, 0xfb, 0x95, 0xce, 0xba, 0x9b, 0x78, 0x9f, 0x53, + 0x91, 0xf4, 0x5e, 0xef, 0xa9, 0xaf, 0xeb, 0xde, 0xff, 0x3f, 0x84, 0xa3, 0x9f, 0x95, 0x30, 0xad, + 0x79, 0xec, 0x13, 0x1c, 0x8e, 0x51, 0x4f, 0x75, 0xac, 0x8d, 0x62, 0xa7, 0xfd, 0x06, 0x77, 0x2d, + 0x4f, 0xf0, 0xaf, 0x41, 0xa5, 0x7b, 0x67, 0x3b, 0x5c, 0x55, 0x48, 0xa1, 0x90, 0xa5, 0x70, 0x3c, + 0x24, 0x8c, 0x35, 0xba, 0x06, 0x4a, 0x65, 0x52, 0xb0, 0x37, 0xcd, 0xaa, 0x40, 0xc0, 0xd1, 0xdf, + 0xee, 0xcd, 0x6d, 0xfa, 0x8c, 0x30, 0xc7, 0xd6, 0x3e, 0x81, 0x40, 0xb0, 0x4f, 0x30, 0x67, 0xfb, + 0xcc, 0xe0, 0x68, 0x82, 0x3c, 0x53, 0x1a, 0x69, 0x4c, 0x71, 0x71, 0xc3, 0xce, 0x9b, 0x95, 0x9e, + 0xe5, 0xd8, 0x2f, 0x5b, 0x12, 0x96, 0x3a, 0x87, 0xee, 0x08, 0xc9, 0xe3, 0x5e, 0xf8, 0x13, 0x51, + 0x88, 0x7c, 0xd9, 0x9a, 0xb1, 0xec, 0x8f, 0xf0, 0x64, 0x62, 0x44, 0x0d, 0x3d, 0xf1, 0x46, 0xb1, + 0xaa, 0xa3, 0x9d, 0x86, 0x4d, 0x8b, 0xf9, 0x0e, 0x4f, 0x87, 0x39, 0xc6, 0xc2, 0x14, 0x35, 0xea, + 0x85, 0xf7, 0x66, 0x1a, 0x8e, 0xc3, 0x9d, 0xef, 0x0e, 0x58, 0xe4, 0x17, 0x00, 0xab, 0x5f, 0xe5, + 0x39, 0x3b, 0x0b, 0xe4, 0xaf, 0xf2, 0xdc, 0xe1, 0x9e, 0xef, 0xb2, 0x2d, 0xec, 0x1b, 0xc0, 0x04, + 0x93, 0xdb, 0x59, 0x15, 0xf2, 0x61, 0x1b, 0x3d, 0x08, 0x6b, 0xda, 0x35, 0xec, 0xe2, 0x11, 0x8b, + 0xa0, 0xf3, 0x55, 0x72, 0x9e, 0x09, 0xce, 0x7a, 0xcd, 0xb0, 0x15, 0x1d, 0xe8, 0x24, 0xe8, 0xd9, + 0x91, 0x22, 0xe8, 0xcc, 0x28, 0x4e, 0x1e, 0x30, 0xac, 0x18, 0x64, 0xac, 0x3d, 0xcb, 0x18, 0x41, + 0xa7, 0x9c, 0x2f, 0x32, 0xa9, 0xcf, 0xb0, 0x62, 0x90, 0xb1, 0xf6, 0xd6, 0xff, 0x66, 0x0e, 0xdd, + 0x6a, 0x35, 0xb1, 0x98, 0x96, 0x05, 0x22, 0x41, 0xff, 0xfb, 0xda, 0x32, 0x83, 0xdf, 0xd7, 0x83, + 0xcc, 0x66, 0x23, 0x86, 0x72, 0x55, 0x94, 0x2b, 0x33, 0x26, 0x69, 0x0a, 0x7f, 0x23, 0x3c, 0x2b, + 0xb8, 0x11, 0x5b, 0x09, 0x4b, 0xfd, 0x05, 0xcf, 0x9c, 0xf1, 0x59, 0x28, 0x1d, 0x97, 0x23, 0x5f, + 0x86, 0xca, 0x9c, 0xeb, 0xd8, 0xaf, 0xda, 0x43, 0x35, 0x3e, 0xfa, 0x77, 0x00, 0x3d, 0x49, 0xbc, + 0x99, 0xfd, 0x9d, 0x29, 0x4d, 0x46, 0xe8, 0x6c, 0x85, 0xd1, 0xb1, 0x77, 0x0e, 0xaf, 0xcb, 0x2b, + 0xa9, 0xae, 0x0f, 0xe6, 0x3f, 0x78, 0xa6, 0x6f, 0xcc, 0xb2, 0x9f, 0xc8, 0xd5, 0xa0, 0x71, 0x5a, + 0xc3, 0x8f, 0x5c, 0x6e, 0xdd, 0xdc, 0x54, 0xd2, 0xa2, 0x54, 0x16, 0x95, 0xa2, 0x16, 0x5c, 0xd6, + 0x4f, 0xcb, 0xc7, 0xd5, 0xcf, 0x87, 0xfb, 0x00, 0x00, 0x00, 0xff, 0xff, 0x89, 0xe1, 0x04, 0xae, + 0xef, 0x05, 0x00, 0x00, +} diff --git a/tensorflow/go/doc.go b/tensorflow/go/doc.go new file mode 100644 index 0000000..8d2a235 --- /dev/null +++ b/tensorflow/go/doc.go @@ -0,0 +1,26 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// Package tensorflow is a Go binding to TensorFlow. +// +// The API is subject to change and may break at any time. +// +// TensorFlow (www.tensorflow.org) is an open source software library for +// numerical computation using data flow graphs. This package provides +// functionality to build and execute such graphs and depends on +// TensorFlow being available. For installation instructions see +// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/README.md +package tensorflow diff --git a/tensorflow/go/example_inception_inference_test.go b/tensorflow/go/example_inception_inference_test.go new file mode 100644 index 0000000..f84a588 --- /dev/null +++ b/tensorflow/go/example_inception_inference_test.go @@ -0,0 +1,291 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow_test + +import ( + "archive/zip" + "bufio" + "flag" + "fmt" + "io" + "io/ioutil" + "log" + "net/http" + "os" + "path/filepath" + + tf "github.com/tensorflow/tensorflow/tensorflow/go" + "github.com/tensorflow/tensorflow/tensorflow/go/op" +) + +func Example() { + // An example for using the TensorFlow Go API for image recognition + // using a pre-trained inception model (http://arxiv.org/abs/1512.00567). + // + // Sample usage: -dir=/tmp/modeldir -image=/path/to/some/jpeg + // + // The pre-trained model takes input in the form of a 4-dimensional + // tensor with shape [ BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, 3 ], + // where: + // - BATCH_SIZE allows for inference of multiple images in one pass through the graph + // - IMAGE_HEIGHT is the height of the images on which the model was trained + // - IMAGE_WIDTH is the width of the images on which the model was trained + // - 3 is the (R, G, B) values of the pixel colors represented as a float. + // + // And produces as output a vector with shape [ NUM_LABELS ]. + // output[i] is the probability that the input image was recognized as + // having the i-th label. + // + // A separate file contains a list of string labels corresponding to the + // integer indices of the output. + // + // This example: + // - Loads the serialized representation of the pre-trained model into a Graph + // - Creates a Session to execute operations on the Graph + // - Converts an image file to a Tensor to provide as input to a Session run + // - Executes the Session and prints out the label with the highest probability + // + // To convert an image file to a Tensor suitable for input to the Inception model, + // this example: + // - Constructs another TensorFlow graph to normalize the image into a + // form suitable for the model (for example, resizing the image) + // - Creates and executes a Session to obtain a Tensor in this normalized form. + modeldir := flag.String("dir", "", "Directory containing the trained model files. The directory will be created and the model downloaded into it if necessary") + imagefile := flag.String("image", "", "Path of a JPEG-image to extract labels for") + flag.Parse() + if *modeldir == "" || *imagefile == "" { + flag.Usage() + return + } + // Load the serialized GraphDef from a file. + modelfile, labelsfile, err := modelFiles(*modeldir) + if err != nil { + log.Fatal(err) + } + model, err := ioutil.ReadFile(modelfile) + if err != nil { + log.Fatal(err) + } + + // Construct an in-memory graph from the serialized form. + graph := tf.NewGraph() + if err := graph.Import(model, ""); err != nil { + log.Fatal(err) + } + + // Create a session for inference over graph. + session, err := tf.NewSession(graph, nil) + if err != nil { + log.Fatal(err) + } + defer session.Close() + + // Run inference on *imageFile. + // For multiple images, session.Run() can be called in a loop (and + // concurrently). Alternatively, images can be batched since the model + // accepts batches of image data as input. + tensor, err := makeTensorFromImage(*imagefile) + if err != nil { + log.Fatal(err) + } + output, err := session.Run( + map[tf.Output]*tf.Tensor{ + graph.Operation("input").Output(0): tensor, + }, + []tf.Output{ + graph.Operation("output").Output(0), + }, + nil) + if err != nil { + log.Fatal(err) + } + // output[0].Value() is a vector containing probabilities of + // labels for each image in the "batch". The batch size was 1. + // Find the most probably label index. + probabilities := output[0].Value().([][]float32)[0] + printBestLabel(probabilities, labelsfile) +} + +func printBestLabel(probabilities []float32, labelsFile string) { + bestIdx := 0 + for i, p := range probabilities { + if p > probabilities[bestIdx] { + bestIdx = i + } + } + // Found the best match. Read the string from labelsFile, which + // contains one line per label. + file, err := os.Open(labelsFile) + if err != nil { + log.Fatal(err) + } + defer file.Close() + scanner := bufio.NewScanner(file) + var labels []string + for scanner.Scan() { + labels = append(labels, scanner.Text()) + } + if err := scanner.Err(); err != nil { + log.Printf("ERROR: failed to read %s: %v", labelsFile, err) + } + fmt.Printf("BEST MATCH: (%2.0f%% likely) %s\n", probabilities[bestIdx]*100.0, labels[bestIdx]) +} + +// Convert the image in filename to a Tensor suitable as input to the Inception model. +func makeTensorFromImage(filename string) (*tf.Tensor, error) { + bytes, err := ioutil.ReadFile(filename) + if err != nil { + return nil, err + } + // DecodeJpeg uses a scalar String-valued tensor as input. + tensor, err := tf.NewTensor(string(bytes)) + if err != nil { + return nil, err + } + // Construct a graph to normalize the image + graph, input, output, err := constructGraphToNormalizeImage() + if err != nil { + return nil, err + } + // Execute that graph to normalize this one image + session, err := tf.NewSession(graph, nil) + if err != nil { + return nil, err + } + defer session.Close() + normalized, err := session.Run( + map[tf.Output]*tf.Tensor{input: tensor}, + []tf.Output{output}, + nil) + if err != nil { + return nil, err + } + return normalized[0], nil +} + +// The inception model takes as input the image described by a Tensor in a very +// specific normalized format (a particular image size, shape of the input tensor, +// normalized pixel values etc.). +// +// This function constructs a graph of TensorFlow operations which takes as +// input a JPEG-encoded string and returns a tensor suitable as input to the +// inception model. +func constructGraphToNormalizeImage() (graph *tf.Graph, input, output tf.Output, err error) { + // Some constants specific to the pre-trained model at: + // https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip + // + // - The model was trained after with images scaled to 224x224 pixels. + // - The colors, represented as R, G, B in 1-byte each were converted to + // float using (value - Mean)/Scale. + const ( + H, W = 224, 224 + Mean = float32(117) + Scale = float32(1) + ) + // - input is a String-Tensor, where the string the JPEG-encoded image. + // - The inception model takes a 4D tensor of shape + // [BatchSize, Height, Width, Colors=3], where each pixel is + // represented as a triplet of floats + // - Apply normalization on each pixel and use ExpandDims to make + // this single image be a "batch" of size 1 for ResizeBilinear. + s := op.NewScope() + input = op.Placeholder(s, tf.String) + output = op.Div(s, + op.Sub(s, + op.ResizeBilinear(s, + op.ExpandDims(s, + op.Cast(s, + op.DecodeJpeg(s, input, op.DecodeJpegChannels(3)), tf.Float), + op.Const(s.SubScope("make_batch"), int32(0))), + op.Const(s.SubScope("size"), []int32{H, W})), + op.Const(s.SubScope("mean"), Mean)), + op.Const(s.SubScope("scale"), Scale)) + graph, err = s.Finalize() + return graph, input, output, err +} + +func modelFiles(dir string) (modelfile, labelsfile string, err error) { + const URL = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip" + var ( + model = filepath.Join(dir, "tensorflow_inception_graph.pb") + labels = filepath.Join(dir, "imagenet_comp_graph_label_strings.txt") + zipfile = filepath.Join(dir, "inception5h.zip") + ) + if filesExist(model, labels) == nil { + return model, labels, nil + } + log.Println("Did not find model in", dir, "downloading from", URL) + if err := os.MkdirAll(dir, 0755); err != nil { + return "", "", err + } + if err := download(URL, zipfile); err != nil { + return "", "", fmt.Errorf("failed to download %v - %v", URL, err) + } + if err := unzip(dir, zipfile); err != nil { + return "", "", fmt.Errorf("failed to extract contents from model archive: %v", err) + } + os.Remove(zipfile) + return model, labels, filesExist(model, labels) +} + +func filesExist(files ...string) error { + for _, f := range files { + if _, err := os.Stat(f); err != nil { + return fmt.Errorf("unable to stat %s: %v", f, err) + } + } + return nil +} + +func download(URL, filename string) error { + resp, err := http.Get(URL) + if err != nil { + return err + } + defer resp.Body.Close() + file, err := os.OpenFile(filename, os.O_RDWR|os.O_CREATE, 0644) + if err != nil { + return err + } + defer file.Close() + _, err = io.Copy(file, resp.Body) + return err +} + +func unzip(dir, zipfile string) error { + r, err := zip.OpenReader(zipfile) + if err != nil { + return err + } + defer r.Close() + for _, f := range r.File { + src, err := f.Open() + if err != nil { + return err + } + log.Println("Extracting", f.Name) + dst, err := os.OpenFile(filepath.Join(dir, f.Name), os.O_WRONLY|os.O_CREATE, 0644) + if err != nil { + return err + } + if _, err := io.Copy(dst, src); err != nil { + return err + } + dst.Close() + } + return nil +} diff --git a/tensorflow/go/genop/.gitignore b/tensorflow/go/genop/.gitignore new file mode 100644 index 0000000..a848384 --- /dev/null +++ b/tensorflow/go/genop/.gitignore @@ -0,0 +1,2 @@ +# .pb.go files generated by generate.sh +internal/proto/* diff --git a/tensorflow/go/genop/generate.sh b/tensorflow/go/genop/generate.sh new file mode 100644 index 0000000..6d56e82 --- /dev/null +++ b/tensorflow/go/genop/generate.sh @@ -0,0 +1,62 @@ +#!/usr/bin/env bash +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e + +#go get github.com/golang/protobuf/proto +#go get github.com/golang/protobuf/protoc-gen-go + +if [ -z "${GOPATH}" ] +then + GOPATH=$(go env GOPATH) +fi + +cd $(dirname $0) +for g in $(echo "${GOPATH//:/ }"); do + TF_DIR="${g}/src/github.com/tensorflow/tensorflow" + PROTOC="${TF_DIR}/bazel-out/host/bin/external/protobuf/protoc" + if [ -x "${PROTOC}" ]; then + break + fi +done + +if [ ! -x "${PROTOC}" ] +then + set +e + PATH_PROTOC=$(which protoc) + if [ ! -x "${PATH_PROTOC}" ] + then + echo "Protocol buffer compiler protoc not found in PATH or in ${PROTOC}" + echo "Perhaps build it using:" + echo "bazel build --config opt @com_google_protobuf//:protoc" + exit 1 + fi + PROTOC=$PATH_PROTOC + set -e +fi + +# Ensure that protoc-gen-go is available in $PATH +# Since ${PROTOC} will require it. +export PATH=$PATH:${GOPATH}/bin +mkdir -p ../vendor +for FILE in ${TF_DIR}/tensorflow/core/framework/*.proto \ + ${TF_DIR}/tensorflow/core/protobuf/*.proto \ + ${TF_DIR}/tensorflow/stream_executor/*.proto; do + ${PROTOC} \ + -I ${TF_DIR} \ + --go_out=../vendor \ + $FILE +done diff --git a/tensorflow/go/genop/internal/api_def_map.go b/tensorflow/go/genop/internal/api_def_map.go new file mode 100644 index 0000000..7fa21f8 --- /dev/null +++ b/tensorflow/go/genop/internal/api_def_map.go @@ -0,0 +1,128 @@ +/* +Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package internal + +/* +#include +#include + +#include "tensorflow/c/c_api.h" +*/ +import "C" + +import ( + "errors" + "fmt" + "runtime" + "unsafe" + + "github.com/golang/protobuf/proto" + adpb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/api_def_go_proto" + odpb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto" +) + +// Encapsulates a collection of API definitions. +// +// apiDefMap represents a map from operation name to corresponding +// ApiDef proto (see +// https://www.tensorflow.org/code/tensorflow/core/framework/api_def.proto +// for ApiDef proto definition). +type apiDefMap struct { + c *C.TF_ApiDefMap +} + +// Creates and returns a new apiDefMap instance. +// +// oplist is and OpList proto instance (see +// https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto +// for OpList proto definition). + +func newAPIDefMap(oplist *odpb.OpList) (*apiDefMap, error) { + // Create a buffer containing the serialized OpList. + opdefSerialized, err := proto.Marshal(oplist) + if err != nil { + return nil, fmt.Errorf("could not serialize OpDef for %s", oplist.String()) + } + data := C.CBytes(opdefSerialized) + defer C.free(data) + + opbuf := C.TF_NewBuffer() + defer C.TF_DeleteBuffer(opbuf) + opbuf.data = data + opbuf.length = C.size_t(len(opdefSerialized)) + + // Create ApiDefMap. + status := C.TF_NewStatus() + defer C.TF_DeleteStatus(status) + capimap := C.TF_NewApiDefMap(opbuf, status) + if C.TF_GetCode(status) != C.TF_OK { + return nil, errors.New(C.GoString(C.TF_Message(status))) + } + apimap := &apiDefMap{capimap} + runtime.SetFinalizer( + apimap, + func(a *apiDefMap) { + C.TF_DeleteApiDefMap(a.c) + }) + return apimap, nil +} + +// Updates apiDefMap with the overrides specified in `data`. +// +// data - ApiDef text proto. +func (m *apiDefMap) Put(data string) error { + cdata := C.CString(data) + defer C.free(unsafe.Pointer(cdata)) + status := C.TF_NewStatus() + defer C.TF_DeleteStatus(status) + C.TF_ApiDefMapPut(m.c, cdata, C.size_t(len(data)), status) + if C.TF_GetCode(status) != C.TF_OK { + return errors.New(C.GoString(C.TF_Message(status))) + } + return nil +} + +// Returns ApiDef proto instance for the TensorFlow operation +// named `opname`. +func (m *apiDefMap) Get(opname string) (*adpb.ApiDef, error) { + cname := C.CString(opname) + defer C.free(unsafe.Pointer(cname)) + status := C.TF_NewStatus() + defer C.TF_DeleteStatus(status) + apidefBuf := C.TF_ApiDefMapGet( + m.c, cname, C.size_t(len(opname)), status) + defer C.TF_DeleteBuffer(apidefBuf) + if C.TF_GetCode(status) != C.TF_OK { + return nil, errors.New(C.GoString(C.TF_Message(status))) + } + if apidefBuf == nil { + return nil, fmt.Errorf("could not find ApiDef for %s", opname) + } + + var ( + apidef = new(adpb.ApiDef) + size = int(apidefBuf.length) + // A []byte backed by C memory. + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + data = (*[1 << 30]byte)(unsafe.Pointer(apidefBuf.data))[:size:size] + err = proto.Unmarshal(data, apidef) + ) + if err != nil { + return nil, err + } + return apidef, nil +} diff --git a/tensorflow/go/genop/internal/genop.go b/tensorflow/go/genop/internal/genop.go new file mode 100644 index 0000000..c4ea8ab --- /dev/null +++ b/tensorflow/go/genop/internal/genop.go @@ -0,0 +1,590 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// Package internal generates Go source code with functions for TensorFlow operations. +// +// The basic outline of the generated API is as follows: +// +// - One function for each TensorFlow operation +// - The arguments to the function are the inputs and required attributes of the operation +// - The function returns the outputs +// - A function is also generated for each optional attribute of the operation. +// +// There is a possibility that there are name collisions between the functions +// generated for ops and the functions generated for optional attributes. For +// now, we ignore those, but will need to revisit if a collision is actually +// encountered. +package internal + +/* +#include + +#include "tensorflow/c/c_api.h" +*/ +import "C" + +import ( + "fmt" + "io" + "io/ioutil" + "path" + "reflect" + "strings" + "text/template" + "unsafe" + + "github.com/golang/protobuf/proto" + adpb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/api_def_go_proto" + odpb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto" +) + +// GenerateFunctionsForRegisteredOps writes a Go source code file to w +// containing functions for each TensorFlow operation registered in the address +// space of the calling process. +// apidefDirs should be a contain of directories containing api_def_*.pbtxt +// files to load. +func GenerateFunctionsForRegisteredOps( + w io.Writer, apidefDirs []string) error { + ops, apimap, err := registeredOps() + if err != nil { + return err + } + for _, dir := range apidefDirs { + if err = updateAPIDefs(apimap, dir); err != nil { + return err + } + } + return generateFunctionsForOps(w, ops, apimap) +} + +func registeredOps() (*odpb.OpList, *apiDefMap, error) { + buf := C.TF_GetAllOpList() + defer C.TF_DeleteBuffer(buf) + var ( + list = new(odpb.OpList) + size = int(buf.length) + // A []byte backed by C memory. + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + data = (*[1 << 30]byte)(unsafe.Pointer(buf.data))[:size:size] + err = proto.Unmarshal(data, list) + ) + if err != nil { + return nil, nil, err + } + apimap, err := newAPIDefMap(list) + return list, apimap, err +} + +func updateAPIDefs(m *apiDefMap, dir string) error { + files, err := ioutil.ReadDir(dir) + if err != nil { + return err + } + for _, file := range files { + data, err := ioutil.ReadFile(path.Join(dir, file.Name())) + if err != nil { + return fmt.Errorf("failed to read %q: %v", file.Name(), err) + } + if err = m.Put(string(data)); err != nil { + return fmt.Errorf("failed to process %q: %v", file.Name(), err) + } + } + return nil +} + +func generateFunctionsForOps(w io.Writer, ops *odpb.OpList, apimap *apiDefMap) error { + thisPackage := reflect.TypeOf(tmplArgs{}).PkgPath() + if err := tmplHeader.Execute(w, thisPackage); err != nil { + return err + } + blacklist := map[string]bool{ + "Const": true, + "PyFunc": true, + "PyFuncStateless": true, + } + for _, op := range ops.Op { + if blacklist[op.Name] { + continue + } + apidef, err := apimap.Get(op.Name) + if err != nil { + return err + } + if err := generateFunctionForOp(w, op, apidef); err != nil { + return err + } + } + return nil +} + +func generateFunctionForOp(w io.Writer, op *odpb.OpDef, apidef *adpb.ApiDef) error { + if strings.HasPrefix(op.Name, "_") { // Internal operation + return nil + } + // Ignore operations where the Go types corresponding to the TensorFlow + // type haven't been worked out (such as "func"s). + for _, a := range op.Attr { + if _, err := goType(a.Type); err != nil { + return nil + } + } + // Also, haven't figured out reference types yet, so ignore those too. + for _, a := range op.InputArg { + if a.IsRef { + return nil + } + } + for _, a := range op.OutputArg { + if a.IsRef { + return nil + } + } + if apidef.Summary == "" { + // Undocumented operation, perhaps a sign of not being ready to + // export. + return nil + } + tmplArgs, err := newTmplArgs(op, apidef) + if err != nil { + return err + } + return tmplOp.Execute(w, tmplArgs) +} + +var ( + // Go keywords that cannot be used as identifiers. + // From https://golang.org/ref/spec#Keywords + keywords = []string{ + "break", "default", "func", "interface", "select", "case", + "defer", "go", "map", "struct", "chan", "else", "goto", + "package", "switch", "const", "fallthrough", "if", "range", + "type", "continue", "for", "import", "return", "var", + } + + tmplHeader = template.Must(template.New("header").Parse(`// DO NOT EDIT +// This file was machine generated by {{.}} +// +// WARNING: This generation of wrapper function for TensorFlow ops is in an +// experimental state. The generated API can change without notice. + +package op + +import tf "github.com/tensorflow/tensorflow/tensorflow/go" + +// optionalAttr is an intentionally un-exported type to hide +// details of how optional attributes to operations are implemented. +type optionalAttr map[string]interface{} + +func makeOutputList(op *tf.Operation, start int, output string) ([]tf.Output, int, error) { + size, err := op.OutputListSize(output) + if err != nil { + return nil, start, err + } + list := make([]tf.Output, size) + for i := 0; i < size; i++ { + list[i] = op.Output(start + i) + } + return list, start + size, nil +} +`)) + + tmplOp = template.Must(template.New("op").Funcs(template.FuncMap{ + "MakeComment": makeComment, + "GoType": goType, + "CamelCase": camelCase, + "Identifier": identifier, + "IsListArg": isListArg, + "IsListAttr": isListAttr, + "StripLeadingColon": stripLeadingColon, + }).Parse(` +{{if .OptionalAttrs -}} +{{/* Type for specifying all optional attributes. */ -}} +// {{.Op.Name}}Attr is an optional argument to {{.Op.Name}}. +type {{.Op.Name}}Attr func(optionalAttr) + +{{range .OptionalAttrs}} +// {{$.Op.Name}}{{CamelCase .RenameTo}} sets the optional {{.RenameTo}} attribute to value. +{{- if .Description}} +// +// value: {{MakeComment .Description}} +{{- end}} +// If not specified, defaults to {{StripLeadingColon .DefaultValue}} +{{- if .HasMinimum}} +// +// {{if .IsListAttr }}REQUIRES: len(value) >= {{.Minimum}}{{else}}REQUIRES: value >= {{.Minimum}}{{end}} +{{- end}} +func {{$.Op.Name}}{{CamelCase .RenameTo}}(value {{GoType .Type}}) {{$.Op.Name}}Attr { + return func(m optionalAttr) { + m[{{printf "%q" .Name}}] = value + } +} +{{end}} +{{end}} + +{{- /* Create a godoc friendly comment. */ -}} + +// {{MakeComment .APIDef.Summary}} + +{{- with .Op.Deprecation}} +// +// DEPRECATED at GraphDef version {{.Version}}: {{.Explanation}} +{{- end -}} + +{{- with .APIDef.Description}} +// +// {{MakeComment .}} +{{- end -}} + +{{- if .DescribeArguments}} +// +// Arguments: +{{- range .InArgsReordered}} +// {{if .Description}}{{Identifier .RenameTo}}: {{MakeComment .Description}}{{end}} +{{- end -}} +{{- range .RequiredAttrs}} +// {{if .Description}}{{Identifier .RenameTo}}: {{MakeComment .Description}}{{end}} +{{- end -}} +{{- end -}} + +{{- if (not .Op.OutputArg) }} +// +// Returns the created operation. +{{- else }} +{{- if .DescribeOutputs}} +// +{{- if eq (len .OutArgs) 1 }} +// Returns {{range .OutArgs}}{{MakeComment .Description}}{{end}} +{{- else }} +// Returns: +{{- range .OutArgs}} +// {{Identifier .RenameTo}}{{if .Description}}: {{MakeComment .Description}}{{end}} +{{- end -}} +{{- end -}} +{{- end -}} +{{- end -}} +{{- /* + + The function signature. + Since OpDef.Name is in CamelCase, it cannot conflict with a reserved keyword in Golang +*/}} +func {{.Op.Name}} + +{{- /* + Fill in input arguments: + (1) The Scope + (2) All input arguments (which may be either []tf.Output or tf.Output) + (3) All required attributes + (4) Variadic list of optional attributes +*/ -}} + +(scope *Scope +{{- range $i, $a := .InArgsReordered}}, {{Identifier $a.RenameTo}} {{if $a.IsListArg}}[]{{end}}tf.Output{{end -}} +{{range $i, $a := .RequiredAttrs}}, {{Identifier $a.RenameTo}} {{GoType $a.Type}}{{end -}} +{{if .OptionalAttrs}}, optional ...{{.Op.Name}}Attr{{end -}} +) + +{{- /* Construct outputs: len(.OutArgs) or a *tf.Operation */ -}} + +{{if .OutArgs -}} +({{range $i,$a := .OutArgs}}{{if $i}}, {{end}}{{Identifier $a.RenameTo}} {{if $a.IsListArg}}[]{{end}}tf.Output{{end -}}) +{{- else -}} +(o *tf.Operation) +{{- end }} { + if scope.Err() != nil { + return + } + {{if .HasAttrs -}} + attrs := map[string]interface{}{ {{- range .RequiredAttrs}}{{printf "%q" .Name}}: {{Identifier .RenameTo}},{{end}}} + {{if .OptionalAttrs -}} + for _, a := range optional { + a(attrs) + } + {{end -}} + {{end -}} + opspec := tf.OpSpec{ + Type: {{printf "%q" .Op.Name}}, + {{if .InArgs -}} + Input: []tf.Input{ + {{range $i,$a := .InArgs}}{{if $a.IsListArg}}tf.OutputList({{Identifier $a.RenameTo}}){{else}}{{Identifier $a.RenameTo}}{{end}}, {{end}} + }, + {{- end}} + {{- if .HasAttrs}} + Attrs: attrs, + {{- end}} + } + {{- if .OutArgs}} + {{- if .HasListOutput}} + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + {{- range $i, $a := .OutArgs}} + {{- if $a.IsListArg}} + if {{Identifier .RenameTo}}, idx, err = makeOutputList(op, idx, {{printf "%q" .Name}}); err != nil { + scope.UpdateErr({{printf "%q" $.Op.Name}}, err) + return + } + {{- else }} + {{Identifier .RenameTo}} = op.Output(idx) + {{- end }}{{- /* if IsListArg */}} + {{- end }}{{- /* range .OutArgs */}} + return {{range $i, $a := .OutArgs}}{{if $i}}, {{end}}{{Identifier .RenameTo}}{{end}} + {{- else }} + op := scope.AddOperation(opspec) + return {{range $i, $a := .OutArgs}}{{if $i}}, {{end}}op.Output({{$i}}){{end}} + {{- end }}{{- /* if .HasListOutput */}} + {{- else }} + return scope.AddOperation(opspec) + {{- end }}{{- /* if .OutArgs */}} +} +`)) +) + +type attrWrapper struct { + op *odpb.OpDef_AttrDef + api *adpb.ApiDef_Attr +} + +func (a *attrWrapper) Name() string { return a.api.Name } +func (a *attrWrapper) RenameTo() string { return a.api.RenameTo } +func (a *attrWrapper) Description() string { return a.api.Description } +func (a *attrWrapper) Type() string { return a.op.Type } +func (a *attrWrapper) IsListAttr() bool { return isListAttr(a.op) } +func (a *attrWrapper) HasMinimum() bool { return a.op.HasMinimum } +func (a *attrWrapper) Minimum() int64 { return a.op.Minimum } +func (a *attrWrapper) DefaultValue() interface{} { return a.api.DefaultValue } + +type argWrapper struct { + op *odpb.OpDef_ArgDef + api *adpb.ApiDef_Arg +} + +func (a *argWrapper) Name() string { return a.api.Name } +func (a *argWrapper) RenameTo() string { return a.api.RenameTo } +func (a *argWrapper) Description() string { return a.api.Description } +func (a *argWrapper) IsListArg() bool { return isListArg(a.op) } + +type tmplArgs struct { + Op *odpb.OpDef + APIDef *adpb.ApiDef + // Op.Attr is split into two categories + // (1) Required: These must be specified by the client and are thus + // included in the function signature. + // (2) Optional: These need not be specified (as they have default + // values) and thus do not appear in the function signature. + RequiredAttrs []*attrWrapper + OptionalAttrs []*attrWrapper + InArgs []*argWrapper + // Input arguments ordered based on arg_order field of ApiDef. + InArgsReordered []*argWrapper + OutArgs []*argWrapper +} + +func newTmplArgs(op *odpb.OpDef, apidef *adpb.ApiDef) (*tmplArgs, error) { + ret := tmplArgs{Op: op, APIDef: apidef} + + // Setup InArgs field + for i, in := range op.InputArg { + argCombined := argWrapper{op: in, api: apidef.InArg[i]} + ret.InArgs = append(ret.InArgs, &argCombined) + } + + // Setup OutArgs field + for i, out := range op.OutputArg { + argCombined := argWrapper{op: out, api: apidef.OutArg[i]} + ret.OutArgs = append(ret.OutArgs, &argCombined) + } + + // Setup InArgsReordered field + for _, argName := range apidef.ArgOrder { + // Find the argument in op.InputArg + argIndex := -1 + for i, in := range op.InputArg { + if in.Name == argName { + argIndex = i + break + } + } + if argIndex == -1 { + return nil, fmt.Errorf( + "couldn't find argument %s in ApiDef for op %s", + argName, op.Name) + } + argCombined := argWrapper{ + op: op.InputArg[argIndex], api: apidef.InArg[argIndex]} + ret.InArgsReordered = append(ret.InArgsReordered, &argCombined) + } + + if len(op.Attr) == 0 { + return &ret, nil + } + // Attributes related to the InputArg's type are inferred automatically + // and are not exposed to the client. + inferred := make(map[string]bool) + for _, in := range op.InputArg { + switch { + case in.TypeAttr != "": + inferred[in.TypeAttr] = true + case in.TypeListAttr != "": + inferred[in.TypeListAttr] = true + } + if in.NumberAttr != "" { + inferred[in.NumberAttr] = true + } + } + for i, attr := range op.Attr { + if inferred[attr.Name] { + continue + } + attrCombined := attrWrapper{op: attr, api: apidef.Attr[i]} + if attr.DefaultValue == nil { + ret.RequiredAttrs = append(ret.RequiredAttrs, &attrCombined) + } else { + ret.OptionalAttrs = append(ret.OptionalAttrs, &attrCombined) + } + } + return &ret, nil +} + +func (a *tmplArgs) HasAttrs() bool { return len(a.RequiredAttrs)+len(a.OptionalAttrs) > 0 } +func (a *tmplArgs) DescribeArguments() bool { + for _, arg := range a.InArgs { + if arg.Description() != "" { + return true + } + } + for _, attr := range a.RequiredAttrs { + if attr.Description() != "" { + return true + } + } + return false + +} +func (a *tmplArgs) DescribeOutputs() bool { + for _, arg := range a.OutArgs { + if arg.Description() != "" { + return true + } + } + return false +} +func (a *tmplArgs) HasListOutput() bool { + for _, arg := range a.OutArgs { + if arg.IsListArg() { + return true + } + } + return false +} + +func makeComment(lines string) string { + return strings.Join(strings.SplitAfter(lines, "\n"), "// ") +} + +// goType converts a TensorFlow "type" ('string', 'int', 'list(string)' etc.) +// to the corresponding type in Go. +func goType(tfType string) (string, error) { + list, tfType := parseTFType(tfType) + var gotype string + switch tfType { + case "int": + gotype = "int64" + case "float": + gotype = "float32" + case "bool": + gotype = "bool" + case "type": + gotype = "tf.DataType" + case "shape": + gotype = "tf.Shape" + case "tensor": + gotype = "tf.Tensor" + case "string": + gotype = "string" + default: + return "", fmt.Errorf("%q is not a recognized DataType", tfType) + } + if list { + gotype = "[]" + gotype + } + return gotype, nil +} + +func camelCase(snakeCase string) string { + words := strings.Split(snakeCase, "_") + for i, w := range words { + words[i] = strings.ToUpper(string(w[0])) + w[1:] + } + return strings.Join(words, "") +} + +// identifier creates an identifier for s usable in the generated Go source +// code. +// +// Avoids collisions with keywords and other identifiers used in the generated +// code. +func identifier(s string) string { + // Identifiers used in the generated code. + if s == "tf" || s == "scope" || s == "err" || s == "op" { + return s + "_" + } + for _, k := range keywords { + if s == k { + // Alternatively, make the first letter upper case. + return s + "_" + } + } + return s +} + +func isListArg(argdef *odpb.OpDef_ArgDef) bool { + return argdef.TypeListAttr != "" || argdef.NumberAttr != "" +} + +func isListAttr(attrdef *odpb.OpDef_AttrDef) bool { + list, _ := parseTFType(attrdef.Type) + return list +} + +// stripLeadingColon removes the prefix of the string up to the first colon. +// +// This is useful when 's' corresponds to a "oneof" protocol buffer message. +// For example, consider the protocol buffer message: +// oneof value { bool b = 1; int64 i = 2; } +// proto.CompactTextString) will print "b:true", or "i:7" etc. This function +// strips out the leading "b:" or "i:". +func stripLeadingColon(m proto.Message) string { + x := proto.CompactTextString(m) + y := strings.SplitN(x, ":", 2) + if len(y) < 2 { + return x + } + return y[1] +} + +func parseTFType(tfType string) (list bool, typ string) { + const ( + listPrefix = "list(" + listSuffix = ")" + ) + if strings.HasPrefix(tfType, listPrefix) && strings.HasSuffix(tfType, listSuffix) { + return true, strings.TrimSuffix(strings.TrimPrefix(tfType, listPrefix), listSuffix) + } + return false, tfType +} diff --git a/tensorflow/go/genop/internal/genop_test.go b/tensorflow/go/genop/internal/genop_test.go new file mode 100644 index 0000000..b467efc --- /dev/null +++ b/tensorflow/go/genop/internal/genop_test.go @@ -0,0 +1,820 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package internal + +import ( + "bytes" + "go/format" + "testing" + + "github.com/golang/protobuf/proto" + adpb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/api_def_go_proto" + odpb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto" +) + +// Creates an ApiDef based on opdef and applies overrides +// from apidefText (ApiDef text proto). +func GetAPIDef(t *testing.T, opdef *odpb.OpDef, apidefText string) *adpb.ApiDef { + opdefList := &odpb.OpList{Op: []*odpb.OpDef{opdef}} + apimap, err := newAPIDefMap(opdefList) + if err != nil { + t.Fatal(err) + } + err = apimap.Put(apidefText) + if err != nil { + t.Fatal(err) + } + apidef, err := apimap.Get(opdef.Name) + if err != nil { + t.Fatal(err) + } + return apidef +} + +func TestGenerateOp(t *testing.T) { + // TestGenerateOp validates the generated source code for an op. + // The OpDef for the test cases are simplified forms of real ops. + testdata := []struct { + tag string + opdef string + apidef string + wanted string + }{ + { + tag: "NoOp", + opdef: ` +name: "NoOp" +`, + apidef: ` +op: < +graph_op_name: "NoOp" +summary: "No. Op." +> +`, + wanted: ` +// No. Op. +// +// Returns the created operation. +func NoOp(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NoOp", + } + return scope.AddOperation(opspec) +} +`, + }, + { + tag: "NoAttributes", + opdef: ` +name: "Add" +input_arg: < + name: "x" + type_attr: "T" +> +input_arg: < + name: "y" + type_attr: "T" +> +output_arg: < + name: "z" + type_attr: "T" +> +attr: < + name: "T" + type: "type" + allowed_values: < + list: < + type: DT_FLOAT + type: DT_INT64 + > + > +> +`, + apidef: ` +op: < +graph_op_name: "Add" +summary: "Returns x + y element-wise." +description: "Blah blah", +> +`, + wanted: ` +// Returns x + y element-wise. +// +// Blah blah +func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Add", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} +`, + }, + { + tag: "RequiredAttributes", + opdef: ` +name: "Cast" +input_arg: < + name: "x" + type_attr: "SrcT" +> +output_arg: < + name: "y" + type_attr: "DstT" +> +attr: < + name: "SrcT" + type: "type" +> +attr: < + name: "DstT" + type: "type" +> +`, + apidef: ` +op: < +graph_op_name: "Cast" +summary: "Cast x of type SrcT to y of DstT." +> +`, + wanted: ` +// Cast x of type SrcT to y of DstT. +func Cast(scope *Scope, x tf.Output, DstT tf.DataType) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"DstT": DstT} + opspec := tf.OpSpec{ + Type: "Cast", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} +`, + }, + { + tag: "OptionalAttributes", + opdef: ` +name: "DecodeJpeg" +input_arg: < + name: "contents" + type: DT_STRING +> +output_arg: < + name: "image" + type: DT_UINT8 +> +attr: < + name: "channels" + type: "int" + default_value: < + i: 0 + > +> +attr: < + name: "fancy_upscaling" + type: "bool" + default_value: < + b: true + > +> +attr: < + name: "acceptable_fraction" + type: "float" + default_value: < + f: 1 + > +> +`, + apidef: ` +op: < +graph_op_name: "DecodeJpeg" +in_arg: < + name: "contents" + description: "0-D. The JPEG-encoded image." +> +out_arg: < + name: "image" + description: "3-D with shape [height, width, channels]" +> +attr: < + name: "channels" + description: "Number of color channels for the decoded image." +> +attr: < + name: "fancy_upscaling" + description: "If true use a slower but nicer upscaling of the\nchroma planes (yuv420/422 only)." +> +attr: < + name: "acceptable_fraction" + description: "The minimum required fraction of lines before a truncated\ninput is accepted." +> +summary: "Decode a JPEG-encoded image to a uint8 tensor." +description: "Norna dorna fjord\nkajorna\nhahaha" +> +`, + wanted: ` +// DecodeJpegAttr is an optional argument to DecodeJpeg. +type DecodeJpegAttr func(optionalAttr) + +// DecodeJpegChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodeJpegChannels(value int64) DecodeJpegAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). +// If not specified, defaults to true +func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr { + return func(m optionalAttr) { + m["fancy_upscaling"] = value + } +} + +// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} + +// Decode a JPEG-encoded image to a uint8 tensor. +// +// Norna dorna fjord +// kajorna +// hahaha +// +// Arguments: +// contents: 0-D. The JPEG-encoded image. +// +// Returns 3-D with shape [height, width, channels] +func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeJpeg", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} +`, + }, + { + tag: "MultipleOutputs", + opdef: ` +name: "TwoOutputs" +input_arg: < + name: "input" + type_attr: "T" +> +output_arg < + name: "x" + type_attr: "T" +> +output_arg < + name: "y" + type_attr: "T" +> +attr: < + name: "T" + type: "type" +> +`, + apidef: ` +op: < +graph_op_name: "TwoOutputs" +summary: "Op that produces multiple outputs" +> +`, + wanted: ` +// Op that produces multiple outputs +func TwoOutputs(scope *Scope, input tf.Output) (x tf.Output, y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TwoOutputs", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} +`, + }, + { + tag: "ListOutput", + opdef: ` +name: "ShapeN" +input_arg: < + name: "input" + type_attr: "T" + number_attr: "N" +> +output_arg: < + name: "output" + type_attr: "out_type" + number_attr: "N" +> +attr: < + name: "N" + type: "int" + has_minimum: true + minimum: 1 +> +attr: < + name: "T" + type: "type" +> +attr: < + name: "out_type" + type: "type" + default_value: < + type: DT_INT32 + > + allowed_values: < + list: < + type: DT_INT32 + type: DT_INT64 + > + > +> +`, + apidef: ` +op: < +graph_op_name: "ShapeN" +summary: "Returns shape of tensors." +description: "Some description here." +> +`, + wanted: ` +// ShapeNAttr is an optional argument to ShapeN. +type ShapeNAttr func(optionalAttr) + +// ShapeNOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func ShapeNOutType(value tf.DataType) ShapeNAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns shape of tensors. +// +// Some description here. +func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShapeN", + Input: []tf.Input{ + tf.OutputList(input), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("ShapeN", err) + return + } + return output +} +`, + }, + { + tag: "ApiDefOverrides", + opdef: ` +name: "TestOp" +input_arg: < + name: "a" + type: DT_STRING +> +input_arg: < + name: "b" + type: DT_STRING +> +output_arg: < + name: "c" + type: DT_UINT8 +> +attr: < + name: "d" + type: "int" + default_value: < + i: 0 + > +> +`, + apidef: ` +op: < +graph_op_name: "TestOp" +in_arg: < + name: "a" + rename_to: "aa" + description: "Description for aa." +> +in_arg: < + name: "b" + rename_to: "bb" + description: "Description for bb." +> +arg_order: "b" +arg_order: "a" +out_arg: < + name: "c" + rename_to: "cc" + description: "Description for cc." +> +attr: < + name: "d" + rename_to: "dd" + description: "Description for dd." +> +summary: "Summary for TestOp." +description: "Description for TestOp." +> +`, + wanted: ` +// TestOpAttr is an optional argument to TestOp. +type TestOpAttr func(optionalAttr) + +// TestOpDd sets the optional dd attribute to value. +// +// value: Description for dd. +// If not specified, defaults to 0 +func TestOpDd(value int64) TestOpAttr { + return func(m optionalAttr) { + m["d"] = value + } +} + +// Summary for TestOp. +// +// Description for TestOp. +// +// Arguments: +// bb: Description for bb. +// aa: Description for aa. +// +// Returns Description for cc. +func TestOp(scope *Scope, bb tf.Output, aa tf.Output, optional ...TestOpAttr) (cc tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TestOp", + Input: []tf.Input{ + aa, bb, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} +`, + }, + { + tag: "SampleDistortedBoundingBox", + opdef: ` +name: "SampleDistortedBoundingBox" +input_arg { + name: "image_size" + type_attr: "T" +} +input_arg { + name: "bounding_boxes" + type: DT_FLOAT +} +output_arg { + name: "begin" + type_attr: "T" +} +output_arg { + name: "size" + type_attr: "T" +} +output_arg { + name: "bboxes" + type: DT_FLOAT +} +attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + } + } +} +attr { + name: "seed" + type: "int" + default_value { + i: 0 + } +} +attr { + name: "seed2" + type: "int" + default_value { + i: 0 + } +} +attr { + name: "min_object_covered" + type: "float" + default_value { + f: 0.1 + } +} +attr { + name: "aspect_ratio_range" + type: "list(float)" + default_value { + list { + f: 0.75 + f: 1.33 + } + } +} +attr { + name: "area_range" + type: "list(float)" + default_value { + list { + f: 0.05 + f: 1 + } + } +} +attr { + name: "max_attempts" + type: "int" + default_value { + i: 100 + } +} +attr { + name: "use_image_if_no_bounding_boxes" + type: "bool" + default_value { + b: false + } +} +is_stateful: true +`, + apidef: ` +op { + graph_op_name: "SampleDistortedBoundingBox" + in_arg { + name: "image_size" + description: "Blah blah" + } + in_arg { + name: "bounding_boxes" + description: "Blah blah" + } + out_arg { + name: "begin" + description: "Blah blah" + } + out_arg { + name: "size" + description: "Blah blah" + } + out_arg { + name: "bboxes" + description: "Blah blah" + } + attr { + name: "seed" + description: "Blah blah" + } + attr { + name: "seed2" + description: "Blah blah" + } + attr { + name: "min_object_covered" + description: "Blah blah" + } + attr { + name: "aspect_ratio_range" + description: "Blah blah" + } + attr { + name: "area_range" + description: "Blah blah" + } + attr { + name: "max_attempts" + description: "Blah blah" + } + attr { + name: "use_image_if_no_bounding_boxes" + description: "Blah blah" + } + summary: "Generate a single randomly distorted bounding box for an image." + description: "Blah blah" +} +`, + wanted: ` +// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. +type SampleDistortedBoundingBoxAttr func(optionalAttr) + +// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. +// +// value: Blah blah +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. +// +// value: Blah blah +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. +// +// value: Blah blah +// If not specified, defaults to 0.1 +func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["min_object_covered"] = value + } +} + +// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. +// +// value: Blah blah +// If not specified, defaults to +func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["aspect_ratio_range"] = value + } +} + +// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. +// +// value: Blah blah +// If not specified, defaults to +func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["area_range"] = value + } +} + +// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. +// +// value: Blah blah +// If not specified, defaults to 100 +func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["max_attempts"] = value + } +} + +// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// +// value: Blah blah +// If not specified, defaults to false +func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["use_image_if_no_bounding_boxes"] = value + } +} + +// Generate a single randomly distorted bounding box for an image. +// +// Blah blah +// +// Arguments: +// image_size: Blah blah +// bounding_boxes: Blah blah +// +// Returns: +// begin: Blah blah +// size: Blah blah +// bboxes: Blah blah +func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SampleDistortedBoundingBox", + Input: []tf.Input{ + image_size, bounding_boxes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} +`, + }, + } + + for _, test := range testdata { + t.Run(test.tag, func(t *testing.T) { + var opdef odpb.OpDef + var apidef *adpb.ApiDef + var buf bytes.Buffer + if err := proto.UnmarshalText(test.opdef, &opdef); err != nil { + t.Fatal(err) + } + apidef = GetAPIDef(t, &opdef, test.apidef) + if err := generateFunctionForOp(&buf, &opdef, apidef); err != nil { + t.Fatal(err) + } + got, err := format.Source(buf.Bytes()) + if err != nil { + t.Fatalf("Unable to format: %v\n%s", err, buf.Bytes()) + } + want, err := format.Source([]byte(test.wanted)) + if err != nil { + t.Fatalf("Unable to format: %v\n%s", err, test.wanted) + } + if !bytes.Equal(got, want) { + t.Fatalf("Got:\n%s\nWant:\n%s\n", got, want) + } + }) + } +} diff --git a/tensorflow/go/genop/internal/lib.go b/tensorflow/go/genop/internal/lib.go new file mode 100644 index 0000000..0ae6fd0 --- /dev/null +++ b/tensorflow/go/genop/internal/lib.go @@ -0,0 +1,22 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// Package internal generates Go source code with functions for TensorFlow operations. +package internal + +// #cgo LDFLAGS: -ltensorflow +// #cgo CFLAGS: -I${SRCDIR}/../../../../ +import "C" diff --git a/tensorflow/go/genop/main.go b/tensorflow/go/genop/main.go new file mode 100644 index 0000000..4a53084 --- /dev/null +++ b/tensorflow/go/genop/main.go @@ -0,0 +1,72 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +//go:generate bash generate.sh + +// Command genop generates a Go source file with functions for TensorFlow ops. +package main + +import ( + "bytes" + "flag" + "go/format" + "io/ioutil" + "log" + "os" + "path/filepath" + "strings" + + "github.com/tensorflow/tensorflow/tensorflow/go/genop/internal" +) + +func main() { + var ( + filename = flag.String("outfile", "", "File to write generated source code to.") + header = flag.String("header", "", "Path to a file whose contents will be copied into the generated file. Can be empty") + apiDefDirs = flag.String("api_def_dirs", "", "Comma-separated directories containing api_def_*.pbtxt files.") + buf bytes.Buffer + ) + flag.Parse() + if *filename == "" { + log.Fatal("-outfile must be set") + } + if *header != "" { + hdr, err := ioutil.ReadFile(*header) + if err != nil { + log.Fatalf("Unable to read %s: %v", *header, err) + } + buf.Write(hdr) + buf.WriteString("\n\n") + } + os.MkdirAll(filepath.Dir(*filename), 0755) + + apiDefDirsList := []string{} + if len(*apiDefDirs) > 0 { + apiDefDirsList = strings.Split(*apiDefDirs, ",") + } + + if err := internal.GenerateFunctionsForRegisteredOps( + &buf, apiDefDirsList); err != nil { + log.Fatal(err) + } + formatted, err := format.Source(buf.Bytes()) + if err != nil { + log.Fatalf("Failed to generate valid source? 'go fmt' failed: %v", err) + } + if err := ioutil.WriteFile(*filename, formatted, 0644); err != nil { + log.Fatalf("Failed to write to %q: %v", *filename, err) + } +} diff --git a/tensorflow/go/graph.go b/tensorflow/go/graph.go new file mode 100644 index 0000000..b3b2c9c --- /dev/null +++ b/tensorflow/go/graph.go @@ -0,0 +1,467 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include "tensorflow/c/c_api.h" +// +// #include +// #include +// +// void TF_SetAttrShapeList_Helper(TF_OperationDescription* desc, +// const char* attr_name, +// const int64_t* flat_dims, +// const int* num_dims, +// int num_shapes) { +// const int64_t** dims = +// (const int64_t**)malloc(sizeof(const int64_t*) * num_shapes); +// int i = 0; +// for (i = 0; i < num_shapes; i++) { +// dims[i] = flat_dims; +// if (num_dims[i] > 0) { +// // flat_dims will be NULL iff num_shapes is 0 or all elements in num_dims are <= 0. +// flat_dims += num_dims[i]; +// } +// } +// TF_SetAttrShapeList(desc, attr_name, dims, num_dims, num_shapes); +// free(dims); +// } +import "C" + +import ( + "fmt" + "io" + "runtime" + "unsafe" +) + +// Graph represents a computation graph. Graphs may be shared between sessions. +type Graph struct { + c *C.TF_Graph +} + +// The GraphImportOptions struct holds parameters for the ImportWithOptions function. +type GraphImportOptions struct { + // Node prefix + Prefix string + + // Execution device + Device string + + // TODO: extend this structure to support more options from TF_ImportGraphDefOptions +} + +// NewGraph returns a new Graph. +func NewGraph() *Graph { + g := &Graph{C.TF_NewGraph()} + runtime.SetFinalizer(g, (*Graph).finalizer) + return g +} + +func (g *Graph) finalizer() { + C.TF_DeleteGraph(g.c) +} + +// WriteTo writes out a serialized representation of g to w. +// +// Implements the io.WriterTo interface. +func (g *Graph) WriteTo(w io.Writer) (int64, error) { + buf := C.TF_NewBuffer() + defer C.TF_DeleteBuffer(buf) + status := newStatus() + C.TF_GraphToGraphDef(g.c, buf, status.c) + if err := status.Err(); err != nil { + return 0, err + } + if buf.length > (1 << 30) { + // For very large graphs, the writes can be chunked. + // Punt on that for now. + return 0, fmt.Errorf("Graph is too large to write out, Graph.WriteTo needs to be updated") + } + // A []byte slice backed by C memory. + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + length := int(buf.length) + var slice []byte + if unsafe.Sizeof(unsafe.Pointer(nil)) == 8 { + slice = (*[1<<50 - 1]byte)(unsafe.Pointer(buf.data))[:length:length] + } else { + slice = (*[1 << 30]byte)(unsafe.Pointer(buf.data))[:length:length] + } + n, err := w.Write(slice) + return int64(n), err +} + +// ImportWithOptions imports the nodes and edges from a serialized representation of +// another Graph into g. +// +// Multiple options can be specified for the newly imported nodes. +func (g *Graph) ImportWithOptions(def []byte, options GraphImportOptions) error { + cprefix := C.CString(options.Prefix) + defer C.free(unsafe.Pointer(cprefix)) + + opts := C.TF_NewImportGraphDefOptions() + defer C.TF_DeleteImportGraphDefOptions(opts) + C.TF_ImportGraphDefOptionsSetPrefix(opts, cprefix) + + if len(options.Device) != 0 { + cdev := C.CString(options.Device) + defer C.free(unsafe.Pointer(cdev)) + C.TF_ImportGraphDefOptionsSetDefaultDevice(opts, cdev) + } + + buf := C.TF_NewBuffer() + defer C.TF_DeleteBuffer(buf) + buf.length = C.size_t(len(def)) + buf.data = C.CBytes(def) + if buf.data == nil { + return fmt.Errorf("unable to allocate memory") + } + defer C.free(buf.data) + + status := newStatus() + + C.TF_GraphImportGraphDef(g.c, buf, opts, status.c) + if err := status.Err(); err != nil { + return err + } + + return nil +} + +// Import imports the nodes and edges from a serialized representation of +// another Graph into g. +// +// Names of imported nodes will be prefixed with prefix. +func (g *Graph) Import(def []byte, prefix string) error { + return g.ImportWithOptions(def, GraphImportOptions{Prefix: prefix}) +} + +// Operation returns the Operation named name in the Graph, or nil if no such +// operation is present. +func (g *Graph) Operation(name string) *Operation { + cname := C.CString(name) + defer C.free(unsafe.Pointer(cname)) + cop := C.TF_GraphOperationByName(g.c, cname) + if cop == nil { + return nil + } + return &Operation{cop, g} +} + +// Operations returns a list of all operations in the graph +func (g *Graph) Operations() []Operation { + var pos C.size_t + ops := []Operation{} + for { + cop := C.TF_GraphNextOperation(g.c, &pos) + if cop == nil { + break + } + ops = append(ops, Operation{cop, g}) + } + return ops +} + +// AddGradients adds operations to compute the partial derivatives of the sum of tensors in y +// with respect to tensors in x, i.e., d(y[0] + y[1] + ...) / d x[0], d(y[0] + y[1] + ... ) / d x[1] etc. +// +// prefix, if non-empty, is the name prefix used for all operations added to the graph to compute +// these gradients. +func (g *Graph) AddGradients(prefix string, y []Output, x []Output, dx []Output) ([]Output, error) { + var ( + cprefix *C.char + + cy = make([]C.TF_Output, len(y)) + cx = make([]C.TF_Output, len(x)) + cdx = make([]C.TF_Output, len(dx)) + cdy = make([]C.TF_Output, len(x)) + + pcy *C.TF_Output + pcx *C.TF_Output + pcdx *C.TF_Output + pcdy *C.TF_Output + + status = newStatus() + ) + + if len(y) > 0 { + pcy = &cy[0] + for i, o := range y { + cy[i] = o.c() + } + } + if len(x) > 0 { + pcx = &cx[0] + for i, o := range x { + cx[i] = o.c() + } + pcdy = &cdy[0] + } + if len(dx) > 0 { + pcdx = &cdx[0] + for i, o := range dx { + cdx[i] = o.c() + } + } + + // If prefix is "", the C.TF_AddGradientsWithPrefix need cprefix to be nil but not "" + if len(prefix) != 0 { + cprefix = C.CString(prefix) + defer C.free(unsafe.Pointer(cprefix)) + } + + C.TF_AddGradientsWithPrefix(g.c, cprefix, pcy, C.int(len(y)), pcx, C.int(len(x)), pcdx, status.c, pcdy) + + if err := status.Err(); err != nil { + return nil, err + } + dy := make([]Output, len(x)) + for i, co := range cdy { + op := &Operation{co.oper, g} + dy[i] = Output{op, int(co.index)} + } + + return dy, nil +} + +// OpSpec is the specification of an Operation to be added to a Graph +// (using Graph.AddOperation). +type OpSpec struct { + // Type of the operation (e.g., "Add", "MatMul"). + Type string + + // Name by which the added operation will be referred to in the Graph. + // If omitted, defaults to Type. + Name string + + // Inputs to this operation, which in turn must be outputs + // of other operations already added to the Graph. + // + // An operation may have multiple inputs with individual inputs being + // either a single tensor produced by another operation or a list of + // tensors produced by multiple operations. For example, the "Concat" + // operation takes two inputs: (1) the dimension along which to + // concatenate and (2) a list of tensors to concatenate. Thus, for + // Concat, len(Input) must be 2, with the first element being an Output + // and the second being an OutputList. + Input []Input + + // Map from attribute name to its value that will be attached to this + // operation. + Attrs map[string]interface{} + + // Operations that must be executed before executing the operation + // being added. + ControlDependencies []*Operation + + // The device on which the operation should be executed. + // If omitted, an appropriate device will automatically be selected. + // + // For example, if set of "/device:GPU:0", then the operation will + // execute on GPU #0. + Device string + + // Other possible fields: ColocateWith. +} + +// AddOperation adds an operation to g. +func (g *Graph) AddOperation(args OpSpec) (*Operation, error) { + if args.Name == "" { + args.Name = args.Type + } + cname := C.CString(args.Name) + ctype := C.CString(args.Type) + cdesc := C.TF_NewOperation(g.c, ctype, cname) + C.free(unsafe.Pointer(cname)) + C.free(unsafe.Pointer(ctype)) + + for _, in := range args.Input { + switch in := in.(type) { + case Output: + C.TF_AddInput(cdesc, in.c()) + case OutputList: + size := len(in) + list := make([]C.TF_Output, size) + for i, v := range in { + list[i] = v.c() + } + if size > 0 { + C.TF_AddInputList(cdesc, &list[0], C.int(size)) + } else { + C.TF_AddInputList(cdesc, nil, 0) + } + } + } + for _, in := range args.ControlDependencies { + C.TF_AddControlInput(cdesc, in.c) + } + status := newStatus() + for name, value := range args.Attrs { + if err := setAttr(cdesc, status, name, value); err != nil { + // Memory leak here as the TF_OperationDescription + // object will not be cleaned up. At the time of this + // writing, this was next to impossible since it + // required value to be a string tensor with + // incorrectly encoded strings. Given this rarity, live + // with the memory leak. If it becomes a real problem, + // consider adding a TF_DeleteOperationDescription + // function to the C API. + return nil, fmt.Errorf("%v (memory will be leaked)", err) + } + } + if len(args.Device) > 0 { + cdevice := C.CString(args.Device) + C.TF_SetDevice(cdesc, cdevice) + C.free(unsafe.Pointer(cdevice)) + } + c := C.TF_FinishOperation(cdesc, status.c) + if err := status.Err(); err != nil { + return nil, err + } + return &Operation{c, g}, nil +} + +func setAttr(cdesc *C.TF_OperationDescription, status *status, name string, value interface{}) error { + cAttrName := C.CString(name) + defer C.free(unsafe.Pointer(cAttrName)) + switch value := value.(type) { + case string: + cstr := C.CString(value) + C.TF_SetAttrString(cdesc, cAttrName, unsafe.Pointer(cstr), C.size_t(len(value))) + C.free(unsafe.Pointer(cstr)) + case []string: + size := len(value) + list := make([]unsafe.Pointer, size) + lens := make([]C.size_t, size) + for i, s := range value { + list[i] = unsafe.Pointer(C.CString(s)) + lens[i] = C.size_t(len(s)) + } + if size > 0 { + C.TF_SetAttrStringList(cdesc, cAttrName, &list[0], &lens[0], C.int(size)) + } else { + C.TF_SetAttrStringList(cdesc, cAttrName, nil, nil, 0) + } + for _, s := range list { + C.free(s) + } + case int64: + C.TF_SetAttrInt(cdesc, cAttrName, C.int64_t(value)) + case []int64: + size := len(value) + list := make([]C.int64_t, size) + for i, v := range value { + list[i] = C.int64_t(v) + } + if size > 0 { + C.TF_SetAttrIntList(cdesc, cAttrName, &list[0], C.int(size)) + } else { + C.TF_SetAttrIntList(cdesc, cAttrName, nil, 0) + } + case float32: + C.TF_SetAttrFloat(cdesc, cAttrName, C.float(value)) + case []float32: + size := len(value) + list := make([]C.float, size) + for i, v := range value { + list[i] = C.float(v) + } + if size > 0 { + C.TF_SetAttrFloatList(cdesc, cAttrName, &list[0], C.int(size)) + } else { + C.TF_SetAttrFloatList(cdesc, cAttrName, nil, 0) + } + case bool: + v := C.uchar(0) + if value { + v = 1 + } + C.TF_SetAttrBool(cdesc, cAttrName, v) + case []bool: + size := len(value) + list := make([]C.uchar, size) + for i, v := range value { + if v { + list[i] = 1 + } + } + if size > 0 { + C.TF_SetAttrBoolList(cdesc, cAttrName, &list[0], C.int(size)) + } else { + C.TF_SetAttrBoolList(cdesc, cAttrName, nil, 0) + } + case DataType: + C.TF_SetAttrType(cdesc, cAttrName, C.TF_DataType(value)) + case []DataType: + var list *C.TF_DataType + if len(value) > 0 { + list = (*C.TF_DataType)(&value[0]) + } + C.TF_SetAttrTypeList(cdesc, cAttrName, list, C.int(len(value))) + case *Tensor: + C.TF_SetAttrTensor(cdesc, cAttrName, value.c, status.c) + if err := status.Err(); err != nil { + return fmt.Errorf("bad value for attribute %q: %v", name, err) + } + case []*Tensor: + size := len(value) + list := make([]*C.TF_Tensor, size) + for i, v := range value { + list[i] = v.c + } + var plist **C.TF_Tensor + if size > 0 { + plist = &list[0] + } + C.TF_SetAttrTensorList(cdesc, cAttrName, plist, C.int(size), status.c) + if err := status.Err(); err != nil { + return fmt.Errorf("bad value for attribute %q: %v", name, err) + } + case Shape: + ndims := C.int(value.NumDimensions()) + var dimsp *C.int64_t + if ndims > 0 { + dims := make([]C.int64_t, ndims) + for i, d := range value.dims { + dims[i] = C.int64_t(d) + } + dimsp = &dims[0] + } + C.TF_SetAttrShape(cdesc, cAttrName, dimsp, ndims) + case []Shape: + if len(value) == 0 { + C.TF_SetAttrShapeList(cdesc, cAttrName, nil, nil, 0) + } else { + var flatDims []C.int64_t + ndims := make([]C.int, len(value)) + for i, s := range value { + nd := s.NumDimensions() + ndims[i] = C.int(nd) + for _, d := range s.dims { + flatDims = append(flatDims, C.int64_t(d)) + } + } + var flatDimsp *C.int64_t + if len(flatDims) > 0 { + flatDimsp = &flatDims[0] + } + C.TF_SetAttrShapeList_Helper(cdesc, cAttrName, flatDimsp, &ndims[0], C.int(len(value))) + } + default: + return fmt.Errorf("attribute %q has a type (%T) which is not valid for operation attributes", name, value) + } + return nil +} diff --git a/tensorflow/go/graph_test.go b/tensorflow/go/graph_test.go new file mode 100644 index 0000000..067c7db --- /dev/null +++ b/tensorflow/go/graph_test.go @@ -0,0 +1,340 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "bytes" + "fmt" + "strings" + "testing" +) + +func hasOperations(g *Graph, ops ...string) error { + var missing []string + for _, op := range ops { + if g.Operation(op) == nil { + missing = append(missing, op) + } + } + if len(missing) != 0 { + return fmt.Errorf("Graph does not have the operations %v", missing) + } + + inList := map[string]bool{} + for _, op := range g.Operations() { + inList[op.Name()] = true + } + + for _, op := range ops { + if !inList[op] { + missing = append(missing, op) + } + } + + if len(missing) != 0 { + return fmt.Errorf("Operations %v are missing from graph.Operations()", missing) + } + + return nil +} + +func TestGraphWriteToAndImport(t *testing.T) { + // Construct a graph + g := NewGraph() + v, err := NewTensor(int64(1)) + if err != nil { + t.Fatal(err) + } + input, err := Placeholder(g, "input", v.DataType()) + if err != nil { + t.Fatal(err) + } + if _, err := Neg(g, "neg", input); err != nil { + t.Fatal(err) + } + + // Serialize the graph + buf := new(bytes.Buffer) + if _, err := g.WriteTo(buf); err != nil { + t.Fatal(err) + } + + // Import it into the same graph, with a prefix + if err := g.Import(buf.Bytes(), "imported"); err != nil { + t.Error(err) + } + if err := hasOperations(g, "input", "neg", "imported/input", "imported/neg"); err != nil { + t.Error(err) + } +} + +func TestGraphAddGradients(t *testing.T) { + g := NewGraph() + x1, err := Placeholder(g, "x1", Float) + if err != nil { + t.Fatal(err) + } + x2, err := Placeholder(g, "x2", Float) + if err != nil { + t.Fatal(err) + } + op0, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y0", + Input: []Input{x1}, + }) + if err != nil { + t.Fatal(err) + } + y0 := op0.Output(0) + op1, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y1", + Input: []Input{y0}, + }) + if err != nil { + t.Fatal(err) + } + y1 := op1.Output(0) + op2, err := g.AddOperation(OpSpec{ + Type: "AddN", + Input: []Input{OutputList([]Output{y0, x2})}, + }) + if err != nil { + t.Fatal(err) + } + y2 := op2.Output(0) + + grads0, err := g.AddGradients("", []Output{y1}, []Output{x1}, nil) + if err != nil { + t.Fatal(err) + } + if len(grads0) != 1 { + t.Fatal(len(grads0)) + } + if grads0[0].DataType() != Float { + t.Fatalf("Got DataType %v, wanted %v", grads0[0].DataType(), Float) + } + + grads1, err := g.AddGradients("", []Output{y2}, []Output{x1, x2}, nil) + if err != nil { + t.Fatal(err) + } + if len(grads1) != 2 { + t.Fatal(len(grads1)) + } + if grads1[0].DataType() != Float { + t.Fatalf("Got DataType %v, wanted %v", grads1[0].DataType(), Float) + } + if grads1[1].DataType() != Float { + t.Fatalf("Got DataType %v, wanted %v", grads1[1].DataType(), Float) + } + + sess, err := NewSession(g, nil) + if err != nil { + t.Fatal(err) + } + + c1, _ := NewTensor(float32(3.0)) + c2, _ := NewTensor(float32(2.0)) + outputs, err := sess.Run( + map[Output]*Tensor{x1: c1, x2: c2}, + []Output{grads0[0], grads1[0], grads1[1]}, + nil) + if err != nil { + t.Fatal(err) + } + if len(outputs) != 3 { + t.Fatal(len(outputs)) + } + if outputs[0].Value().(float32) != 108.0 { + t.Fatalf("Got %v, wanted float 108.0", outputs[0].Value()) + } + if outputs[1].Value().(float32) != 6.0 { + t.Fatalf("Got %v, wanted float 6.0", outputs[1].Value()) + } + if outputs[2].Value().(float32) != 1.0 { + t.Fatalf("Got %v, wanted float 1.0", outputs[2].Value()) + } +} + +func TestGraphAddGradientsSums(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + if err != nil { + t.Fatal(err) + } + op0, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y0", + Input: []Input{x}, + }) + if err != nil { + t.Fatal(err) + } + y0 := op0.Output(0) + op1, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y1", + Input: []Input{y0}, + }) + y1 := op1.Output(0) + + grad, err := g.AddGradients("", []Output{y0, y1}, []Output{x}, nil) + if err != nil { + t.Fatal(err) + } + if len(grad) != 1 { + t.Fatal(len(grad)) + } + if grad[0].DataType() != Float { + t.Fatalf("Got DataType %v, wanted %v", grad[0].DataType(), Float) + } + + sess, err := NewSession(g, nil) + if err != nil { + t.Fatal(err) + } + + c, _ := NewTensor(float32(3.0)) + outputs, err := sess.Run( + map[Output]*Tensor{x: c}, + []Output{grad[0]}, + nil) + if err != nil { + t.Fatal(err) + } + if outputs[0].Value().(float32) != 114.0 { + t.Fatalf("Got %v, wanted float 114.0", outputs[0].Value()) + } +} + +func TestGraphAddGradientsWithInitialValues(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + op0, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y0", + Input: []Input{x}, + }) + if err != nil { + t.Fatal(err) + } + y0 := op0.Output(0) + op1, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y1", + Input: []Input{y0}, + }) + if err != nil { + t.Fatal(err) + } + y1 := op1.Output(0) + + grads0, err := g.AddGradients("", []Output{y1}, []Output{y0}, nil) + if err != nil { + t.Fatal(err) + } + if len(grads0) != 1 { + t.Fatal(len(grads0)) + } + if grads0[0].DataType() != Float { + t.Fatalf("Got DataType %v, wanted %v", grads0[0].DataType(), Float) + } + + grads1, err := g.AddGradients("", []Output{y0}, []Output{x}, []Output{grads0[0]}) + if err != nil { + t.Fatal(err) + } + if len(grads1) != 1 { + t.Fatal(len(grads1)) + } + if grads1[0].DataType() != Float { + t.Fatalf("Got DataType %v, wanted %v", grads1[0].DataType(), Float) + } + + sess, err := NewSession(g, nil) + if err != nil { + t.Fatal(err) + } + + c, _ := NewTensor(float32(3.0)) + outputs, err := sess.Run( + map[Output]*Tensor{x: c}, + []Output{grads1[0]}, + nil) + if err != nil { + t.Fatal(err) + } + if outputs[0].Value().(float32) != 108.0 { + t.Fatalf("Got %v, wanted float 108.0", outputs[0].Value()) + } +} + +func TestGraphValidateGradientsNames(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + if err != nil { + t.Fatal(err) + } + op0, err := g.AddOperation(OpSpec{ + Type: "Square", + Name: "y0", + Input: []Input{x}, + }) + if err != nil { + t.Fatal(err) + } + y0 := op0.Output(0) + + grads0, err := g.AddGradients("", []Output{y0}, []Output{x}, nil) + if err != nil { + t.Fatal(err) + } + if !strings.HasPrefix(grads0[0].Op.Name(), "gradients/") { + t.Fatalf("Got name %v, wanted started with gradients/", grads0[0].Op.Name()) + } + + grads1, err := g.AddGradients("", []Output{y0}, []Output{x}, nil) + if err != nil { + t.Fatal(err) + } + if !strings.HasPrefix(grads1[0].Op.Name(), "gradients_1/") { + t.Fatalf("Got name %v, wanted started with gradients_1/", grads1[0].Op.Name()) + } + + grads2, err := g.AddGradients("more_gradients", []Output{y0}, []Output{x}, nil) + if err != nil { + t.Fatal(err) + } + if !strings.HasPrefix(grads2[0].Op.Name(), "more_gradients/") { + t.Fatalf("Got name %v, wanted started with more_gradients/", grads2[0].Op.Name()) + } + + grads3, err := g.AddGradients("even_more_gradients", []Output{y0}, []Output{x}, nil) + if err != nil { + t.Fatal(err) + } + if !strings.HasPrefix(grads3[0].Op.Name(), "even_more_gradients/") { + t.Fatalf("Got name %v, wanted started with even_more_gradients/", grads3[0].Op.Name()) + } + + _, err = g.AddGradients("even_more_gradients", []Output{y0}, []Output{x}, nil) + if err == nil { + t.Error("AddGradients should have failed if gradients name is already existing") + } +} diff --git a/tensorflow/go/lib.go b/tensorflow/go/lib.go new file mode 100644 index 0000000..2800ede --- /dev/null +++ b/tensorflow/go/lib.go @@ -0,0 +1,21 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #cgo LDFLAGS: -ltensorflow +// #cgo CFLAGS: -I${SRCDIR}/../../ +import "C" diff --git a/tensorflow/go/op/generate.go b/tensorflow/go/op/generate.go new file mode 100644 index 0000000..e5a9bea --- /dev/null +++ b/tensorflow/go/op/generate.go @@ -0,0 +1,20 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +//go:generate go generate ../genop +//go:generate go run ../genop/main.go -outfile wrappers.go -api_def_dirs ../../core/api_def/base_api/ + +package op diff --git a/tensorflow/go/op/gradients.go b/tensorflow/go/op/gradients.go new file mode 100644 index 0000000..c595678 --- /dev/null +++ b/tensorflow/go/op/gradients.go @@ -0,0 +1,49 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package op + +import ( + "fmt" + + tf "github.com/tensorflow/tensorflow/tensorflow/go" +) + +// Gradients adds gradients computation ops to the graph according to scope. +// +// Arguments: +// y: output of the function to derive +// x: inputs of the function for which partial derivatives are computed +// dx: if not null, the partial derivatives of some loss function L w.r.t. y +// +// return the partial derivatives +func Gradients(scope *Scope, y []tf.Output, x []tf.Output, dx ...tf.Output) (output []tf.Output) { + if len(scope.controlDependencies) > 0 { + scope.UpdateErr("Gradients", fmt.Errorf("Gradients does not currently support control dependencies (via Scope.WithControlDependencies).")) + return + } + if scope.device != "" { + scope.UpdateErr("Gradients", fmt.Errorf("Gradients does not currently support device annotations (via Scope.WithDevice).")) + return + } + + var err error + if output, err = scope.graph.AddGradients(scope.opName("Gradients"), y, x, dx); err != nil { + scope.UpdateErr("Gradients", err) + return + } + return output +} diff --git a/tensorflow/go/op/gradients_test.go b/tensorflow/go/op/gradients_test.go new file mode 100644 index 0000000..3d1d57b --- /dev/null +++ b/tensorflow/go/op/gradients_test.go @@ -0,0 +1,246 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package op + +import ( + "strings" + "testing" + + tf "github.com/tensorflow/tensorflow/tensorflow/go" +) + +func TestAddGradients(t *testing.T) { + var ( + s = NewScope() + x1 = Placeholder(s.SubScope("x1"), tf.Float) + x2 = Placeholder(s.SubScope("x2"), tf.Float) + y0 = Square(s.SubScope("y0"), x1) + y1 = Square(s.SubScope("y1"), y0) + y2 = AddN(s.SubScope("y2"), []tf.Output{y0, x2}) + ) + + grads0 := Gradients(s, []tf.Output{y1}, []tf.Output{x1}) + if err := s.Err(); err != nil { + t.Fatal(err) + } + if len(grads0) != 1 { + t.Fatal(len(grads0)) + } + if grads0[0].DataType() != tf.Float { + t.Fatalf("Got DataType %v, wanted %v", grads0[0].DataType(), tf.Float) + } + + sub := s.SubScope("sub") + grads1 := Gradients(sub, []tf.Output{y2}, []tf.Output{x1, x2}) + if err := sub.Err(); err != nil { + t.Fatal(err) + } + if len(grads1) != 2 { + t.Fatal(len(grads1)) + } + if grads1[0].DataType() != tf.Float { + t.Fatalf("Got DataType %v, wanted %v", grads1[0].DataType(), tf.Float) + } + if grads1[1].DataType() != tf.Float { + t.Fatalf("Got DataType %v, wanted %v", grads1[1].DataType(), tf.Float) + } + + graph, err := sub.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + + c1, _ := tf.NewTensor(float32(3.0)) + c2, _ := tf.NewTensor(float32(3.0)) + outputs, err := sess.Run( + map[tf.Output]*tf.Tensor{x1: c1, x2: c2}, + []tf.Output{grads0[0], grads1[0], grads1[1]}, + nil) + if err != nil { + t.Fatal(err) + } + if len(outputs) != 3 { + t.Fatal(len(outputs)) + } + if outputs[0].Value().(float32) != 108.0 { + t.Fatalf("Got %v, wanted float 108.0", outputs[0].Value()) + } + if outputs[1].Value().(float32) != 6.0 { + t.Fatalf("Got %v, wanted float 6.0", outputs[1].Value()) + } + if outputs[2].Value().(float32) != 1.0 { + t.Fatalf("Got %v, wanted float 1.0", outputs[2].Value()) + } +} + +func TestAddGradientsSums(t *testing.T) { + var ( + s = NewScope() + x = Placeholder(s.SubScope("x"), tf.Float) + y0 = Square(s.SubScope("y0"), x) + y1 = Square(s.SubScope("y1"), y0) + ) + + grad := Gradients(s, []tf.Output{y0, y1}, []tf.Output{x}) + if err := s.Err(); err != nil { + t.Fatal(err) + } + if len(grad) != 1 { + t.Fatal(len(grad)) + } + if grad[0].DataType() != tf.Float { + t.Fatalf("Got DataType %v, wanted %v", grad[0].DataType(), tf.Float) + } + + graph, err := s.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + + c, _ := tf.NewTensor(float32(3.0)) + outputs, err := sess.Run( + map[tf.Output]*tf.Tensor{x: c}, + []tf.Output{grad[0]}, + nil) + if err != nil { + t.Fatal(err) + } + if outputs[0].Value().(float32) != 114.0 { + t.Fatalf("Got %v, wanted float 114.0", outputs[0].Value()) + } +} + +func TestAddGradientsWithInitialValues(t *testing.T) { + var ( + s = NewScope() + x = Placeholder(s.SubScope("x1"), tf.Float) + y0 = Square(s.SubScope("y0"), x) + y1 = Square(s.SubScope("y1"), y0) + ) + + grads0 := Gradients(s, []tf.Output{y1}, []tf.Output{y0}) + if err := s.Err(); err != nil { + t.Fatal(err) + } + if len(grads0) != 1 { + t.Fatal(len(grads0)) + } + if grads0[0].DataType() != tf.Float { + t.Fatalf("Got DataType %v, wanted %v", grads0[0].DataType(), tf.Float) + } + + sub := s.SubScope("sub") + grads1 := Gradients(sub, []tf.Output{y0}, []tf.Output{x}, grads0[0]) + if err := sub.Err(); err != nil { + t.Fatal(err) + } + if len(grads1) != 1 { + t.Fatal(len(grads1)) + } + if grads1[0].DataType() != tf.Float { + t.Fatalf("Got DataType %v, wanted %v", grads1[0].DataType(), tf.Float) + } + + graph, err := sub.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + + c, _ := tf.NewTensor(float32(3.0)) + outputs, err := sess.Run( + map[tf.Output]*tf.Tensor{x: c}, + []tf.Output{grads1[0]}, + nil) + if err != nil { + t.Fatal(err) + } + if outputs[0].Value().(float32) != 108.0 { + t.Fatalf("Got %v, wanted float 108.0", outputs[0].Value()) + } +} + +func TestValidateGradientsNames(t *testing.T) { + var ( + s = NewScope() + x = Placeholder(s.SubScope("x"), tf.Float) + y0 = Square(s.SubScope("y0"), x) + ) + + grads0 := Gradients(s, []tf.Output{y0}, []tf.Output{x}) + if err := s.Err(); err != nil { + t.Fatal(err) + } + if !strings.HasPrefix(grads0[0].Op.Name(), "Gradients/") { + t.Fatalf("Got name %v, wanted started with Gradients/", grads0[0].Op.Name()) + } + + sub := s.SubScope("sub") + grads1 := Gradients(sub, []tf.Output{y0}, []tf.Output{x}) + if err := s.Err(); err != nil { + t.Fatal(err) + } + if !strings.HasPrefix(grads1[0].Op.Name(), "sub/Gradients/") { + t.Fatalf("Got name %v, wanted started with sub/Gradients/", grads1[0].Op.Name()) + } + + Gradients(sub, []tf.Output{y0}, []tf.Output{x}) + if err := s.Err(); err == nil { + t.Error("Gradients should have failed if executed more than once for scope of the same namespace") + } +} + +func TestAddGradientsWithControlDependencies(t *testing.T) { + var ( + s = NewScope() + zero = Const(s.SubScope("zero"), int32(0)) + x = Placeholder(s.SubScope("x"), tf.Float) + y0 = Square(s.SubScope("y0"), x) + variable = VarHandleOp(s, tf.Int32, tf.ScalarShape()) + init = AssignVariableOp(s, variable, zero) + readDeps = []*tf.Operation{init} + ) + s = s.WithControlDependencies(readDeps...) + Gradients(s, []tf.Output{y0}, []tf.Output{x}) + if err := s.Err(); err == nil { + t.Error("Gradients should have failed when control dependencies are set") + } +} + +func TestAddGradientsWithDevice(t *testing.T) { + var ( + s = NewScope() + x = Placeholder(s.SubScope("x"), tf.Float) + y0 = Square(s.SubScope("y0"), x) + ) + s = s.WithDevice("/device:GPU:0") + Gradients(s, []tf.Output{y0}, []tf.Output{x}) + if err := s.Err(); err == nil { + t.Error("Gradients should have failed when device is set") + } +} diff --git a/tensorflow/go/op/op.go b/tensorflow/go/op/op.go new file mode 100644 index 0000000..1c20bd4 --- /dev/null +++ b/tensorflow/go/op/op.go @@ -0,0 +1,51 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// Package op defines functions for adding TensorFlow operations to a Graph. +// +// Functions for adding an operation to a graph take a Scope object as the +// first argument. The Scope object encapsulates a graph and a set of +// properties (such as a name prefix) for all operations being added +// to the graph. +// +// WARNING: The API in this package has not been finalized and can +// change without notice. +package op + +import ( + tf "github.com/tensorflow/tensorflow/tensorflow/go" +) + +// Const adds an operation to graph that produces value as output. +func Const(scope *Scope, value interface{}) (output tf.Output) { + if scope.Err() != nil { + return + } + t, ok := value.(*tf.Tensor) + if !ok { + var err error + if t, err = tf.NewTensor(value); err != nil { + scope.UpdateErr("Const", err) + return + } + } + return scope.AddOperation(tf.OpSpec{ + Type: "Const", + Attrs: map[string]interface{}{ + "dtype": t.DataType(), + "value": t, + }}).Output(0) +} diff --git a/tensorflow/go/op/op_test.go b/tensorflow/go/op/op_test.go new file mode 100644 index 0000000..842dee9 --- /dev/null +++ b/tensorflow/go/op/op_test.go @@ -0,0 +1,133 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// Tests for the generated code of some operations. + +package op + +import ( + "strings" + "testing" + + tf "github.com/tensorflow/tensorflow/tensorflow/go" +) + +func TestPlaceholder(t *testing.T) { + s := NewScope() + Placeholder(s.SubScope("x"), tf.Float, PlaceholderShape(tf.MakeShape(-1, 10))) + Placeholder(s.SubScope("y"), tf.Float, PlaceholderShape(tf.ScalarShape())) + Placeholder(s.SubScope("z"), tf.Float, PlaceholderShape(tf.Shape{})) + if _, err := s.Finalize(); err != nil { + t.Fatal(err) + } +} + +func TestAddOperationFailure(t *testing.T) { + // Inspired from https://github.com/tensorflow/tensorflow/issues/9931 + s := NewScope() + + resize := ResizeArea(s, Placeholder(s, tf.Float), Const(s, []int64{80, 80})) + if err := s.Err(); err == nil { + t.Fatal("ResizeArea expects an int32 Tensor for size, should fail when an int64 is provided") + } + // And any use of resize should panic with an error message more informative than SIGSEGV + defer func() { + r := recover() + if r == nil { + return + } + s, ok := r.(string) + if ok && strings.Contains(s, "see Scope.Err() for details") { + return + } + t.Errorf("Expected panic string to Scope.Err(), found %T: %q", r, r) + }() + _ = resize.Shape() + t.Errorf("resize.Shape() should have paniced since the underlying Operation was not created") +} + +func TestShapeAttribute(t *testing.T) { + s := NewScope() + x := Placeholder(s.SubScope("x"), tf.Int32, PlaceholderShape(tf.MakeShape(1))) + y := Placeholder(s.SubScope("y"), tf.Int32, PlaceholderShape(tf.Shape{})) + z := Add(s, x, y) + graph, err := s.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + + value, err := tf.NewTensor([]int32{7}) + if err != nil { + t.Fatal(err) + } + feeds := map[tf.Output]*tf.Tensor{ + x: value, + y: value, + } + fetched, err := sess.Run(feeds, []tf.Output{z}, nil) + if err != nil { + t.Fatal(err) + } + if got, want := len(fetched), 1; got != want { + t.Fatalf("Fetched %d tensors, expected %d", got, want) + } + if got, want := fetched[0].Value().([]int32), []int32{14}; len(got) != len(want) || len(got) != 1 || got[0] != want[0] { + t.Fatalf("Got %v, want %v", got, want) + } +} + +func TestDataset(t *testing.T) { + var ( + s = NewScope() + + // The use of a non-scalar here is inspired by + // https://github.com/tensorflow/tensorflow/issues/14891 + c = Const(s, []int32{21718, 31415}) + types = []tf.DataType{c.DataType()} + shapes = []tf.Shape{c.Shape()} + dataset = TensorDataset(s, []tf.Output{c}, shapes) + + iterator = Iterator(s, "", "", types, shapes) + next = IteratorGetNext(s, iterator, types, shapes) + init = MakeIterator(s, dataset, iterator) + ) + graph, err := s.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + if _, err := sess.Run(nil, nil, []*tf.Operation{init}); err != nil { + t.Fatal(err) + } + results, err := sess.Run(nil, next, nil) + if err != nil { + t.Fatal(err) + } + got := results[0].Value().([]int32) + if len(got) != 2 || got[0] != 21718 || got[1] != 31415 { + t.Errorf("Got %v, want {21718, 31415}", got) + } + if _, err := sess.Run(nil, next, nil); err == nil { + t.Errorf("Expected sess.Run() to fail since the iterator should have reached the end of the dataset") + } +} diff --git a/tensorflow/go/op/scope.go b/tensorflow/go/op/scope.go new file mode 100644 index 0000000..83cc6e3 --- /dev/null +++ b/tensorflow/go/op/scope.go @@ -0,0 +1,185 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package op + +import ( + "fmt" + "runtime/debug" + + tf "github.com/tensorflow/tensorflow/tensorflow/go" +) + +// Scope encapsulates common operation properties when building a Graph. +// +// A Scope object (and its derivatives, e.g., obtained from Scope.SubScope) +// act as a builder for graphs. They allow common properties (such as +// a name prefix) to be specified for multiple operations being added +// to the graph. +// +// A Scope object and all its derivatives (e.g., obtained from Scope.SubScope) +// are not safe for concurrent use by multiple goroutines. +type Scope struct { + graph *tf.Graph + namemap map[string]int + namespace string + controlDependencies []*tf.Operation + device string + err *scopeErr +} + +// scopeErr is used to share errors between all derivatives of a root scope. +type scopeErr struct { + err error +} + +// NewScope creates a Scope initialized with an empty Graph. +func NewScope() *Scope { + return &Scope{graph: tf.NewGraph(), namemap: make(map[string]int), err: new(scopeErr)} +} + +// NewScopeWithGraph creates a Scope initialized with the Graph thats passed in +func NewScopeWithGraph(g *tf.Graph) *Scope { + return &Scope{graph: g, namemap: make(map[string]int), err: new(scopeErr)} +} + +// Finalize returns the Graph on which this scope operates on and renders s +// unusable. If there was an error during graph construction, that error is +// returned instead. +func (s *Scope) Finalize() (*tf.Graph, error) { + if err := s.Err(); err != nil { + return nil, err + } + s.err.err = fmt.Errorf("Scope has been finalized and is no longer usable") + return s.graph, nil +} + +// AddOperation adds the operation to the Graph managed by s. +// +// If there is a name prefix associated with s (such as if s was created +// by a call to SubScope), then this prefix will be applied to the name +// of the operation being added. See also Graph.AddOperation. +func (s *Scope) AddOperation(args tf.OpSpec) *tf.Operation { + if s.Err() != nil { + return nil + } + if args.Name == "" { + args.Name = args.Type + } + if s.namespace != "" { + args.Name = s.namespace + "/" + args.Name + } + args.ControlDependencies = append(args.ControlDependencies, s.controlDependencies...) + args.Device = s.device + op, err := s.graph.AddOperation(args) + if err != nil { + s.UpdateErr(args.Type, err) + } + return op +} + +// SubScope returns a new Scope which will cause all operations added to the +// graph to be namespaced with 'namespace'. If namespace collides with an +// existing namespace within the scope, then a suffix will be added. +func (s *Scope) SubScope(namespace string) *Scope { + namespace = s.uniqueName(namespace) + if s.namespace != "" { + namespace = s.namespace + "/" + namespace + } + return &Scope{ + graph: s.graph, + namemap: make(map[string]int), + namespace: namespace, + controlDependencies: s.controlDependencies, + device: s.device, + err: s.err, + } +} + +// WithControlDependencies returns a new Scope which will cause all operations +// added to the graph to execute only after all the provided operations have +// executed first (in addition to any other control dependencies in s). +func (s *Scope) WithControlDependencies(ops ...*tf.Operation) *Scope { + // Force a copy of the control dependencies into a new underlying array on + // every call. We cannot alias the same underlying array as `ops`, otherwise + // the user could modify that array after calling s.WithControlDependencies, + // which would be confusing. We cannot alias the same underlying array as the + // original `s.controlDependencies`, since Scopes form a logical tree, and + // other calls to s.WithControlDependencies could stomp on each other. + deps := make([]*tf.Operation, 0, len(s.controlDependencies)+len(ops)) + deps = append(deps, s.controlDependencies...) + deps = append(deps, ops...) + return &Scope{ + graph: s.graph, + namemap: s.namemap, + namespace: s.namespace, + controlDependencies: deps, + device: s.device, + err: s.err, + } +} + +// WithDevice returns a new Scope which will cause all operations added to the +// graph to execute on devices that match the provided device specification. +// +// For example, WithDevice("/device:GPU:0") will cause operations added to +// the graph to execute on GPU #0. +// +// An empty string removes any device restrictions. +func (s *Scope) WithDevice(device string) *Scope { + return &Scope{ + graph: s.graph, + namemap: s.namemap, + namespace: s.namespace, + controlDependencies: s.controlDependencies, + device: device, + err: s.err, + } +} + +// Err returns the error, if any, encountered during the construction +// of the Graph managed by s. +// +// Once Err returns a non-nil error, all future calls will do the same, +// indicating that the scope should be discarded as the graph could not +// be constructed. +func (s *Scope) Err() error { + return s.err.err +} + +// UpdateErr is used to notify Scope of any graph construction errors +// while creating the operation op. +func (s *Scope) UpdateErr(op string, err error) { + if s.err.err == nil { + s.err.err = fmt.Errorf("failed to add operation %q: %v (Stacktrace: %s)", op, err, debug.Stack()) + } +} + +func (s *Scope) uniqueName(name string) string { + count := s.namemap[name] + s.namemap[name]++ + if count == 0 { + return name + } + return fmt.Sprint(name, "_", count) +} + +func (s *Scope) opName(typ string) string { + if s.namespace == "" { + return typ + } + return s.namespace + "/" + typ +} diff --git a/tensorflow/go/op/scope_test.go b/tensorflow/go/op/scope_test.go new file mode 100644 index 0000000..be7b0ad --- /dev/null +++ b/tensorflow/go/op/scope_test.go @@ -0,0 +1,201 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package op + +import ( + "fmt" + "testing" + + tf "github.com/tensorflow/tensorflow/tensorflow/go" +) + +func TestScopeSubScope(t *testing.T) { + var ( + root = NewScope() + sub1 = root.SubScope("x") + sub2 = root.SubScope("x") + sub1a = sub1.SubScope("y") + sub2a = sub2.SubScope("y") + ) + testdata := []struct { + scope *Scope + name string + }{ + {root, "Const"}, + {sub1, "x/Const"}, + {sub1a, "x/y/Const"}, + {sub2, "x_1/Const"}, + {sub2a, "x_1/y/Const"}, + } + for _, test := range testdata { + c := Const(test.scope, int64(1)) + if err := test.scope.Err(); err != nil { + t.Fatalf("%q: %v", test.name, err) + } + if got := c.Op.Name(); got != test.name { + t.Errorf("%q: Got %q", test.name, got) + } + } +} + +func TestScopeSubScopeErrors(t *testing.T) { + var ( + root = NewScope() + sub = root.SubScope("x") + ) + // Error on the root, even after sub has been created should be propagated. + // Force an error by creating a Const which has a type that does not + // translate to the TensorFlow type system. + Const(root, int(1)) + if err := root.Err(); err == nil { + t.Fatal("Expected error") + } + if err := sub.Err(); err == nil { + t.Errorf("Root scope had error [%v], but sub-scope did not", root.Err()) + } +} + +func TestControlDependencies(t *testing.T) { + var ( + s = NewScope() + zero = Const(s.SubScope("zero"), int32(0)) + one = Const(s.SubScope("one"), int32(1)) + variable = VarHandleOp(s, tf.Int32, tf.ScalarShape()) + init = AssignVariableOp(s, variable, zero) + update = AssignAddVariableOp(s, variable, one) + readDeps = []*tf.Operation{update} + ) + // We intend for `read` to have a control dependency on `update`. + s = s.WithControlDependencies(readDeps...) + // Ensure that Scope.WithControlDependencies makes a copy of the underlying + // array, rather than just holding a slice reference to the same user-supplied + // underlying array. If the copy is correctly performed, overwriting + // readDeps[0] should have no effect on control dependencies for `read`. + readDeps[0] = init + read := ReadVariableOp(s, variable, tf.Int32) + + graph, err := s.Finalize() + if err != nil { + t.Fatal(err) + } + sess, err := tf.NewSession(graph, nil) + if err != nil { + t.Fatal(err) + } + if _, err = sess.Run(nil, nil, []*tf.Operation{init}); err != nil { + t.Fatal(err) + } + // Without the control dependency, the read operation may not see the + // update. + for i := int32(0); i < 10; i++ { + out, err := sess.Run(nil, []tf.Output{read}, nil) + if err != nil { + t.Fatal(err) + } + if got, want := out[0].Value().(int32), i+1; got != want { + t.Errorf("Got %d, want %d", got, want) + } + } +} + +func TestDevice(t *testing.T) { + s := NewScope() + matrix := Const(s, [][]float32{{3.0}}) + s = s.WithDevice("/device:GPU:0") + square := MatMul(s.SubScope("square"), matrix, matrix) + s = s.WithDevice("") + cube := MatMul(s.SubScope("cube"), square, matrix) + if got, want := square.Op.Device(), "/device:GPU:0"; got != want { + t.Errorf("Got %q, want %q", got, want) + } + if got, want := cube.Op.Device(), ""; got != want { + t.Errorf("Got %q, want %q", got, want) + } +} + +func TestScopeFinalize(t *testing.T) { + var ( + root = NewScope() + sub1 = root.SubScope("x") + sub2 = sub1.SubScope("y") + ) + if _, err := sub1.Finalize(); err != nil { + t.Fatal(err) + } + if err := root.Err(); err == nil { + t.Error("Root scope's Err() should be non-nil once Finalize has been called") + } + if err := sub2.Err(); err == nil { + t.Error("Sub scope's Err() should be non-nil once Finalize has been called") + } +} + +func TestMultipleGeneratedOps(t *testing.T) { + s := NewScope() + Placeholder(s.SubScope("x"), tf.Float) + Placeholder(s.SubScope("y"), tf.Float) + if _, err := s.Finalize(); err != nil { + t.Fatal(err) + } +} + +func TestScopeWithGraph(t *testing.T) { + s1 := NewScope() + Const(s1, "hello") + graph, err := s1.Finalize() + if err != nil { + t.Fatal(err) + } + + s2 := NewScopeWithGraph(graph) + Const(s2.SubScope("addition"), "world") + if err := s2.Err(); err != nil { + t.Fatal(err) + } +} + +func Example() { + // This example creates a Graph that multiplies a constant matrix with + // a matrix to be provided during graph execution (via + // tensorflow.Session). + s := NewScope() + input := Placeholder(s, tf.Float) // Matrix to be provided to Session.Run + output := MatMul(s, + Const(s, [][]float32{{10}, {20}}), // Constant 2x1 matrix + input, + MatMulTransposeB(true)) + if s.Err() != nil { + panic(s.Err()) + } + // Shape of the product: The number of rows is fixed by m1, but the + // number of columns will depend on m2, which is unknown. + fmt.Println(output.Shape()) + // Output: [2, ?] +} + +func ExampleScope_SubScope() { + var ( + s = NewScope() + c1 = Const(s.SubScope("x"), int64(1)) + c2 = Const(s.SubScope("x"), int64(1)) + ) + if s.Err() != nil { + panic(s.Err()) + } + fmt.Println(c1.Op.Name(), c2.Op.Name()) + // Output: x/Const x_1/Const +} diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go new file mode 100644 index 0000000..3675c26 --- /dev/null +++ b/tensorflow/go/op/wrappers.go @@ -0,0 +1,49701 @@ +// Copyright 2017 The TensorFlow Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// DO NOT EDIT +// This file was machine generated by github.com/tensorflow/tensorflow/tensorflow/go/genop/internal +// +// WARNING: This generation of wrapper function for TensorFlow ops is in an +// experimental state. The generated API can change without notice. + +package op + +import tf "github.com/tensorflow/tensorflow/tensorflow/go" + +// optionalAttr is an intentionally un-exported type to hide +// details of how optional attributes to operations are implemented. +type optionalAttr map[string]interface{} + +func makeOutputList(op *tf.Operation, start int, output string) ([]tf.Output, int, error) { + size, err := op.OutputListSize(output) + if err != nil { + return nil, start, err + } + list := make([]tf.Output, size) + for i := 0; i < size; i++ { + list[i] = op.Output(start + i) + } + return list, start + size, nil +} + +// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient. +type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value. +// +// value: The bitwidth of the quantization; between 2 and 8, inclusive. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value. +// +// value: Whether to quantize into 2^num_bits - 1 distinct values. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Compute gradients for a FakeQuantWithMinMaxVars operation. +// +// Arguments: +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation. +// min, max: Quantization interval, scalar floats. +// +// +// +// Returns: +// backprops_wrt_input: Backpropagated gradients w.r.t. inputs: +// `gradients * (inputs >= min && inputs <= max)`. +// backprop_wrt_min: Backpropagated gradients w.r.t. min parameter: +// `sum(gradients * (inputs < min))`. +// backprop_wrt_max: Backpropagated gradients w.r.t. max parameter: +// `sum(gradients * (inputs > max))`. +func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVarsGradient", + Input: []tf.Input{ + gradients, inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient. +type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr) + +// FakeQuantWithMinMaxArgsGradientMin sets the optional min attribute to value. +// If not specified, defaults to -6 +func FakeQuantWithMinMaxArgsGradientMin(value float32) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["min"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientMax sets the optional max attribute to value. +// If not specified, defaults to 6 +func FakeQuantWithMinMaxArgsGradientMax(value float32) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["max"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxArgsGradientNumBits(value int64) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxArgsGradientNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxArgsGradientNarrowRange(value bool) FakeQuantWithMinMaxArgsGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Compute gradients for a FakeQuantWithMinMaxArgs operation. +// +// Arguments: +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. +// +// Returns Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: +// `gradients * (inputs >= min && inputs <= max)`. +func FakeQuantWithMinMaxArgsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsGradientAttr) (backprops tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxArgsGradient", + Input: []tf.Input{ + gradients, inputs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Subtracts sparse `updates` from an existing tensor according to `indices`. +// +// This operation creates a new tensor by subtracting sparse `updates` from the +// passed in `tensor`. +// This operation is very similar to `tf.scatter_nd_sub`, except that the updates +// are subtracted from an existing tensor (as opposed to a variable). If the memory +// for the existing tensor cannot be re-used, a copy is made and updated. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of tensor_scatter_sub is to subtract individual elements +// from a tensor by index. For example, say we want to insert 4 scattered elements +// in a rank-1 tensor with 8 elements. +// +// In Python, this scatter subtract operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// tensor = tf.ones([8], dtype=tf.int32) +// updated = tf.tensor_scatter_nd_sub(tensor, indices, updates) +// print(updated) +// ``` +// +// The resulting tensor would look like this: +// +// [1, -10, 1, -9, -8, 1, 1, -11] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// In Python, this scatter add operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// tensor = tf.ones([4, 4, 4],dtype=tf.int32) +// updated = tf.tensor_scatter_nd_sub(tensor, indices, updates) +// print(updated) +// ``` +// +// The resulting tensor would look like this: +// +// [[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], +// [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// tensor: Tensor to copy/update. +// indices: Index tensor. +// updates: Updates to scatter into output. +// +// Returns A new tensor copied from tensor and updates subtracted according to the indices. +func TensorScatterSub(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorScatterSub", + Input: []tf.Input{ + tensor, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds sparse `updates` to an existing tensor according to `indices`. +// +// This operation creates a new tensor by adding sparse `updates` to the passed +// in `tensor`. +// This operation is very similar to `tf.scatter_nd_add`, except that the updates +// are added onto an existing tensor (as opposed to a variable). If the memory +// for the existing tensor cannot be re-used, a copy is made and updated. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `tensor.shape`. The last dimension of `indices` can be at most the rank of +// `tensor.shape`: +// +// indices.shape[-1] <= tensor.shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = tensor.shape.rank`) or slices +// (if `indices.shape[-1] < tensor.shape.rank`) along dimension +// `indices.shape[-1]` of `tensor.shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + tensor.shape[indices.shape[-1]:] +// +// The simplest form of tensor_scatter_add is to add individual elements to a +// tensor by index. For example, say we want to add 4 elements in a rank-1 +// tensor with 8 elements. +// +// In Python, this scatter add operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// tensor = tf.ones([8], dtype=tf.int32) +// updated = tf.tensor_scatter_nd_add(tensor, indices, updates) +// print(updated) +// ``` +// +// The resulting tensor would look like this: +// +// [1, 12, 1, 11, 10, 1, 1, 13] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// In Python, this scatter add operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// tensor = tf.ones([4, 4, 4],dtype=tf.int32) +// updated = tf.tensor_scatter_nd_add(tensor, indices, updates) +// print(updated) +// ``` +// +// The resulting tensor would look like this: +// +// [[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], +// [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], +// [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// tensor: Tensor to copy/update. +// indices: Index tensor. +// updates: Updates to scatter into output. +// +// Returns A new tensor copied from tensor and updates added according to the indices. +func TensorScatterAdd(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorScatterAdd", + Input: []tf.Input{ + tensor, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reshapes a quantized tensor as per the Reshape op. +// +// ``` +// +// Arguments: +// +// shape: Defines the shape of the output tensor. +// input_min: The minimum value of the input. +// input_max: The maximum value of the input. +// +// Returns: +// output +// output_min: This value is copied from input_min. +// output_max: This value is copied from input_max. +func QuantizedReshape(scope *Scope, tensor tf.Output, shape tf.Output, input_min tf.Output, input_max tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedReshape", + Input: []tf.Input{ + tensor, shape, input_min, input_max, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QuantizeAndDequantizeV2Attr is an optional argument to QuantizeAndDequantizeV2. +type QuantizeAndDequantizeV2Attr func(optionalAttr) + +// QuantizeAndDequantizeV2SignedInput sets the optional signed_input attribute to value. +// +// value: Whether the quantization is signed or unsigned. (actually this parameter should +// have been called `signed_output`) +// If not specified, defaults to true +func QuantizeAndDequantizeV2SignedInput(value bool) QuantizeAndDequantizeV2Attr { + return func(m optionalAttr) { + m["signed_input"] = value + } +} + +// QuantizeAndDequantizeV2NumBits sets the optional num_bits attribute to value. +// +// value: The bitwidth of the quantization. +// If not specified, defaults to 8 +func QuantizeAndDequantizeV2NumBits(value int64) QuantizeAndDequantizeV2Attr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// QuantizeAndDequantizeV2RangeGiven sets the optional range_given attribute to value. +// +// value: Whether the range is given or should be determined from the `input` tensor. +// If not specified, defaults to false +func QuantizeAndDequantizeV2RangeGiven(value bool) QuantizeAndDequantizeV2Attr { + return func(m optionalAttr) { + m["range_given"] = value + } +} + +// QuantizeAndDequantizeV2RoundMode sets the optional round_mode attribute to value. +// +// value: The 'round_mode' attribute controls which rounding tie-breaking algorithm is +// used when rounding float values to their quantized equivalents. The following +// rounding modes are currently supported: +// +// * HALF_TO_EVEN: this is the default round_mode. +// * HALF_UP: round towards positive. In this mode 7.5 rounds up to 8 and -7.5 +// rounds up to -7. +// +// If not specified, defaults to "HALF_TO_EVEN" +func QuantizeAndDequantizeV2RoundMode(value string) QuantizeAndDequantizeV2Attr { + return func(m optionalAttr) { + m["round_mode"] = value + } +} + +// QuantizeAndDequantizeV2NarrowRange sets the optional narrow_range attribute to value. +// +// value: If True, then the absolute value of the quantized minimum value is the same as +// the quantized maximum value, instead of 1 greater. +// i.e. for 8 bit quantization, the minimum value is -127 instead of -128. +// If not specified, defaults to false +func QuantizeAndDequantizeV2NarrowRange(value bool) QuantizeAndDequantizeV2Attr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// QuantizeAndDequantizeV2Axis sets the optional axis attribute to value. +// +// value: If specified, this axis is treated as a channel or slice axis, and a separate +// quantization range is used for each channel or slice along this axis. +// If not specified, defaults to -1 +func QuantizeAndDequantizeV2Axis(value int64) QuantizeAndDequantizeV2Attr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Quantizes then dequantizes a tensor. +// +// This op simulates the precision loss from the quantized forward pass by: +// +// 1. Quantizing the tensor to fixed point numbers, which should match the target +// quantization method when it is used in inference. +// 2. Dequantizing it back to floating point numbers for the following ops, most +// likely matmul. +// +// There are different ways to quantize. This version uses only scaling, so 0.0 +// maps to 0. +// +// From the specified 'num_bits' in the quantized output type, it determines +// minimum and maximum representable quantized values. +// +// e.g. +// +// * [-128, 127] for signed, num_bits = 8, or +// * [0, 255] for unsigned, num_bits = 8. +// +// If range_given == False, the initial input_min, input_max will be determined +// automatically as the minimum and maximum values in the input tensor, otherwise +// the specified values of input_min, input_max are used. +// +// Note: If the input_min, input_max are specified, they do not need to equal the +// actual minimum and maximum values in the tensor. e.g. in some cases it may be +// beneficial to specify these values such that the low probability extremes of the +// input distribution are clipped. +// +// This op determines the maximum scale_factor that would map the initial +// [input_min, input_max] range to a range that lies within the representable +// quantized range. +// +// It determines the scale from one of input_min and input_max, then updates the +// other one to maximize the representable range. +// +// e.g. +// +// * if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0, +// 5.0]: it would use a scale_factor of -128 / -10.0 = 12.8 In this case, it +// would update input_max to be 127 / 12.8 = 9.921875 +// * if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0, +// 10.0]: it would use a scale_factor of 127 / 10.0 = 12.7 In this case, it +// would update input_min to be 128.0 / 12.7 = -10.07874 +// * if the output is unsigned, input_min is forced to be 0, and only the +// specified input_max is used. +// +// After determining the scale_factor and updating the input range, it applies the +// following to each value in the 'input' tensor. +// +// output = round(clamp(value, input_min, input_max) * scale_factor) / scale_factor. +// +// The above round function rounds the value based on the given round_mode. +// +// +// Arguments: +// input: Tensor to quantize and then dequantize. +// input_min: If `range_given == True`, this specifies the minimum input value that needs to +// be represented, otherwise it is determined from the min value of the `input` +// tensor. +// input_max: If `range_given == True`, this specifies the maximum input value that needs to +// be represented, otherwise it is determined from the max value of the `input` +// tensor. +func QuantizeAndDequantizeV2(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, optional ...QuantizeAndDequantizeV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizeAndDequantizeV2", + Input: []tf.Input{ + input, input_min, input_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizeAndDequantizeAttr is an optional argument to QuantizeAndDequantize. +type QuantizeAndDequantizeAttr func(optionalAttr) + +// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value. +// If not specified, defaults to true +func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["signed_input"] = value + } +} + +// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value. +// If not specified, defaults to false +func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["range_given"] = value + } +} + +// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value. +// If not specified, defaults to 0 +func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["input_min"] = value + } +} + +// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value. +// If not specified, defaults to 0 +func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr { + return func(m optionalAttr) { + m["input_max"] = value + } +} + +// Use QuantizeAndDequantizeV2 instead. +// +// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2 +func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizeAndDequantize", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OneHotAttr is an optional argument to OneHot. +type OneHotAttr func(optionalAttr) + +// OneHotAxis sets the optional axis attribute to value. +// +// value: The axis to fill (default: -1, a new inner-most axis). +// If not specified, defaults to -1 +func OneHotAxis(value int64) OneHotAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Returns a one-hot tensor. +// +// The locations represented by indices in `indices` take value `on_value`, +// while all other locations take value `off_value`. +// +// If the input `indices` is rank `N`, the output will have rank `N+1`, +// The new axis is created at dimension `axis` (default: the new axis is +// appended at the end). +// +// If `indices` is a scalar the output shape will be a vector of length `depth`. +// +// If `indices` is a vector of length `features`, the output shape will be: +// ``` +// features x depth if axis == -1 +// depth x features if axis == 0 +// ``` +// +// If `indices` is a matrix (batch) with shape `[batch, features]`, +// the output shape will be: +// ``` +// batch x features x depth if axis == -1 +// batch x depth x features if axis == 1 +// depth x batch x features if axis == 0 +// ``` +// +// +// Examples +// ========= +// +// Suppose that +// ``` +// indices = [0, 2, -1, 1] +// depth = 3 +// on_value = 5.0 +// off_value = 0.0 +// axis = -1 +// ``` +// +// Then output is `[4 x 3]`: +// ``` +// output = +// [5.0 0.0 0.0] // one_hot(0) +// [0.0 0.0 5.0] // one_hot(2) +// [0.0 0.0 0.0] // one_hot(-1) +// [0.0 5.0 0.0] // one_hot(1) +// ``` +// +// Suppose that +// ``` +// indices = [0, 2, -1, 1] +// depth = 3 +// on_value = 0.0 +// off_value = 3.0 +// axis = 0 +// ``` +// +// Then output is `[3 x 4]`: +// ``` +// output = +// [0.0 3.0 3.0 3.0] +// [3.0 3.0 3.0 0.0] +// [3.0 3.0 3.0 3.0] +// [3.0 0.0 3.0 3.0] +// // ^ one_hot(0) +// // ^ one_hot(2) +// // ^ one_hot(-1) +// // ^ one_hot(1) +// ``` +// +// Suppose that +// ``` +// indices = [[0, 2], [1, -1]] +// depth = 3 +// on_value = 1.0 +// off_value = 0.0 +// axis = -1 +// ``` +// +// Then output is `[2 x 2 x 3]`: +// ``` +// output = +// [ +// [1.0, 0.0, 0.0] // one_hot(0) +// [0.0, 0.0, 1.0] // one_hot(2) +// ][ +// [0.0, 1.0, 0.0] // one_hot(1) +// [0.0, 0.0, 0.0] // one_hot(-1) +// ] +// ``` +// +// Arguments: +// indices: A tensor of indices. +// depth: A scalar defining the depth of the one hot dimension. +// on_value: A scalar defining the value to fill in output when `indices[j] = i`. +// off_value: A scalar defining the value to fill in output when `indices[j] != i`. +// +// Returns The one-hot tensor. +func OneHot(scope *Scope, indices tf.Output, depth tf.Output, on_value tf.Output, off_value tf.Output, optional ...OneHotAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OneHot", + Input: []tf.Input{ + indices, depth, on_value, off_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Extract `patches` from `input` and put them in the "depth" output dimension. 3D extension of `extract_image_patches`. +// +// Arguments: +// input: 5-D Tensor with shape `[batch, in_planes, in_rows, in_cols, depth]`. +// ksizes: The size of the sliding window for each dimension of `input`. +// strides: 1-D of length 5. How far the centers of two consecutive patches are in +// `input`. Must be: `[1, stride_planes, stride_rows, stride_cols, 1]`. +// padding: The type of padding algorithm to use. +// +// We specify the size-related attributes as: +// +// ```python +// ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1] +// strides = [1, stride_planes, strides_rows, strides_cols, 1] +// ``` +// +// Returns 5-D Tensor with shape `[batch, out_planes, out_rows, out_cols, +// ksize_planes * ksize_rows * ksize_cols * depth]` containing patches +// with size `ksize_planes x ksize_rows x ksize_cols x depth` vectorized +// in the "depth" dimension. Note `out_planes`, `out_rows` and `out_cols` +// are the dimensions of the output patches. +func ExtractVolumePatches(scope *Scope, input tf.Output, ksizes []int64, strides []int64, padding string) (patches tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "ExtractVolumePatches", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DepthToSpaceAttr is an optional argument to DepthToSpace. +type DepthToSpaceAttr func(optionalAttr) + +// DepthToSpaceDataFormat sets the optional data_format attribute to value. +// If not specified, defaults to "NHWC" +func DepthToSpaceDataFormat(value string) DepthToSpaceAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthToSpace for tensors of type T. +// +// Rearranges data from depth into blocks of spatial data. +// This is the reverse transformation of SpaceToDepth. More specifically, +// this op outputs a copy of the input tensor where values from the `depth` +// dimension are moved in spatial blocks to the `height` and `width` dimensions. +// The attr `block_size` indicates the input block size and how the data is moved. +// +// * Chunks of data of size `block_size * block_size` from depth are rearranged +// into non-overlapping blocks of size `block_size x block_size` +// * The width the output tensor is `input_depth * block_size`, whereas the +// height is `input_height * block_size`. +// * The Y, X coordinates within each block of the output image are determined +// by the high order component of the input channel index. +// * The depth of the input tensor must be divisible by +// `block_size * block_size`. +// +// The `data_format` attr specifies the layout of the input and output tensors +// with the following options: +// "NHWC": `[ batch, height, width, channels ]` +// "NCHW": `[ batch, channels, height, width ]` +// "NCHW_VECT_C": +// `qint8 [ batch, channels / 4, height, width, 4 ]` +// +// It is useful to consider the operation as transforming a 6-D Tensor. +// e.g. for data_format = NHWC, +// Each element in the input tensor can be specified via 6 coordinates, +// ordered by decreasing memory layout significance as: +// n,iY,iX,bY,bX,oC (where n=batch index, iX, iY means X or Y coordinates +// within the input image, bX, bY means coordinates +// within the output block, oC means output channels). +// The output would be the input transposed to the following layout: +// n,iY,bY,iX,bX,oC +// +// This operation is useful for resizing the activations between convolutions +// (but keeping all data), e.g. instead of pooling. It is also useful for training +// purely convolutional models. +// +// For example, given an input of shape `[1, 1, 1, 4]`, data_format = "NHWC" and +// block_size = 2: +// +// ``` +// x = [[[[1, 2, 3, 4]]]] +// +// ``` +// +// This operation will output a tensor of shape `[1, 2, 2, 1]`: +// +// ``` +// [[[[1], [2]], +// [[3], [4]]]] +// ``` +// +// Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`, +// the corresponding output will have 2x2 elements and will have a depth of +// 1 channel (1 = `4 / (block_size * block_size)`). +// The output element shape is `[2, 2, 1]`. +// +// For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g. +// +// ``` +// x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] +// ``` +// +// This operation, for block size of 2, will return the following tensor of shape +// `[1, 2, 2, 3]` +// +// ``` +// [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// +// ``` +// +// Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2: +// +// ``` +// x = [[[[1, 2, 3, 4], +// [5, 6, 7, 8]], +// [[9, 10, 11, 12], +// [13, 14, 15, 16]]]] +// ``` +// +// the operator will return the following tensor of shape `[1 4 4 1]`: +// +// ``` +// x = [[[ [1], [2], [5], [6]], +// [ [3], [4], [7], [8]], +// [ [9], [10], [13], [14]], +// [ [11], [12], [15], [16]]]] +// +// ``` +// +// Arguments: +// +// block_size: The size of the spatial block, same as in Space2Depth. +func DepthToSpace(scope *Scope, input tf.Output, block_size int64, optional ...DepthToSpaceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"block_size": block_size} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthToSpace", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SpaceToDepthAttr is an optional argument to SpaceToDepth. +type SpaceToDepthAttr func(optionalAttr) + +// SpaceToDepthDataFormat sets the optional data_format attribute to value. +// If not specified, defaults to "NHWC" +func SpaceToDepthDataFormat(value string) SpaceToDepthAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// SpaceToDepth for tensors of type T. +// +// Rearranges blocks of spatial data, into depth. More specifically, +// this op outputs a copy of the input tensor where values from the `height` +// and `width` dimensions are moved to the `depth` dimension. +// The attr `block_size` indicates the input block size. +// +// * Non-overlapping blocks of size `block_size x block size` are rearranged +// into depth at each location. +// * The depth of the output tensor is `block_size * block_size * input_depth`. +// * The Y, X coordinates within each block of the input become the high order +// component of the output channel index. +// * The input tensor's height and width must be divisible by block_size. +// +// The `data_format` attr specifies the layout of the input and output tensors +// with the following options: +// "NHWC": `[ batch, height, width, channels ]` +// "NCHW": `[ batch, channels, height, width ]` +// "NCHW_VECT_C": +// `qint8 [ batch, channels / 4, height, width, 4 ]` +// +// It is useful to consider the operation as transforming a 6-D Tensor. +// e.g. for data_format = NHWC, +// Each element in the input tensor can be specified via 6 coordinates, +// ordered by decreasing memory layout significance as: +// n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates +// within the output image, bX, bY means coordinates +// within the input block, iC means input channels). +// The output would be a transpose to the following layout: +// n,oY,oX,bY,bX,iC +// +// This operation is useful for resizing the activations between convolutions +// (but keeping all data), e.g. instead of pooling. It is also useful for training +// purely convolutional models. +// +// For example, given an input of shape `[1, 2, 2, 1]`, data_format = "NHWC" and +// block_size = 2: +// +// ``` +// x = [[[[1], [2]], +// [[3], [4]]]] +// ``` +// +// This operation will output a tensor of shape `[1, 1, 1, 4]`: +// +// ``` +// [[[[1, 2, 3, 4]]]] +// ``` +// +// Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`, +// the corresponding output will have a single element (i.e. width and height are +// both 1) and will have a depth of 4 channels (1 * block_size * block_size). +// The output element shape is `[1, 1, 4]`. +// +// For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g. +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// This operation, for block_size of 2, will return the following tensor of shape +// `[1, 1, 1, 12]` +// +// ``` +// [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] +// ``` +// +// Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2: +// +// ``` +// x = [[[[1], [2], [5], [6]], +// [[3], [4], [7], [8]], +// [[9], [10], [13], [14]], +// [[11], [12], [15], [16]]]] +// ``` +// +// the operator will return the following tensor of shape `[1 2 2 4]`: +// +// ``` +// x = [[[[1, 2, 3, 4], +// [5, 6, 7, 8]], +// [[9, 10, 11, 12], +// [13, 14, 15, 16]]]] +// ``` +// +// Arguments: +// +// block_size: The size of the spatial block. +func SpaceToDepth(scope *Scope, input tf.Output, block_size int64, optional ...SpaceToDepthAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"block_size": block_size} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SpaceToDepth", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BatchToSpace for 4-D tensors of type T. +// +// This is a legacy version of the more general BatchToSpaceND. +// +// Rearranges (permutes) data from batch into blocks of spatial data, followed by +// cropping. This is the reverse transformation of SpaceToBatch. More specifically, +// this op outputs a copy of the input tensor where values from the `batch` +// dimension are moved in spatial blocks to the `height` and `width` dimensions, +// followed by cropping along the `height` and `width` dimensions. +// +// Arguments: +// input: 4-D tensor with shape +// `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size, +// depth]`. Note that the batch size of the input tensor must be divisible by +// `block_size * block_size`. +// crops: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies +// how many elements to crop from the intermediate result across the spatial +// dimensions as follows: +// +// crops = [[crop_top, crop_bottom], [crop_left, crop_right]] +// +// +// Returns 4-D with shape `[batch, height, width, depth]`, where: +// +// height = height_pad - crop_top - crop_bottom +// width = width_pad - crop_left - crop_right +// +// The attr `block_size` must be greater than one. It indicates the block size. +// +// Some examples: +// +// (1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2: +// +// ``` +// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// ``` +// +// The output tensor has shape `[1, 2, 2, 1]` and value: +// +// ``` +// x = [[[[1], [2]], [[3], [4]]]] +// ``` +// +// (2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2: +// +// ``` +// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] +// ``` +// +// The output tensor has shape `[1, 2, 2, 3]` and value: +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// (3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [3]], [[9], [11]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +// +// The output tensor has shape `[1, 4, 4, 1]` and value: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]], +// [[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// (4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], +// [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] +// ``` +// +// The output tensor has shape `[2, 2, 4, 1]` and value: +// +// ``` +// x = [[[[1], [3]], [[5], [7]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +func BatchToSpace(scope *Scope, input tf.Output, crops tf.Output, block_size int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"block_size": block_size} + opspec := tf.OpSpec{ + Type: "BatchToSpace", + Input: []tf.Input{ + input, crops, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SpaceToBatch for 4-D tensors of type T. +// +// This is a legacy version of the more general SpaceToBatchND. +// +// Zero-pads and then rearranges (permutes) blocks of spatial data into batch. +// More specifically, this op outputs a copy of the input tensor where values from +// the `height` and `width` dimensions are moved to the `batch` dimension. After +// the zero-padding, both `height` and `width` of the input must be divisible by the +// block size. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, depth]`. +// paddings: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies +// the padding of the input with zeros across the spatial dimensions as follows: +// +// paddings = [[pad_top, pad_bottom], [pad_left, pad_right]] +// +// The effective spatial dimensions of the zero-padded input tensor will be: +// +// height_pad = pad_top + height + pad_bottom +// width_pad = pad_left + width + pad_right +// +// The attr `block_size` must be greater than one. It indicates the block size. +// +// * Non-overlapping blocks of size `block_size x block size` in the height and +// width dimensions are rearranged into the batch dimension at each location. +// * The batch of the output tensor is `batch * block_size * block_size`. +// * Both height_pad and width_pad must be divisible by block_size. +// +// The shape of the output will be: +// +// [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, +// depth] +// +// Some examples: +// +// (1) For the following input of shape `[1, 2, 2, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [2]], [[3], [4]]]] +// ``` +// +// The output tensor has shape `[4, 1, 1, 1]` and value: +// +// ``` +// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// ``` +// +// (2) For the following input of shape `[1, 2, 2, 3]` and block_size of 2: +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// The output tensor has shape `[4, 1, 1, 3]` and value: +// +// ``` +// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] +// ``` +// +// (3) For the following input of shape `[1, 4, 4, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]], +// [[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// The output tensor has shape `[4, 2, 2, 1]` and value: +// +// ``` +// x = [[[[1], [3]], [[9], [11]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +// +// (4) For the following input of shape `[2, 2, 4, 1]` and block_size of 2: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]]], +// [[[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// The output tensor has shape `[8, 1, 2, 1]` and value: +// +// ``` +// x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], +// [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] +// ``` +// +// Among others, this operation is useful for reducing atrous convolution into +// regular convolution. +// +func SpaceToBatch(scope *Scope, input tf.Output, paddings tf.Output, block_size int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"block_size": block_size} + opspec := tf.OpSpec{ + Type: "SpaceToBatch", + Input: []tf.Input{ + input, paddings, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SqueezeAttr is an optional argument to Squeeze. +type SqueezeAttr func(optionalAttr) + +// SqueezeAxis sets the optional axis attribute to value. +// +// value: If specified, only squeezes the dimensions listed. The dimension +// index starts at 0. It is an error to squeeze a dimension that is not 1. Must +// be in the range `[-rank(input), rank(input))`. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func SqueezeAxis(value []int64) SqueezeAttr { + return func(m optionalAttr) { + m["squeeze_dims"] = value + } +} + +// Removes dimensions of size 1 from the shape of a tensor. +// +// Given a tensor `input`, this operation returns a tensor of the same type with +// all dimensions of size 1 removed. If you don't want to remove all size 1 +// dimensions, you can remove specific size 1 dimensions by specifying +// `axis`. +// +// For example: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t)) ==> [2, 3] +// ``` +// +// Or, to remove specific size 1 dimensions: +// +// ``` +// # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] +// shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] +// ``` +// +// Arguments: +// input: The `input` to squeeze. +// +// Returns Contains the same data as `input`, but has one or more dimensions of +// size 1 removed. +func Squeeze(scope *Scope, input tf.Output, optional ...SqueezeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Squeeze", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A placeholder op that passes through `input` when its output is not fed. +// +// Arguments: +// input: The default value to produce when `output` is not fed. +// shape: The (possibly partial) shape of the tensor. +// +// Returns A placeholder tensor that defaults to `input` if it is not fed. +func PlaceholderWithDefault(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "PlaceholderWithDefault", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PlaceholderAttr is an optional argument to Placeholder. +type PlaceholderAttr func(optionalAttr) + +// PlaceholderShape sets the optional shape attribute to value. +// +// value: (Optional) The shape of the tensor. If the shape has 0 dimensions, the +// shape is unconstrained. +// If not specified, defaults to +func PlaceholderShape(value tf.Shape) PlaceholderAttr { + return func(m optionalAttr) { + m["shape"] = value + } +} + +// A placeholder op for a value that will be fed into the computation. +// +// N.B. This operation will fail with an error if it is executed. It is +// intended as a way to represent a value that will always be fed, and to +// provide attrs that enable the fed value to be checked at runtime. +// +// Arguments: +// dtype: The type of elements in the tensor. +// +// Returns A placeholder tensor that must be replaced using the feed mechanism. +func Placeholder(scope *Scope, dtype tf.DataType, optional ...PlaceholderAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Placeholder", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Return the reduction indices for computing gradients of s0 op s1 with broadcast. +// +// This is typically used by gradient computations for a broadcasting operation. +func BroadcastGradientArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output, r1 tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastGradientArgs", + Input: []tf.Input{ + s0, s1, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Return the shape of s0 op s1 with broadcast. +// +// Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the +// broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. +func BroadcastArgs(scope *Scope, s0 tf.Output, s1 tf.Output) (r0 tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastArgs", + Input: []tf.Input{ + s0, s1, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorStridedSliceUpdateAttr is an optional argument to TensorStridedSliceUpdate. +type TensorStridedSliceUpdateAttr func(optionalAttr) + +// TensorStridedSliceUpdateBeginMask sets the optional begin_mask attribute to value. +// If not specified, defaults to 0 +func TensorStridedSliceUpdateBeginMask(value int64) TensorStridedSliceUpdateAttr { + return func(m optionalAttr) { + m["begin_mask"] = value + } +} + +// TensorStridedSliceUpdateEndMask sets the optional end_mask attribute to value. +// If not specified, defaults to 0 +func TensorStridedSliceUpdateEndMask(value int64) TensorStridedSliceUpdateAttr { + return func(m optionalAttr) { + m["end_mask"] = value + } +} + +// TensorStridedSliceUpdateEllipsisMask sets the optional ellipsis_mask attribute to value. +// If not specified, defaults to 0 +func TensorStridedSliceUpdateEllipsisMask(value int64) TensorStridedSliceUpdateAttr { + return func(m optionalAttr) { + m["ellipsis_mask"] = value + } +} + +// TensorStridedSliceUpdateNewAxisMask sets the optional new_axis_mask attribute to value. +// If not specified, defaults to 0 +func TensorStridedSliceUpdateNewAxisMask(value int64) TensorStridedSliceUpdateAttr { + return func(m optionalAttr) { + m["new_axis_mask"] = value + } +} + +// TensorStridedSliceUpdateShrinkAxisMask sets the optional shrink_axis_mask attribute to value. +// If not specified, defaults to 0 +func TensorStridedSliceUpdateShrinkAxisMask(value int64) TensorStridedSliceUpdateAttr { + return func(m optionalAttr) { + m["shrink_axis_mask"] = value + } +} + +// Assign `value` to the sliced l-value reference of `input`. +// +// The values of `value` are assigned to the positions in the tensor `input` that +// are selected by the slice parameters. The slice parameters `begin` `end` +// `strides` etc. work exactly as in `StridedSlice`. +// +// NOTE this op currently does not support broadcasting and so `value`'s shape +// must be exactly the shape produced by the slice of `input`. +func TensorStridedSliceUpdate(scope *Scope, input tf.Output, begin tf.Output, end tf.Output, strides tf.Output, value tf.Output, optional ...TensorStridedSliceUpdateAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorStridedSliceUpdate", + Input: []tf.Input{ + input, begin, end, strides, value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Ensures that the tensor's shape matches the expected shape. +// +// Raises an error if the input tensor's shape does not match the specified shape. +// Returns the input tensor otherwise. +// +// Arguments: +// input: A tensor, whose shape is to be validated. +// shape: The expected (possibly partially specified) shape of the input tensor. +// +// Returns A tensor with the same shape and contents as the input tensor or value. +func EnsureShape(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "EnsureShape", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ShapeAttr is an optional argument to Shape. +type ShapeAttr func(optionalAttr) + +// ShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func ShapeOutType(value tf.DataType) ShapeAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns the shape of a tensor. +// +// This operation returns a 1-D integer tensor representing the shape of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func Shape(scope *Scope, input tf.Output, optional ...ShapeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Shape", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UniqueWithCountsV2Attr is an optional argument to UniqueWithCountsV2. +type UniqueWithCountsV2Attr func(optionalAttr) + +// UniqueWithCountsV2OutIdx sets the optional out_idx attribute to value. +// If not specified, defaults to DT_INT32 +func UniqueWithCountsV2OutIdx(value tf.DataType) UniqueWithCountsV2Attr { + return func(m optionalAttr) { + m["out_idx"] = value + } +} + +// Finds unique elements along an axis of a tensor. +// +// This operation either returns a tensor `y` containing unique elements +// along the `axis` of a tensor. The returned unique elements is sorted +// in the same order as they occur along `axis` in `x`. +// This operation also returns a tensor `idx` and a tensor `count` +// that are the same size as the number of the elements in `x` along the +// `axis` dimension. The `idx` contains the index in the unique output `y` +// and the `count` contains the count in the unique output `y`. +// In other words, for an `1-D` tensor `x` with `axis = None: +// +// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` +// +// For example: +// +// ``` +// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] +// y, idx, count = unique_with_counts(x) +// y ==> [1, 2, 4, 7, 8] +// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] +// count ==> [2, 1, 3, 1, 2] +// ``` +// +// For an `2-D` tensor `x` with `axis = 0`: +// +// ``` +// # tensor 'x' is [[1, 0, 0], +// # [1, 0, 0], +// # [2, 0, 0]] +// y, idx, count = unique_with_counts(x, axis=0) +// y ==> [[1, 0, 0], +// [2, 0, 0]] +// idx ==> [0, 0, 1] +// count ==> [2, 1] +// ``` +// +// For an `2-D` tensor `x` with `axis = 1`: +// +// ``` +// # tensor 'x' is [[1, 0, 0], +// # [1, 0, 0], +// # [2, 0, 0]] +// y, idx, count = unique_with_counts(x, axis=1) +// y ==> [[1, 0], +// [1, 0], +// [2, 0]] +// idx ==> [0, 1, 1] +// count ==> [1, 2] +// ``` +// +// Arguments: +// x: A `Tensor`. +// axis: A `Tensor` of type `int32` (default: None). The axis of the Tensor to +// find the unique elements. +// +// Returns: +// y: A `Tensor`. Unique elements along the `axis` of `Tensor` x. +// idx: A 1-D Tensor. Has the same type as x that contains the index of each +// value of x in the output y. +// count: A 1-D Tensor. The count of each value of x in the output y. +func UniqueWithCountsV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueWithCountsV2Attr) (y tf.Output, idx tf.Output, count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UniqueWithCountsV2", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Shuffle dimensions of x according to a permutation and conjugate the result. +// +// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: +// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` +// `y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])` +func ConjugateTranspose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConjugateTranspose", + Input: []tf.Input{ + x, perm, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the inverse permutation of a tensor. +// +// This operation computes the inverse of an index permutation. It takes a 1-D +// integer tensor `x`, which represents the indices of a zero-based array, and +// swaps each value with its index position. In other words, for an output tensor +// `y` and an input tensor `x`, this operation computes the following: +// +// `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]` +// +// The values must include 0. There can be no duplicate values or negative values. +// +// For example: +// +// ``` +// # tensor `x` is [3, 4, 0, 2, 1] +// invert_permutation(x) ==> [2, 4, 3, 0, 1] +// ``` +// +// Arguments: +// x: 1-D. +// +// Returns 1-D. +func InvertPermutation(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InvertPermutation", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PreventGradientAttr is an optional argument to PreventGradient. +type PreventGradientAttr func(optionalAttr) + +// PreventGradientMessage sets the optional message attribute to value. +// +// value: Will be printed in the error when anyone tries to differentiate +// this operation. +// If not specified, defaults to "" +func PreventGradientMessage(value string) PreventGradientAttr { + return func(m optionalAttr) { + m["message"] = value + } +} + +// An identity op that triggers an error if a gradient is requested. +// +// When executed in a graph, this op outputs its input tensor as-is. +// +// When building ops to compute gradients, the TensorFlow gradient system +// will return an error when trying to lookup the gradient of this op, +// because no gradient must ever be registered for this function. This +// op exists to prevent subtle bugs from silently returning unimplemented +// gradients in some corner cases. +// +// Arguments: +// input: any tensor. +// +// Returns the same input tensor. +func PreventGradient(scope *Scope, input tf.Output, optional ...PreventGradientAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PreventGradient", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Stops gradient computation. +// +// When executed in a graph, this op outputs its input tensor as-is. +// +// When building ops to compute gradients, this op prevents the contribution of +// its inputs to be taken into account. Normally, the gradient generator adds ops +// to a graph to compute the derivatives of a specified 'loss' by recursively +// finding out inputs that contributed to its computation. If you insert this op +// in the graph it inputs are masked from the gradient generator. They are not +// taken into account for computing gradients. +// +// This is useful any time you want to compute a value with TensorFlow but need +// to pretend that the value was a constant. Some examples include: +// +// * The *EM* algorithm where the *M-step* should not involve backpropagation +// through the output of the *E-step*. +// * Contrastive divergence training of Boltzmann machines where, when +// differentiating the energy function, the training must not backpropagate +// through the graph that generated the samples from the model. +// * Adversarial training, where no backprop should happen through the adversarial +// example generation process. +func StopGradient(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StopGradient", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Identity op for gradient debugging. +// +// This op is hidden from public in Python. It is used by TensorFlow Debugger to +// register gradient tensors for gradient debugging. +// This op operates on non-reference-type tensors. +func DebugGradientIdentity(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DebugGradientIdentity", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gather slices from `params` into a Tensor with shape specified by `indices`. +// +// `indices` is a K-dimensional integer tensor, best thought of as a +// (K-1)-dimensional tensor of indices into `params`, where each element defines a +// slice of `params`: +// +// output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]] +// +// Whereas in `tf.gather` `indices` defines slices into the `axis` +// dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the +// first `N` dimensions of `params`, where `N = indices.shape[-1]`. +// +// The last dimension of `indices` can be at most the rank of +// `params`: +// +// indices.shape[-1] <= params.rank +// +// The last dimension of `indices` corresponds to elements +// (if `indices.shape[-1] == params.rank`) or slices +// (if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]` +// of `params`. The output tensor has shape +// +// indices.shape[:-1] + params.shape[indices.shape[-1]:] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, a 0 is stored in the +// corresponding output value. +// +// Some examples below. +// +// Simple indexing into a matrix: +// +// ```python +// indices = [[0, 0], [1, 1]] +// params = [['a', 'b'], ['c', 'd']] +// output = ['a', 'd'] +// ``` +// +// Slice indexing into a matrix: +// +// ```python +// indices = [[1], [0]] +// params = [['a', 'b'], ['c', 'd']] +// output = [['c', 'd'], ['a', 'b']] +// ``` +// +// Indexing into a 3-tensor: +// +// ```python +// indices = [[1]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [[['a1', 'b1'], ['c1', 'd1']]] +// +// +// indices = [[0, 1], [1, 0]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [['c0', 'd0'], ['a1', 'b1']] +// +// +// indices = [[0, 0, 1], [1, 0, 1]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = ['b0', 'b1'] +// ``` +// +// Batched indexing into a matrix: +// +// ```python +// indices = [[[0, 0]], [[0, 1]]] +// params = [['a', 'b'], ['c', 'd']] +// output = [['a'], ['b']] +// ``` +// +// Batched slice indexing into a matrix: +// +// ```python +// indices = [[[1]], [[0]]] +// params = [['a', 'b'], ['c', 'd']] +// output = [[['c', 'd']], [['a', 'b']]] +// ``` +// +// Batched indexing into a 3-tensor: +// +// ```python +// indices = [[[1]], [[0]]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [[[['a1', 'b1'], ['c1', 'd1']]], +// [[['a0', 'b0'], ['c0', 'd0']]]] +// +// indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [[['c0', 'd0'], ['a1', 'b1']], +// [['a0', 'b0'], ['c1', 'd1']]] +// +// +// indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]] +// params = [[['a0', 'b0'], ['c0', 'd0']], +// [['a1', 'b1'], ['c1', 'd1']]] +// output = [['b0', 'b1'], ['d0', 'c1']] +// ``` +// +// See also `tf.gather` and `tf.batch_gather`. +// +// Arguments: +// params: The tensor from which to gather values. +// indices: Index tensor. +// +// Returns Values from `params` gathered from indices given by `indices`, with +// shape `indices.shape[:-1] + params.shape[indices.shape[-1]:]`. +func GatherNd(scope *Scope, params tf.Output, indices tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GatherNd", + Input: []tf.Input{ + params, indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// GatherV2Attr is an optional argument to GatherV2. +type GatherV2Attr func(optionalAttr) + +// GatherV2BatchDims sets the optional batch_dims attribute to value. +// If not specified, defaults to 0 +func GatherV2BatchDims(value int64) GatherV2Attr { + return func(m optionalAttr) { + m["batch_dims"] = value + } +} + +// Gather slices from `params` axis `axis` according to `indices`. +// +// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). +// Produces an output tensor with shape `params.shape[:axis] + +// indices.shape[batch_dims:] + params.shape[axis + 1:]` where: +// +// ```python +// # Scalar indices (output is rank(params) - 1). +// output[a_0, ..., a_n, b_0, ..., b_n] = +// params[a_0, ..., a_n, indices, b_0, ..., b_n] +// +// # Vector indices (output is rank(params)). +// output[a_0, ..., a_n, i, b_0, ..., b_n] = +// params[a_0, ..., a_n, indices[i], b_0, ..., b_n] +// +// # Higher rank indices (output is rank(params) + rank(indices) - 1). +// output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] = +// params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n] +// ``` +// +//
+// +//
+// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, a 0 is stored in the +// corresponding output value. +// +// See also `tf.batch_gather` and `tf.gather_nd`. +// +// Arguments: +// params: The tensor from which to gather values. Must be at least rank +// `axis + 1`. +// indices: Index tensor. Must be in range `[0, params.shape[axis])`. +// axis: The axis in `params` to gather `indices` from. Defaults to the first +// dimension. Supports negative indexes. +// +// Returns Values from `params` gathered from indices given by `indices`, with +// shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`. +func GatherV2(scope *Scope, params tf.Output, indices tf.Output, axis tf.Output, optional ...GatherV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "GatherV2", + Input: []tf.Input{ + params, indices, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reverses specific dimensions of a tensor. +// +// NOTE `tf.reverse` has now changed behavior in preparation for 1.0. +// `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0. +// +// Given a `tensor`, and a `int32` tensor `axis` representing the set of +// dimensions of `tensor` to reverse. This operation reverses each dimension +// `i` for which there exists `j` s.t. `axis[j] == i`. +// +// `tensor` can have up to 8 dimensions. The number of dimensions specified +// in `axis` may be 0 or more entries. If an index is specified more than +// once, a InvalidArgument error is raised. +// +// For example: +// +// ``` +// # tensor 't' is [[[[ 0, 1, 2, 3], +// # [ 4, 5, 6, 7], +// # [ 8, 9, 10, 11]], +// # [[12, 13, 14, 15], +// # [16, 17, 18, 19], +// # [20, 21, 22, 23]]]] +// # tensor 't' shape is [1, 2, 3, 4] +// +// # 'dims' is [3] or 'dims' is [-1] +// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], +// [ 7, 6, 5, 4], +// [ 11, 10, 9, 8]], +// [[15, 14, 13, 12], +// [19, 18, 17, 16], +// [23, 22, 21, 20]]]] +// +// # 'dims' is '[1]' (or 'dims' is '[-3]') +// reverse(t, dims) ==> [[[[12, 13, 14, 15], +// [16, 17, 18, 19], +// [20, 21, 22, 23] +// [[ 0, 1, 2, 3], +// [ 4, 5, 6, 7], +// [ 8, 9, 10, 11]]]] +// +// # 'dims' is '[2]' (or 'dims' is '[-2]') +// reverse(t, dims) ==> [[[[8, 9, 10, 11], +// [4, 5, 6, 7], +// [0, 1, 2, 3]] +// [[20, 21, 22, 23], +// [16, 17, 18, 19], +// [12, 13, 14, 15]]]] +// ``` +// +// Arguments: +// tensor: Up to 8-D. +// axis: 1-D. The indices of the dimensions to reverse. Must be in the range +// `[-rank(tensor), rank(tensor))`. +// +// Returns The same shape as `tensor`. +func ReverseV2(scope *Scope, tensor tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReverseV2", + Input: []tf.Input{ + tensor, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the batched diagonal part of a batched tensor. +// +// This operation returns a tensor with the `diagonal` part +// of the batched `input`. The `diagonal` part is computed as follows: +// +// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a +// tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where: +// +// `diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`. +// +// The input must be at least a matrix. +// +// For example: +// +// ``` +// # 'input' is [[[1, 0, 0, 0] +// [0, 2, 0, 0] +// [0, 0, 3, 0] +// [0, 0, 0, 4]], +// [[5, 0, 0, 0] +// [0, 6, 0, 0] +// [0, 0, 7, 0] +// [0, 0, 0, 8]]] +// +// and input.shape = (2, 4, 4) +// +// tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]] +// +// which has shape (2, 4) +// ``` +// +// Arguments: +// input: Rank `k` tensor where `k >= 2`. +// +// Returns The extracted diagonal(s) having shape +// `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`. +func MatrixDiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDiagPart", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MatrixSetDiagV3Attr is an optional argument to MatrixSetDiagV3. +type MatrixSetDiagV3Attr func(optionalAttr) + +// MatrixSetDiagV3Align sets the optional align attribute to value. +// +// value: Some diagonals are shorter than `max_diag_len` and need to be padded. `align` is +// a string specifying how superdiagonals and subdiagonals should be aligned, +// respectively. There are four possible alignments: "RIGHT_LEFT" (default), +// "LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals +// to the right (left-pads the row) and subdiagonals to the left (right-pads the +// row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is +// the opposite alignment. +// If not specified, defaults to "RIGHT_LEFT" +func MatrixSetDiagV3Align(value string) MatrixSetDiagV3Attr { + return func(m optionalAttr) { + m["align"] = value + } +} + +// Returns a batched matrix tensor with new batched diagonal values. +// +// Given `input` and `diagonal`, this operation returns a tensor with the +// same shape and values as `input`, except for the specified diagonals of the +// innermost matrices. These will be overwritten by the values in `diagonal`. +// +// `input` has `r+1` dimensions `[I, J, ..., L, M, N]`. When `k` is scalar or +// `k[0] == k[1]`, `diagonal` has `r` dimensions `[I, J, ..., L, max_diag_len]`. +// Otherwise, it has `r+1` dimensions `[I, J, ..., L, num_diags, max_diag_len]`. +// `num_diags` is the number of diagonals, `num_diags = k[1] - k[0] + 1`. +// `max_diag_len` is the longest diagonal in the range `[k[0], k[1]]`, +// `max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))` +// +// The output is a tensor of rank `k+1` with dimensions `[I, J, ..., L, M, N]`. +// If `k` is scalar or `k[0] == k[1]`: +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1] +// input[i, j, ..., l, m, n] ; otherwise +// ``` +// +// Otherwise, +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1] +// input[i, j, ..., l, m, n] ; otherwise +// ``` +// where `d = n - m`, `diag_index = k[1] - d`, and +// `index_in_diag = n - max(d, 0) + offset`. +// +// `offset` is zero except when the alignment of the diagonal is to the right. +// ``` +// offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT} +// and `d >= 0`) or +// (`align` in {LEFT_RIGHT, RIGHT_RIGHT} +// and `d <= 0`) +// 0 ; otherwise +// ``` +// where `diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))`. +// +// For example: +// +// ``` +// # The main diagonal. +// input = np.array([[[7, 7, 7, 7], # Input shape: (2, 3, 4) +// [7, 7, 7, 7], +// [7, 7, 7, 7]], +// [[7, 7, 7, 7], +// [7, 7, 7, 7], +// [7, 7, 7, 7]]]) +// diagonal = np.array([[1, 2, 3], # Diagonal shape: (2, 3) +// [4, 5, 6]]) +// tf.matrix_set_diag(input, diagonal) +// ==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4) +// [7, 2, 7, 7], +// [7, 7, 3, 7]], +// [[4, 7, 7, 7], +// [7, 5, 7, 7], +// [7, 7, 6, 7]]] +// +// # A superdiagonal (per batch). +// tf.matrix_set_diag(input, diagonal, k = 1) +// ==> [[[7, 1, 7, 7], # Output shape: (2, 3, 4) +// [7, 7, 2, 7], +// [7, 7, 7, 3]], +// [[7, 4, 7, 7], +// [7, 7, 5, 7], +// [7, 7, 7, 6]]] +// +// # A band of diagonals. +// diagonals = np.array([[[0, 9, 1], # Diagonal shape: (2, 4, 3) +// [6, 5, 8], +// [1, 2, 3], +// [4, 5, 0]], +// [[0, 1, 2], +// [5, 6, 4], +// [6, 1, 2], +// [3, 4, 0]]]) +// tf.matrix_set_diag(input, diagonals, k = (-1, 2)) +// ==> [[[1, 6, 9, 7], # Output shape: (2, 3, 4) +// [4, 2, 5, 1], +// [7, 5, 3, 8]], +// [[6, 5, 1, 7], +// [3, 1, 6, 2], +// [7, 4, 2, 4]]] +// +// # LEFT_RIGHT alignment. +// diagonals = np.array([[[9, 1, 0], # Diagonal shape: (2, 4, 3) +// [6, 5, 8], +// [1, 2, 3], +// [0, 4, 5]], +// [[1, 2, 0], +// [5, 6, 4], +// [6, 1, 2], +// [0, 3, 4]]]) +// tf.matrix_set_diag(input, diagonals, k = (-1, 2), align="LEFT_RIGHT") +// ==> [[[1, 6, 9, 7], # Output shape: (2, 3, 4) +// [4, 2, 5, 1], +// [7, 5, 3, 8]], +// [[6, 5, 1, 7], +// [3, 1, 6, 2], +// [7, 4, 2, 4]]] +// +// ``` +// +// Arguments: +// input: Rank `r+1`, where `r >= 1`. +// diagonal: Rank `r` when `k` is an integer or `k[0] == k[1]`. Otherwise, it has rank `r+1`. +// `k >= 1`. +// k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main +// diagonal, and negative value means subdiagonals. `k` can be a single integer +// (for a single diagonal) or a pair of integers specifying the low and high ends +// of a matrix band. `k[0]` must not be larger than `k[1]`. +// +// Returns Rank `r+1`, with `output.shape = input.shape`. +func MatrixSetDiagV3(scope *Scope, input tf.Output, diagonal tf.Output, k tf.Output, optional ...MatrixSetDiagV3Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixSetDiagV3", + Input: []tf.Input{ + input, diagonal, k, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a batched matrix tensor with new batched diagonal values. +// +// Given `input` and `diagonal`, this operation returns a tensor with the +// same shape and values as `input`, except for the specified diagonals of the +// innermost matrices. These will be overwritten by the values in `diagonal`. +// +// `input` has `r+1` dimensions `[I, J, ..., L, M, N]`. When `k` is scalar or +// `k[0] == k[1]`, `diagonal` has `r` dimensions `[I, J, ..., L, max_diag_len]`. +// Otherwise, it has `r+1` dimensions `[I, J, ..., L, num_diags, max_diag_len]`. +// `num_diags` is the number of diagonals, `num_diags = k[1] - k[0] + 1`. +// `max_diag_len` is the longest diagonal in the range `[k[0], k[1]]`, +// `max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))` +// +// The output is a tensor of rank `k+1` with dimensions `[I, J, ..., L, M, N]`. +// If `k` is scalar or `k[0] == k[1]`: +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1] +// input[i, j, ..., l, m, n] ; otherwise +// ``` +// +// Otherwise, +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1] +// input[i, j, ..., l, m, n] ; otherwise +// ``` +// where `d = n - m`, `diag_index = k[1] - d`, and `index_in_diag = n - max(d, 0)`. +// +// For example: +// +// ``` +// # The main diagonal. +// input = np.array([[[7, 7, 7, 7], # Input shape: (2, 3, 4) +// [7, 7, 7, 7], +// [7, 7, 7, 7]], +// [[7, 7, 7, 7], +// [7, 7, 7, 7], +// [7, 7, 7, 7]]]) +// diagonal = np.array([[1, 2, 3], # Diagonal shape: (2, 3) +// [4, 5, 6]]) +// tf.matrix_set_diag(diagonal) ==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4) +// [7, 2, 7, 7], +// [7, 7, 3, 7]], +// [[4, 7, 7, 7], +// [7, 5, 7, 7], +// [7, 7, 6, 7]]] +// +// # A superdiagonal (per batch). +// tf.matrix_set_diag(diagonal, k = 1) +// ==> [[[7, 1, 7, 7], # Output shape: (2, 3, 4) +// [7, 7, 2, 7], +// [7, 7, 7, 3]], +// [[7, 4, 7, 7], +// [7, 7, 5, 7], +// [7, 7, 7, 6]]] +// +// # A band of diagonals. +// diagonals = np.array([[[1, 2, 3], # Diagonal shape: (2, 2, 3) +// [4, 5, 0]], +// [[6, 1, 2], +// [3, 4, 0]]]) +// tf.matrix_set_diag(diagonals, k = (-1, 0)) +// ==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4) +// [4, 2, 7, 7], +// [0, 5, 3, 7]], +// [[6, 7, 7, 7], +// [3, 1, 7, 7], +// [7, 4, 2, 7]]] +// +// ``` +// +// Arguments: +// input: Rank `r+1`, where `r >= 1`. +// diagonal: Rank `r` when `k` is an integer or `k[0] == k[1]`. Otherwise, it has rank `r+1`. +// `k >= 1`. +// k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main +// diagonal, and negative value means subdiagonals. `k` can be a single integer +// (for a single diagonal) or a pair of integers specifying the low and high ends +// of a matrix band. `k[0]` must not be larger than `k[1]`. +// +// Returns Rank `r+1`, with `output.shape = input.shape`. +func MatrixSetDiagV2(scope *Scope, input tf.Output, diagonal tf.Output, k tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixSetDiagV2", + Input: []tf.Input{ + input, diagonal, k, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a diagonal tensor with a given diagonal values. +// +// Given a `diagonal`, this operation returns a tensor with the `diagonal` and +// everything else padded with zeros. The diagonal is computed as follows: +// +// Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of +// rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: +// +// `output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else. +// +// For example: +// +// ``` +// # 'diagonal' is [1, 2, 3, 4] +// tf.diag(diagonal) ==> [[1, 0, 0, 0] +// [0, 2, 0, 0] +// [0, 0, 3, 0] +// [0, 0, 0, 4]] +// ``` +// +// Arguments: +// diagonal: Rank k tensor where k is at most 1. +func Diag(scope *Scope, diagonal tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Diag", + Input: []tf.Input{ + diagonal, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a tensor of ones with the same shape and type as x. +// +// Arguments: +// x: a tensor of type T. +// +// Returns a tensor of the same shape and type as x but filled with ones. +func OnesLike(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OnesLike", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a constant tensor on the host. Only for writing C++ tests. +// +// Arguments: +// value: Attr `value` is the tensor to return. +// +func HostConst(scope *Scope, value tf.Tensor, dtype tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"value": value, "dtype": dtype} + opspec := tf.OpSpec{ + Type: "HostConst", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Splits a tensor into `num_split` tensors along one dimension. +// +// Arguments: +// axis: 0-D. The dimension along which to split. Must be in the range +// `[-rank(value), rank(value))`. +// value: The tensor to split. +// num_split: The number of ways to split. Must evenly divide +// `value.shape[split_dim]`. +// +// Returns They are identically shaped tensors, whose shape matches that of `value` +// except along `axis`, where their sizes are +// `values.shape[split_dim] / num_split`. +func Split(scope *Scope, axis tf.Output, value tf.Output, num_split int64) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_split": num_split} + opspec := tf.OpSpec{ + Type: "Split", + Input: []tf.Input{ + axis, value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("Split", err) + return + } + return output +} + +// Computes offsets of concat inputs within its output. +// +// For example: +// +// ``` +// # 'x' is [2, 2, 7] +// # 'y' is [2, 3, 7] +// # 'z' is [2, 5, 7] +// concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0] +// ``` +// +// This is typically used by gradient computations for a concat operation. +// +// Arguments: +// concat_dim: The dimension along which to concatenate. +// shape: The `N` int32 vectors representing shape of tensors being concatenated. +// +// Returns The `N` int32 vectors representing the starting offset +// of input tensors within the concatenated output. +func ConcatOffset(scope *Scope, concat_dim tf.Output, shape []tf.Output) (offset []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConcatOffset", + Input: []tf.Input{ + concat_dim, tf.OutputList(shape), + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if offset, idx, err = makeOutputList(op, idx, "offset"); err != nil { + scope.UpdateErr("ConcatOffset", err) + return + } + return offset +} + +// Checks a tensor for NaN and Inf values. +// +// When run, reports an `InvalidArgument` error if `tensor` has any values +// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is. +// +// Arguments: +// +// message: Prefix of the error message. +func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"message": message} + opspec := tf.OpSpec{ + Type: "CheckNumerics", + Input: []tf.Input{ + tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Broadcast an array for a compatible shape. +// +// Broadcasting is the process of making arrays to have compatible shapes +// for arithmetic operations. Two shapes are compatible if for each +// dimension pair they are either equal or one of them is one. When trying +// to broadcast a Tensor to a shape, it starts with the trailing dimensions, +// and works its way forward. +// +// For example, +// +// >>> x = tf.constant([1, 2, 3]) +// >>> y = tf.broadcast_to(x, [3, 3]) +// >>> print(y) +// tf.Tensor( +// [[1 2 3] +// [1 2 3] +// [1 2 3]], shape=(3, 3), dtype=int32) +// +// In the above example, the input Tensor with the shape of `[1, 3]` +// is broadcasted to output Tensor with shape of `[3, 3]`. +// +// When doing broadcasted operations such as multiplying a tensor +// by a scalar, broadcasting (usually) confers some time or space +// benefit, as the broadcasted tensor is never materialized. +// +// However, `broadcast_to` does not carry with it any such benefits. +// The newly-created tensor takes the full memory of the broadcasted +// shape. (In a graph context, `broadcast_to` might be fused to +// subsequent operation and then be optimized away, however.) +// +// Arguments: +// input: A Tensor to broadcast. +// shape: An 1-D `int` Tensor. The shape of the desired output. +// +// Returns A Tensor. +func BroadcastTo(scope *Scope, input tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastTo", + Input: []tf.Input{ + input, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts an array of flat indices into a tuple of coordinate arrays. +// +// +// Example: +// +// ``` +// y = tf.unravel_index(indices=[2, 5, 7], dims=[3, 3]) +// # 'dims' represent a hypothetical (3, 3) tensor of indices: +// # [[0, 1, *2*], +// # [3, 4, *5*], +// # [6, *7*, 8]] +// # For each entry from 'indices', this operation returns +// # its coordinates (marked with '*'), such as +// # 2 ==> (0, 2) +// # 5 ==> (1, 2) +// # 7 ==> (2, 1) +// y ==> [[0, 1, 2], [2, 2, 1]] +// ``` +// +// @compatibility(numpy) +// Equivalent to np.unravel_index +// @end_compatibility +// +// Arguments: +// indices: An 0-D or 1-D `int` Tensor whose elements are indices into the +// flattened version of an array of dimensions dims. +// dims: An 1-D `int` Tensor. The shape of the array to use for unraveling +// indices. +// +// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the +// same shape as the indices array. +func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnravelIndex", + Input: []tf.Input{ + indices, dims, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EmptyAttr is an optional argument to Empty. +type EmptyAttr func(optionalAttr) + +// EmptyInit sets the optional init attribute to value. +// +// value: If True, initialize the returned tensor with the default value of dtype. Otherwise, the implementation is free not to initializethe tensor's content. +// If not specified, defaults to false +func EmptyInit(value bool) EmptyAttr { + return func(m optionalAttr) { + m["init"] = value + } +} + +// Creates a tensor with the given shape. +// +// This operation creates a tensor of `shape` and `dtype`. +// +// Arguments: +// shape: 1-D. Represents the shape of the output tensor. +// +// +// Returns A `Tensor` of type `T`. +func Empty(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...EmptyAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Empty", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Subtracts `v` into specified rows of `x`. +// +// Computes y = x; y[i, :] -= v; return y. +// +// Arguments: +// x: A `Tensor` of type T. +// i: A vector. Indices into the left-most dimension of `x`. +// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// +// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. +func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InplaceSub", + Input: []tf.Input{ + x, i, v, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds v into specified rows of x. +// +// Computes y = x; y[i, :] += v; return y. +// +// Arguments: +// x: A `Tensor` of type T. +// i: A vector. Indices into the left-most dimension of `x`. +// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// +// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. +func InplaceAdd(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InplaceAdd", + Input: []tf.Input{ + x, i, v, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Makes a copy of `x`. +// +// Arguments: +// x: The source tensor of type `T`. +// +// Returns y: A `Tensor` of type `T`. A copy of `x`. Guaranteed that `y` +// is not an alias of `x`. +func DeepCopy(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeepCopy", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PackAttr is an optional argument to Pack. +type PackAttr func(optionalAttr) + +// PackAxis sets the optional axis attribute to value. +// +// value: Dimension along which to pack. Negative values wrap around, so the +// valid range is `[-(R+1), R+1)`. +// If not specified, defaults to 0 +func PackAxis(value int64) PackAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. +// +// Packs the `N` tensors in `values` into a tensor with rank one higher than each +// tensor in `values`, by packing them along the `axis` dimension. +// Given a list of tensors of shape `(A, B, C)`; +// +// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. +// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. +// Etc. +// +// For example: +// +// ``` +// # 'x' is [1, 4] +// # 'y' is [2, 5] +// # 'z' is [3, 6] +// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. +// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] +// ``` +// +// This is the opposite of `unpack`. +// +// Arguments: +// values: Must be of same shape and type. +// +// Returns The packed tensor. +func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Pack", + Input: []tf.Input{ + tf.OutputList(values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MfccAttr is an optional argument to Mfcc. +type MfccAttr func(optionalAttr) + +// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. +// +// value: The highest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 4000 +func MfccUpperFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["upper_frequency_limit"] = value + } +} + +// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. +// +// value: The lowest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 20 +func MfccLowerFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["lower_frequency_limit"] = value + } +} + +// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. +// +// value: Resolution of the Mel bank used internally. +// If not specified, defaults to 40 +func MfccFilterbankChannelCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["filterbank_channel_count"] = value + } +} + +// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. +// +// value: How many output channels to produce per time slice. +// If not specified, defaults to 13 +func MfccDctCoefficientCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["dct_coefficient_count"] = value + } +} + +// Transforms a spectrogram into a form that's useful for speech recognition. +// +// Mel Frequency Cepstral Coefficients are a way of representing audio data that's +// been effective as an input feature for machine learning. They are created by +// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the +// higher frequencies that are less significant to the human ear. They have a long +// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum +// is a good resource to learn more. +// +// Arguments: +// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared +// set to true. +// sample_rate: How many samples per second the source audio used. +func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Mfcc", + Input: []tf.Input{ + spectrogram, sample_rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AudioSpectrogramAttr is an optional argument to AudioSpectrogram. +type AudioSpectrogramAttr func(optionalAttr) + +// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value. +// +// value: Whether to return the squared magnitude or just the +// magnitude. Using squared magnitude can avoid extra calculations. +// If not specified, defaults to false +func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr { + return func(m optionalAttr) { + m["magnitude_squared"] = value + } +} + +// Produces a visualization of audio data over time. +// +// Spectrograms are a standard way of representing audio information as a series of +// slices of frequency information, one slice for each window of time. By joining +// these together into a sequence, they form a distinctive fingerprint of the sound +// over time. +// +// This op expects to receive audio data as an input, stored as floats in the range +// -1 to 1, together with a window width in samples, and a stride specifying how +// far to move the window between slices. From this it generates a three +// dimensional output. The first dimension is for the channels in the input, so a +// stereo audio input would have two here for example. The second dimension is time, +// with successive frequency slices. The third dimension has an amplitude value for +// each frequency during that time slice. +// +// This means the layout when converted and saved as an image is rotated 90 degrees +// clockwise from a typical spectrogram. Time is descending down the Y axis, and +// the frequency decreases from left to right. +// +// Each value in the result represents the square root of the sum of the real and +// imaginary parts of an FFT on the current window of samples. In this way, the +// lowest dimension represents the power of each frequency in the current window, +// and adjacent windows are concatenated in the next dimension. +// +// To get a more intuitive and visual look at what this operation does, you can run +// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the +// resulting spectrogram as a PNG image. +// +// Arguments: +// input: Float representation of audio data. +// window_size: How wide the input window is in samples. For the highest efficiency +// this should be a power of two, but other values are accepted. +// stride: How widely apart the center of adjacent sample windows should be. +// +// Returns 3D representation of the audio frequencies as an image. +func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"window_size": window_size, "stride": stride} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSpectrogram", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeWavAttr is an optional argument to DecodeWav. +type DecodeWavAttr func(optionalAttr) + +// DecodeWavDesiredChannels sets the optional desired_channels attribute to value. +// +// value: Number of sample channels wanted. +// If not specified, defaults to -1 +func DecodeWavDesiredChannels(value int64) DecodeWavAttr { + return func(m optionalAttr) { + m["desired_channels"] = value + } +} + +// DecodeWavDesiredSamples sets the optional desired_samples attribute to value. +// +// value: Length of audio requested. +// If not specified, defaults to -1 +func DecodeWavDesiredSamples(value int64) DecodeWavAttr { + return func(m optionalAttr) { + m["desired_samples"] = value + } +} + +// Decode a 16-bit PCM WAV file to a float tensor. +// +// The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float. +// +// When desired_channels is set, if the input contains fewer channels than this +// then the last channel will be duplicated to give the requested number, else if +// the input has more channels than requested then the additional channels will be +// ignored. +// +// If desired_samples is set, then the audio will be cropped or padded with zeroes +// to the requested length. +// +// The first output contains a Tensor with the content of the audio samples. The +// lowest dimension will be the number of channels, and the second will be the +// number of samples. For example, a ten-sample-long stereo WAV file should give an +// output shape of [10, 2]. +// +// Arguments: +// contents: The WAV-encoded audio, usually from a file. +// +// Returns: +// audio: 2-D with shape `[length, channels]`. +// sample_rate: Scalar holding the sample rate found in the WAV header. +func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (audio tf.Output, sample_rate tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeWav", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// UnbatchGradAttr is an optional argument to UnbatchGrad. +type UnbatchGradAttr func(optionalAttr) + +// UnbatchGradContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func UnbatchGradContainer(value string) UnbatchGradAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// UnbatchGradSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func UnbatchGradSharedName(value string) UnbatchGradAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Gradient of Unbatch. +// +// Acts like Batch but using the given batch_index index of batching things as they +// become available. This ensures that the gradients are propagated back in the +// same session which did the forward pass. +// +// original_input: The input to the Unbatch operation this is the gradient of. +// batch_index: The batch_index given to the Unbatch operation this is the gradient +// of. +// grad: The downstream gradient. +// id: The id scalar emitted by Batch. +// batched_grad: The return value, either an empty tensor or the batched gradient. +// container: Container to control resource sharing. +// shared_name: Instances of UnbatchGrad with the same container and shared_name +// are assumed to possibly belong to the same batch. If left empty, the op name +// will be used as the shared name. +func UnbatchGrad(scope *Scope, original_input tf.Output, batch_index tf.Output, grad tf.Output, id tf.Output, optional ...UnbatchGradAttr) (batched_grad tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnbatchGrad", + Input: []tf.Input{ + original_input, batch_index, grad, id, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes element-wise population count (a.k.a. popcount, bitsum, bitcount). +// +// For each entry in `x`, calculates the number of `1` (on) bits in the binary +// representation of that entry. +// +// **NOTE**: It is more efficient to first `tf.bitcast` your tensors into +// `int32` or `int64` and perform the bitcount on the result, than to feed in +// 8- or 16-bit inputs and then aggregate the resulting counts. +func PopulationCount(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "PopulationCount", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Bucketize each feature based on bucket boundaries. +// +// An op that returns a list of float tensors, where each tensor represents the +// bucketized values for a single feature. +// +// Arguments: +// float_values: float; List of Rank 1 Tensor each containing float values for a single feature. +// bucket_boundaries: float; List of Rank 1 Tensors each containing the bucket boundaries for a single +// feature. +// +// Returns int; List of Rank 1 Tensors each containing the bucketized values for a single feature. +func BoostedTreesBucketize(scope *Scope, float_values []tf.Output, bucket_boundaries []tf.Output) (buckets []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesBucketize", + Input: []tf.Input{ + tf.OutputList(float_values), tf.OutputList(bucket_boundaries), + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if buckets, idx, err = makeOutputList(op, idx, "buckets"); err != nil { + scope.UpdateErr("BoostedTreesBucketize", err) + return + } + return buckets +} + +// Returns immutable tensor from memory region. +// +// The current implementation memmaps the tensor from a file. +// +// Arguments: +// dtype: Type of the returned tensor. +// shape: Shape of the returned tensor. +// memory_region_name: Name of readonly memory region used by the tensor, see +// NewReadOnlyMemoryRegionFromFile in tensorflow::Env. +func ImmutableConst(scope *Scope, dtype tf.DataType, shape tf.Shape, memory_region_name string) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape, "memory_region_name": memory_region_name} + opspec := tf.OpSpec{ + Type: "ImmutableConst", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Add the quantile summaries to each quantile stream resource. +// +// An op that adds a list of quantile summaries to a quantile stream resource. Each +// summary Tensor is rank 2, containing summaries (value, weight, min_rank, max_rank) +// for a single feature. +// +// Arguments: +// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. +// summaries: string; List of Rank 2 Tensor each containing the summaries for a single feature. +// +// Returns the created operation. +func BoostedTreesQuantileStreamResourceAddSummaries(scope *Scope, quantile_stream_resource_handle tf.Output, summaries []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesQuantileStreamResourceAddSummaries", + Input: []tf.Input{ + quantile_stream_resource_handle, tf.OutputList(summaries), + }, + } + return scope.AddOperation(opspec) +} + +// BoostedTreesCreateQuantileStreamResourceAttr is an optional argument to BoostedTreesCreateQuantileStreamResource. +type BoostedTreesCreateQuantileStreamResourceAttr func(optionalAttr) + +// BoostedTreesCreateQuantileStreamResourceMaxElements sets the optional max_elements attribute to value. +// +// value: int; The maximum number of data points that can be fed to the stream. +// If not specified, defaults to 1099511627776 +func BoostedTreesCreateQuantileStreamResourceMaxElements(value int64) BoostedTreesCreateQuantileStreamResourceAttr { + return func(m optionalAttr) { + m["max_elements"] = value + } +} + +// Create the Resource for Quantile Streams. +// +// Arguments: +// quantile_stream_resource_handle: resource; Handle to quantile stream resource. +// epsilon: float; The required approximation error of the stream resource. +// num_streams: int; The number of streams managed by the resource that shares the same epsilon. +// +// Returns the created operation. +func BoostedTreesCreateQuantileStreamResource(scope *Scope, quantile_stream_resource_handle tf.Output, epsilon tf.Output, num_streams tf.Output, optional ...BoostedTreesCreateQuantileStreamResourceAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCreateQuantileStreamResource", + Input: []tf.Input{ + quantile_stream_resource_handle, epsilon, num_streams, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// BoostedTreesUpdateEnsembleV2Attr is an optional argument to BoostedTreesUpdateEnsembleV2. +type BoostedTreesUpdateEnsembleV2Attr func(optionalAttr) + +// BoostedTreesUpdateEnsembleV2LogitsDimension sets the optional logits_dimension attribute to value. +// +// value: scalar, dimension of the logits +// If not specified, defaults to 1 +func BoostedTreesUpdateEnsembleV2LogitsDimension(value int64) BoostedTreesUpdateEnsembleV2Attr { + return func(m optionalAttr) { + m["logits_dimension"] = value + } +} + +// Updates the tree ensemble by adding a layer to the last tree being grown +// +// or by starting a new tree. +// +// Arguments: +// tree_ensemble_handle: Handle to the ensemble variable. +// feature_ids: Rank 1 tensor with ids for each feature. This is the real id of +// the feature that will be used in the split. +// dimension_ids: List of rank 1 tensors representing the dimension in each feature. +// node_ids: List of rank 1 tensors representing the nodes for which this feature +// has a split. +// gains: List of rank 1 tensors representing the gains for each of the feature's +// split. +// thresholds: List of rank 1 tensors representing the thesholds for each of the +// feature's split. +// left_node_contribs: List of rank 2 tensors with left leaf contribs for each of +// the feature's splits. Will be added to the previous node values to constitute +// the values of the left nodes. +// right_node_contribs: List of rank 2 tensors with right leaf contribs for each +// of the feature's splits. Will be added to the previous node values to constitute +// the values of the right nodes. +// split_types: List of rank 1 tensors representing the split type for each feature. +// max_depth: Max depth of the tree to build. +// learning_rate: shrinkage const for each new tree. +// pruning_mode: 0-No pruning, 1-Pre-pruning, 2-Post-pruning. +// +// Returns the created operation. +func BoostedTreesUpdateEnsembleV2(scope *Scope, tree_ensemble_handle tf.Output, feature_ids []tf.Output, dimension_ids []tf.Output, node_ids []tf.Output, gains []tf.Output, thresholds []tf.Output, left_node_contribs []tf.Output, right_node_contribs []tf.Output, split_types []tf.Output, max_depth tf.Output, learning_rate tf.Output, pruning_mode tf.Output, optional ...BoostedTreesUpdateEnsembleV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesUpdateEnsembleV2", + Input: []tf.Input{ + tree_ensemble_handle, tf.OutputList(feature_ids), tf.OutputList(dimension_ids), tf.OutputList(node_ids), tf.OutputList(gains), tf.OutputList(thresholds), tf.OutputList(left_node_contribs), tf.OutputList(right_node_contribs), tf.OutputList(split_types), max_depth, learning_rate, pruning_mode, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Updates the tree ensemble by either adding a layer to the last tree being grown +// +// or by starting a new tree. +// +// Arguments: +// tree_ensemble_handle: Handle to the ensemble variable. +// feature_ids: Rank 1 tensor with ids for each feature. This is the real id of +// the feature that will be used in the split. +// node_ids: List of rank 1 tensors representing the nodes for which this feature +// has a split. +// gains: List of rank 1 tensors representing the gains for each of the feature's +// split. +// thresholds: List of rank 1 tensors representing the thesholds for each of the +// feature's split. +// left_node_contribs: List of rank 2 tensors with left leaf contribs for each of +// the feature's splits. Will be added to the previous node values to constitute +// the values of the left nodes. +// right_node_contribs: List of rank 2 tensors with right leaf contribs for each +// of the feature's splits. Will be added to the previous node values to constitute +// the values of the right nodes. +// max_depth: Max depth of the tree to build. +// learning_rate: shrinkage const for each new tree. +// pruning_mode: 0-No pruning, 1-Pre-pruning, 2-Post-pruning. +// +// Returns the created operation. +func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, feature_ids tf.Output, node_ids []tf.Output, gains []tf.Output, thresholds []tf.Output, left_node_contribs []tf.Output, right_node_contribs []tf.Output, max_depth tf.Output, learning_rate tf.Output, pruning_mode int64) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pruning_mode": pruning_mode} + opspec := tf.OpSpec{ + Type: "BoostedTreesUpdateEnsemble", + Input: []tf.Input{ + tree_ensemble_handle, feature_ids, tf.OutputList(node_ids), tf.OutputList(gains), tf.OutputList(thresholds), tf.OutputList(left_node_contribs), tf.OutputList(right_node_contribs), max_depth, learning_rate, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Runs multiple additive regression ensemble predictors on input instances and +// +// computes the update to cached logits. It is designed to be used during training. +// It traverses the trees starting from cached tree id and cached node id and +// calculates the updates to be pushed to the cache. +// +// Arguments: +// +// cached_tree_ids: Rank 1 Tensor containing cached tree ids which is the starting +// tree of prediction. +// cached_node_ids: Rank 1 Tensor containing cached node id which is the starting +// node of prediction. +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for partial logits +// shape. +// +// Returns: +// partial_logits: Rank 2 Tensor containing logits update (with respect to cached +// values stored) for each example. +// tree_ids: Rank 1 Tensor containing new tree ids for each example. +// node_ids: Rank 1 Tensor containing new node ids in the new tree_ids. +func BoostedTreesTrainingPredict(scope *Scope, tree_ensemble_handle tf.Output, cached_tree_ids tf.Output, cached_node_ids tf.Output, bucketized_features []tf.Output, logits_dimension int64) (partial_logits tf.Output, tree_ids tf.Output, node_ids tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesTrainingPredict", + Input: []tf.Input{ + tree_ensemble_handle, cached_tree_ids, cached_node_ids, tf.OutputList(bucketized_features), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Aggregates the summary of accumulated stats for the batch. +// +// The summary stats contains gradients and hessians accumulated for each node, feature dimension id and bucket. +// +// Arguments: +// node_ids: int32; Rank 1 Tensor containing node ids for each example, shape [batch_size]. +// gradients: float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example. +// hessians: float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example. +// feature: int32; Rank 2 feature Tensors (shape=[batch_size, feature_dimension]). +// max_splits: int; the maximum number of splits possible in the whole tree. +// num_buckets: int; equals to the maximum possible value of bucketized feature. +// +// Returns output Rank 4 Tensor (shape=[splits, feature_dimension, buckets, logits_dimension + hessian_dimension]) +// containing accumulated stats for each node, feature dimension and bucket. +func BoostedTreesAggregateStats(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, feature tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "BoostedTreesAggregateStats", + Input: []tf.Input{ + node_ids, gradients, hessians, feature, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Makes the summary of accumulated stats for the batch. +// +// The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example. +// +// Arguments: +// node_ids: int32 Rank 1 Tensor containing node ids, which each example falls into for the requested layer. +// gradients: float32; Rank 2 Tensor (shape=[#examples, 1]) for gradients. +// hessians: float32; Rank 2 Tensor (shape=[#examples, 1]) for hessians. +// bucketized_features_list: int32 list of Rank 1 Tensors, each containing the bucketized feature (for each feature column). +// max_splits: int; the maximum number of splits possible in the whole tree. +// num_buckets: int; equals to the maximum possible value of bucketized feature. +// +// Returns output Rank 4 Tensor (shape=[#features, #splits, #buckets, 2]) containing accumulated stats put into the corresponding node and bucket. The first index of 4th dimension refers to gradients, and the second to hessians. +func BoostedTreesMakeStatsSummary(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, bucketized_features_list []tf.Output, max_splits int64, num_buckets int64) (stats_summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "BoostedTreesMakeStatsSummary", + Input: []tf.Input{ + node_ids, gradients, hessians, tf.OutputList(bucketized_features_list), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deserializes a serialized tree ensemble config and replaces current tree +// +// ensemble. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble. +// stamp_token: Token to use as the new value of the resource stamp. +// tree_ensemble_serialized: Serialized proto of the ensemble. +// +// Returns the created operation. +func BoostedTreesDeserializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesDeserializeEnsemble", + Input: []tf.Input{ + tree_ensemble_handle, stamp_token, tree_ensemble_serialized, + }, + } + return scope.AddOperation(opspec) +} + +// Flush the quantile summaries from each quantile stream resource. +// +// An op that outputs a list of quantile summaries of a quantile stream resource. +// Each summary Tensor is rank 2, containing summaries (value, weight, min_rank, +// max_rank) for a single feature. +// +// Arguments: +// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. +// +func BoostedTreesFlushQuantileSummaries(scope *Scope, quantile_stream_resource_handle tf.Output, num_features int64) (summaries []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_features": num_features} + opspec := tf.OpSpec{ + Type: "BoostedTreesFlushQuantileSummaries", + Input: []tf.Input{ + quantile_stream_resource_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if summaries, idx, err = makeOutputList(op, idx, "summaries"); err != nil { + scope.UpdateErr("BoostedTreesFlushQuantileSummaries", err) + return + } + return summaries +} + +// BoostedTreesSparseCalculateBestFeatureSplitAttr is an optional argument to BoostedTreesSparseCalculateBestFeatureSplit. +type BoostedTreesSparseCalculateBestFeatureSplitAttr func(optionalAttr) + +// BoostedTreesSparseCalculateBestFeatureSplitSplitType sets the optional split_type attribute to value. +// +// value: A string indicating if this Op should perform inequality split or equality split. +// If not specified, defaults to "inequality" +func BoostedTreesSparseCalculateBestFeatureSplitSplitType(value string) BoostedTreesSparseCalculateBestFeatureSplitAttr { + return func(m optionalAttr) { + m["split_type"] = value + } +} + +// Calculates gains for each feature and returns the best possible split information for the feature. +// +// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. +// +// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split. +// +// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). +// +// The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature. +// +// Arguments: +// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive). +// stats_summary_indices: A Rank 2 int64 tensor of dense shape [N, 4] (N specifies the number of non-zero values) for accumulated stats summary (gradient/hessian) per node per bucket for each feature. The second dimension contains node id, feature dimension, bucket id, and stats dim. +// stats dim is the sum of logits dimension and hessian dimension, hessian dimension can either be logits dimension if diagonal hessian is used, or logits dimension^2 if full hessian is used. +// stats_summary_values: A Rank 1 float tensor of dense shape [N] (N specifies the number of non-zero values), which supplies the values for each element in summary_indices. +// stats_summary_shape: A Rank 1 float tensor of dense shape [4], which specifies the dense shape of the sparse tensor, which is [num tree nodes, feature dimensions, num buckets, stats dim]. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// tree_complexity: adjustment to the gain, per leaf based. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. +// logits_dimension: The dimension of logit, i.e., number of classes. +// +// Returns: +// node_ids: A Rank 1 tensor indicating possible node ids that can be split. +// gains: A Rank 1 tensor indicating the best gains to split each node. +// feature_dimensions: A Rank 1 tensor indicating the best feature dimension for each feature to split for each node. +// thresholds: A Rank 1 tensor indicating the bucket id to compare with (as a threshold) for split in each node. +// left_node_contribs: A Rank 2 tensor indicating the contribution of the left nodes when branching from parent nodes to the left direction by the given threshold for each feature. +// This value will be used to make the left node value by adding to the parent node value. Second dimension size is logits dimension. +// right_node_contribs: A Rank 2 tensor, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node. +// split_with_default_directions: A Rank 1 tensor indicating which direction to go if data is missing. +// Inequality with default left returns 0, inequality with default right returns 1, equality with default right returns 2. +func BoostedTreesSparseCalculateBestFeatureSplit(scope *Scope, node_id_range tf.Output, stats_summary_indices tf.Output, stats_summary_values tf.Output, stats_summary_shape tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, logits_dimension int64, optional ...BoostedTreesSparseCalculateBestFeatureSplitAttr) (node_ids tf.Output, gains tf.Output, feature_dimensions tf.Output, thresholds tf.Output, left_node_contribs tf.Output, right_node_contribs tf.Output, split_with_default_directions tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesSparseCalculateBestFeatureSplit", + Input: []tf.Input{ + node_id_range, stats_summary_indices, stats_summary_values, stats_summary_shape, l1, l2, tree_complexity, min_node_weight, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// Calculates gains for each feature and returns the best possible split information for each node. However, if no split is found, then no split information is returned for that node. +// +// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. +// +// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split. +// +// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). +// +// The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature. +// +// Arguments: +// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive). +// stats_summaries_list: A list of Rank 4 tensor (#shape=[max_splits, feature_dims, bucket, stats_dims]) for accumulated stats summary (gradient/hessian) per node, per dimension, per buckets for each feature. +// The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used. +// split_types: A Rank 1 tensor indicating if this Op should perform inequality split or equality split per feature. +// candidate_feature_ids: Rank 1 tensor with ids for each feature. This is the real id of the feature. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// tree_complexity: adjustment to the gain, per leaf based. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. +// logits_dimension: The dimension of logit, i.e., number of classes. +// +// Returns: +// node_ids: A Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes. +// gains: A Rank 1 tensor indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes. +// feature_ids: A Rank 1 tensors indicating the best feature id for each node. See above for details like shapes and sizes. +// feature_dimensions: A Rank 1 tensors indicating the best feature dimension for each feature to split for certain nodes if the feature is multi-dimension. See above for details like shapes and sizes. +// thresholds: A Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes. +// left_node_contribs: A Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes. +// right_node_contribs: A Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node. +// split_with_default_directions: A Rank 1 tensors indicating the which direction to go if data is missing. See above for details like shapes and sizes. +// Inequality with default left returns 0, inequality with default right returns 1, equality with default right returns 2. +func BoostedTreesCalculateBestFeatureSplitV2(scope *Scope, node_id_range tf.Output, stats_summaries_list []tf.Output, split_types tf.Output, candidate_feature_ids tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, logits_dimension int64) (node_ids tf.Output, gains tf.Output, feature_ids tf.Output, feature_dimensions tf.Output, thresholds tf.Output, left_node_contribs tf.Output, right_node_contribs tf.Output, split_with_default_directions tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesCalculateBestFeatureSplitV2", + Input: []tf.Input{ + node_id_range, tf.OutputList(stats_summaries_list), split_types, candidate_feature_ids, l1, l2, tree_complexity, min_node_weight, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6), op.Output(7) +} + +// Calculates gains for each feature and returns the best possible split information for the feature. +// +// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. +// +// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split. +// +// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). +// +// The length of output lists are all of the same length, `num_features`. +// The output shapes are compatible in a way that the first dimension of all tensors of all lists are the same and equal to the number of possible split nodes for each feature. +// +// Arguments: +// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive). +// stats_summary_list: A list of Rank 3 tensor (#shape=[max_splits, bucket, 2]) for accumulated stats summary (gradient/hessian) per node per buckets for each feature. The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// tree_complexity: adjustment to the gain, per leaf based. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. +// max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors. +// +// Returns: +// node_ids_list: An output list of Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes. +// gains_list: An output list of Rank 1 tensors indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes. +// thresholds_list: An output list of Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes. +// left_node_contribs_list: A list of Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes. +// right_node_contribs_list: A list of Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node. +func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Output, stats_summary_list []tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, max_splits int64) (node_ids_list []tf.Output, gains_list []tf.Output, thresholds_list []tf.Output, left_node_contribs_list []tf.Output, right_node_contribs_list []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"max_splits": max_splits} + opspec := tf.OpSpec{ + Type: "BoostedTreesCalculateBestGainsPerFeature", + Input: []tf.Input{ + node_id_range, tf.OutputList(stats_summary_list), l1, l2, tree_complexity, min_node_weight, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if node_ids_list, idx, err = makeOutputList(op, idx, "node_ids_list"); err != nil { + scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err) + return + } + if gains_list, idx, err = makeOutputList(op, idx, "gains_list"); err != nil { + scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err) + return + } + if thresholds_list, idx, err = makeOutputList(op, idx, "thresholds_list"); err != nil { + scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err) + return + } + if left_node_contribs_list, idx, err = makeOutputList(op, idx, "left_node_contribs_list"); err != nil { + scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err) + return + } + if right_node_contribs_list, idx, err = makeOutputList(op, idx, "right_node_contribs_list"); err != nil { + scope.UpdateErr("BoostedTreesCalculateBestGainsPerFeature", err) + return + } + return node_ids_list, gains_list, thresholds_list, left_node_contribs_list, right_node_contribs_list +} + +// Checks whether a tree ensemble has been initialized. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble resource. +// +// Returns output boolean on whether it is initialized or not. +func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsBoostedTreesEnsembleInitialized", + Input: []tf.Input{ + tree_ensemble_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesEnsembleResourceHandleOpAttr is an optional argument to BoostedTreesEnsembleResourceHandleOp. +type BoostedTreesEnsembleResourceHandleOpAttr func(optionalAttr) + +// BoostedTreesEnsembleResourceHandleOpContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func BoostedTreesEnsembleResourceHandleOpContainer(value string) BoostedTreesEnsembleResourceHandleOpAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// BoostedTreesEnsembleResourceHandleOpSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func BoostedTreesEnsembleResourceHandleOpSharedName(value string) BoostedTreesEnsembleResourceHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a handle to a BoostedTreesEnsembleResource +func BoostedTreesEnsembleResourceHandleOp(scope *Scope, optional ...BoostedTreesEnsembleResourceHandleOpAttr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesEnsembleResourceHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deserializes a proto into the tree handle +// +// Arguments: +// tree_handle: Handle to the tree resource to be restored. +// tree_config: Serialied proto string of the boosted_trees.Tree proto. +// +// Returns the created operation. +func TensorForestTreeDeserialize(scope *Scope, tree_handle tf.Output, tree_config tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorForestTreeDeserialize", + Input: []tf.Input{ + tree_handle, tree_config, + }, + } + return scope.AddOperation(opspec) +} + +// Serializes the tree handle to a proto +// +// Arguments: +// tree_handle: Handle to the tree resource to be serialized. +// +// Returns Serialied proto string of the tree resource. +func TensorForestTreeSerialize(scope *Scope, tree_handle tf.Output) (tree_config tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorForestTreeSerialize", + Input: []tf.Input{ + tree_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a tree resource and returns a handle to it. +// +// Arguments: +// tree_handle: Handle to the tree resource to be created. +// tree_config: Serialized proto string of the boosted_trees.Tree. +// +// Returns the created operation. +func TensorForestCreateTreeVariable(scope *Scope, tree_handle tf.Output, tree_config tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorForestCreateTreeVariable", + Input: []tf.Input{ + tree_handle, tree_config, + }, + } + return scope.AddOperation(opspec) +} + +// Checks whether a tree has been initialized. +// +// Arguments: +// tree_handle: Handle to the tree. +// +// Returns Whether the tree is initialized. +func TensorForestTreeIsInitializedOp(scope *Scope, tree_handle tf.Output) (is_initialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorForestTreeIsInitializedOp", + Input: []tf.Input{ + tree_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorForestTreeResourceHandleOpAttr is an optional argument to TensorForestTreeResourceHandleOp. +type TensorForestTreeResourceHandleOpAttr func(optionalAttr) + +// TensorForestTreeResourceHandleOpContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func TensorForestTreeResourceHandleOpContainer(value string) TensorForestTreeResourceHandleOpAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// TensorForestTreeResourceHandleOpSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func TensorForestTreeResourceHandleOpSharedName(value string) TensorForestTreeResourceHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a handle to a TensorForestTreeResource +func TensorForestTreeResourceHandleOp(scope *Scope, optional ...TensorForestTreeResourceHandleOpAttr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorForestTreeResourceHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AllCandidateSamplerAttr is an optional argument to AllCandidateSampler. +type AllCandidateSamplerAttr func(optionalAttr) + +// AllCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func AllCandidateSamplerSeed(value int64) AllCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// AllCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func AllCandidateSamplerSeed2(value int64) AllCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to produce. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// +// Returns: +// sampled_candidates: A vector of length num_sampled, in which each element is +// the ID of a sampled candidate. +// true_expected_count: A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability. +// sampled_expected_count: A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func AllCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, optional ...AllCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AllCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// FixedUnigramCandidateSamplerAttr is an optional argument to FixedUnigramCandidateSampler. +type FixedUnigramCandidateSamplerAttr func(optionalAttr) + +// FixedUnigramCandidateSamplerVocabFile sets the optional vocab_file attribute to value. +// +// value: Each valid line in this file (which should have a CSV-like format) +// corresponds to a valid word ID. IDs are in sequential order, starting from +// num_reserved_ids. The last entry in each line is expected to be a value +// corresponding to the count or relative probability. Exactly one of vocab_file +// and unigrams needs to be passed to this op. +// If not specified, defaults to "" +func FixedUnigramCandidateSamplerVocabFile(value string) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["vocab_file"] = value + } +} + +// FixedUnigramCandidateSamplerDistortion sets the optional distortion attribute to value. +// +// value: The distortion is used to skew the unigram probability distribution. +// Each weight is first raised to the distortion's power before adding to the +// internal unigram distribution. As a result, distortion = 1.0 gives regular +// unigram sampling (as defined by the vocab file), and distortion = 0.0 gives +// a uniform distribution. +// If not specified, defaults to 1 +func FixedUnigramCandidateSamplerDistortion(value float32) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["distortion"] = value + } +} + +// FixedUnigramCandidateSamplerNumReservedIds sets the optional num_reserved_ids attribute to value. +// +// value: Optionally some reserved IDs can be added in the range [0, +// ..., num_reserved_ids) by the users. One use case is that a special unknown +// word token is used as ID 0. These IDs will have a sampling probability of 0. +// If not specified, defaults to 0 +func FixedUnigramCandidateSamplerNumReservedIds(value int64) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["num_reserved_ids"] = value + } +} + +// FixedUnigramCandidateSamplerNumShards sets the optional num_shards attribute to value. +// +// value: A sampler can be used to sample from a subset of the original range +// in order to speed up the whole computation through parallelism. This parameter +// (together with 'shard') indicates the number of partitions that are being +// used in the overall computation. +// If not specified, defaults to 1 +// +// REQUIRES: value >= 1 +func FixedUnigramCandidateSamplerNumShards(value int64) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["num_shards"] = value + } +} + +// FixedUnigramCandidateSamplerShard sets the optional shard attribute to value. +// +// value: A sampler can be used to sample from a subset of the original range +// in order to speed up the whole computation through parallelism. This parameter +// (together with 'num_shards') indicates the particular partition number of a +// sampler op, when partitioning is being used. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func FixedUnigramCandidateSamplerShard(value int64) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["shard"] = value + } +} + +// FixedUnigramCandidateSamplerUnigrams sets the optional unigrams attribute to value. +// +// value: A list of unigram counts or probabilities, one per ID in sequential +// order. Exactly one of vocab_file and unigrams should be passed to this op. +// If not specified, defaults to <> +func FixedUnigramCandidateSamplerUnigrams(value []float32) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["unigrams"] = value + } +} + +// FixedUnigramCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func FixedUnigramCandidateSamplerSeed(value int64) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// FixedUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func FixedUnigramCandidateSamplerSeed2(value int64) FixedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. +// +// A unigram sampler could use a fixed unigram distribution read from a +// file or passed in as an in-memory array instead of building up the distribution +// from data on the fly. There is also an option to skew the distribution by +// applying a distortion power to the weights. +// +// The vocabulary file should be in CSV-like format, with the last field +// being the weight associated with the word. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). +// +// Returns: +// sampled_candidates: A vector of length num_sampled, in which each element is +// the ID of a sampled candidate. +// true_expected_count: A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability. +// sampled_expected_count: A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func FixedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...FixedUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FixedUnigramCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ThreadUnsafeUnigramCandidateSamplerAttr is an optional argument to ThreadUnsafeUnigramCandidateSampler. +type ThreadUnsafeUnigramCandidateSamplerAttr func(optionalAttr) + +// ThreadUnsafeUnigramCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ThreadUnsafeUnigramCandidateSamplerSeed(value int64) ThreadUnsafeUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// ThreadUnsafeUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func ThreadUnsafeUnigramCandidateSamplerSeed2(value int64) ThreadUnsafeUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). +// +// Returns: +// sampled_candidates: A vector of length num_sampled, in which each element is +// the ID of a sampled candidate. +// true_expected_count: A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability. +// sampled_expected_count: A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func ThreadUnsafeUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...ThreadUnsafeUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ThreadUnsafeUnigramCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// MatrixDiagPartV3Attr is an optional argument to MatrixDiagPartV3. +type MatrixDiagPartV3Attr func(optionalAttr) + +// MatrixDiagPartV3Align sets the optional align attribute to value. +// +// value: Some diagonals are shorter than `max_diag_len` and need to be padded. `align` is +// a string specifying how superdiagonals and subdiagonals should be aligned, +// respectively. There are four possible alignments: "RIGHT_LEFT" (default), +// "LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals +// to the right (left-pads the row) and subdiagonals to the left (right-pads the +// row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is +// the opposite alignment. +// If not specified, defaults to "RIGHT_LEFT" +func MatrixDiagPartV3Align(value string) MatrixDiagPartV3Attr { + return func(m optionalAttr) { + m["align"] = value + } +} + +// Returns the batched diagonal part of a batched tensor. +// +// Returns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched +// `input`. +// +// Assume `input` has `r` dimensions `[I, J, ..., L, M, N]`. +// Let `max_diag_len` be the maximum length among all diagonals to be extracted, +// `max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))` +// Let `num_diags` be the number of diagonals to extract, +// `num_diags = k[1] - k[0] + 1`. +// +// If `num_diags == 1`, the output tensor is of rank `r - 1` with shape +// `[I, J, ..., L, max_diag_len]` and values: +// +// ``` +// diagonal[i, j, ..., l, n] +// = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, +// padding_value ; otherwise. +// ``` +// where `y = max(-k[1], 0)`, `x = max(k[1], 0)`. +// +// Otherwise, the output tensor has rank `r` with dimensions +// `[I, J, ..., L, num_diags, max_diag_len]` with values: +// +// ``` +// diagonal[i, j, ..., l, m, n] +// = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, +// padding_value ; otherwise. +// ``` +// where `d = k[1] - m`, `y = max(-d, 0) - offset`, and `x = max(d, 0) - offset`. +// +// `offset` is zero except when the alignment of the diagonal is to the right. +// ``` +// offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT} +// and `d >= 0`) or +// (`align` in {LEFT_RIGHT, RIGHT_RIGHT} +// and `d <= 0`) +// 0 ; otherwise +// ``` +// where `diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))`. +// +// The input must be at least a matrix. +// +// For example: +// +// ``` +// input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4) +// [5, 6, 7, 8], +// [9, 8, 7, 6]], +// [[5, 4, 3, 2], +// [1, 2, 3, 4], +// [5, 6, 7, 8]]]) +// +// # A main diagonal from each batch. +// tf.matrix_diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3) +// [5, 2, 7]] +// +// # A superdiagonal from each batch. +// tf.matrix_diag_part(input, k = 1) +// ==> [[2, 7, 6], # Output shape: (2, 3) +// [4, 3, 8]] +// +// # A band from each batch. +// tf.matrix_diag_part(input, k = (-1, 2)) +// ==> [[[0, 3, 8], # Output shape: (2, 4, 3) +// [2, 7, 6], +// [1, 6, 7], +// [5, 8, 0]], +// [[0, 3, 4], +// [4, 3, 8], +// [5, 2, 7], +// [1, 6, 0]]] +// +// # LEFT_RIGHT alignment. +// tf.matrix_diag_part(input, k = (-1, 2), align="LEFT_RIGHT") +// ==> [[[3, 8, 0], # Output shape: (2, 4, 3) +// [2, 7, 6], +// [1, 6, 7], +// [0, 5, 8]], +// [[3, 4, 0], +// [4, 3, 8], +// [5, 2, 7], +// [0, 1, 6]]] +// +// # max_diag_len can be shorter than the main diagonal. +// tf.matrix_diag_part(input, k = (-2, -1)) +// ==> [[[5, 8], +// [9, 0]], +// [[1, 6], +// [5, 0]]] +// +// # padding_value = 9 +// tf.matrix_diag_part(input, k = (1, 3), padding_value = 9) +// ==> [[[9, 9, 4], # Output shape: (2, 3, 3) +// [9, 3, 8], +// [2, 7, 6]], +// [[9, 9, 2], +// [9, 3, 4], +// [4, 3, 8]]] +// +// ``` +// +// Arguments: +// input: Rank `r` tensor where `r >= 2`. +// k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main +// diagonal, and negative value means subdiagonals. `k` can be a single integer +// (for a single diagonal) or a pair of integers specifying the low and high ends +// of a matrix band. `k[0]` must not be larger than `k[1]`. +// padding_value: The value to fill the area outside the specified diagonal band with. +// Default is 0. +// +// Returns The extracted diagonal(s). +func MatrixDiagPartV3(scope *Scope, input tf.Output, k tf.Output, padding_value tf.Output, optional ...MatrixDiagPartV3Attr) (diagonal tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixDiagPartV3", + Input: []tf.Input{ + input, k, padding_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LearnedUnigramCandidateSamplerAttr is an optional argument to LearnedUnigramCandidateSampler. +type LearnedUnigramCandidateSamplerAttr func(optionalAttr) + +// LearnedUnigramCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func LearnedUnigramCandidateSamplerSeed(value int64) LearnedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// LearnedUnigramCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func LearnedUnigramCandidateSamplerSeed2(value int64) LearnedUnigramCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a learned unigram distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). +// +// Returns: +// sampled_candidates: A vector of length num_sampled, in which each element is +// the ID of a sampled candidate. +// true_expected_count: A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability. +// sampled_expected_count: A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func LearnedUnigramCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LearnedUnigramCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LearnedUnigramCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler. +type LogUniformCandidateSamplerAttr func(optionalAttr) + +// LogUniformCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func LogUniformCandidateSamplerSeed(value int64) LogUniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// LogUniformCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func LogUniformCandidateSamplerSeed2(value int64) LogUniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a log-uniform distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). +// +// Returns: +// sampled_candidates: A vector of length num_sampled, in which each element is +// the ID of a sampled candidate. +// true_expected_count: A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability. +// sampled_expected_count: A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func LogUniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...LogUniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LogUniformCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Selects the k nearest centers for each point. +// +// Rows of points are assumed to be input points. Rows of centers are assumed to be +// the list of candidate centers. For each point, the k centers that have least L2 +// distance to it are computed. +// +// Arguments: +// points: Matrix of shape (n, d). Rows are assumed to be input points. +// centers: Matrix of shape (m, d). Rows are assumed to be centers. +// k: Number of nearest centers to return for each point. If k is larger than m, then +// only m centers are returned. +// +// Returns: +// nearest_center_indices: Matrix of shape (n, min(m, k)). Each row contains the indices of the centers +// closest to the corresponding point, ordered by increasing distance. +// nearest_center_distances: Matrix of shape (n, min(m, k)). Each row contains the squared L2 distance to the +// corresponding center in nearest_center_indices. +func NearestNeighbors(scope *Scope, points tf.Output, centers tf.Output, k tf.Output) (nearest_center_indices tf.Output, nearest_center_distances tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NearestNeighbors", + Input: []tf.Input{ + points, centers, k, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Returns the index of a data point that should be added to the seed set. +// +// Entries in distances are assumed to be squared distances of candidate points to +// the already sampled centers in the seed set. The op constructs one Markov chain +// of the k-MC^2 algorithm and returns the index of one candidate point to be added +// as an additional cluster center. +// +// Arguments: +// distances: Vector with squared distances to the closest previously sampled cluster center +// for each candidate point. +// seed: Scalar. Seed for initializing the random number generator. +// +// Returns Scalar with the index of the sampled point. +func KMC2ChainInitialization(scope *Scope, distances tf.Output, seed tf.Output) (index tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "KMC2ChainInitialization", + Input: []tf.Input{ + distances, seed, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Selects num_to_sample rows of input using the KMeans++ criterion. +// +// Rows of points are assumed to be input points. One row is selected at random. +// Subsequent rows are sampled with probability proportional to the squared L2 +// distance from the nearest row selected thus far till num_to_sample rows have +// been sampled. +// +// Arguments: +// points: Matrix of shape (n, d). Rows are assumed to be input points. +// num_to_sample: Scalar. The number of rows to sample. This value must not be larger than n. +// seed: Scalar. Seed for initializing the random number generator. +// num_retries_per_sample: Scalar. For each row that is sampled, this parameter +// specifies the number of additional points to draw from the current +// distribution before selecting the best. If a negative value is specified, a +// heuristic is used to sample O(log(num_to_sample)) additional points. +// +// Returns Matrix of shape (num_to_sample, d). The sampled rows. +func KmeansPlusPlusInitialization(scope *Scope, points tf.Output, num_to_sample tf.Output, seed tf.Output, num_retries_per_sample tf.Output) (samples tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "KmeansPlusPlusInitialization", + Input: []tf.Input{ + points, num_to_sample, seed, num_retries_per_sample, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AbortAttr is an optional argument to Abort. +type AbortAttr func(optionalAttr) + +// AbortErrorMsg sets the optional error_msg attribute to value. +// +// value: A string which is the message associated with the exception. +// If not specified, defaults to "" +func AbortErrorMsg(value string) AbortAttr { + return func(m optionalAttr) { + m["error_msg"] = value + } +} + +// AbortExitWithoutError sets the optional exit_without_error attribute to value. +// If not specified, defaults to false +func AbortExitWithoutError(value bool) AbortAttr { + return func(m optionalAttr) { + m["exit_without_error"] = value + } +} + +// Raise a exception to abort the process when called. +// +// If exit_without_error is true, the process will exit normally, +// otherwise it will exit with a SIGABORT signal. +// +// Returns nothing but an exception. +// +// Returns the created operation. +func Abort(scope *Scope, optional ...AbortAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Abort", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Makes its input available to the next iteration. +// +// Arguments: +// data: The tensor to be made available to the next iteration. +// +// Returns The same tensor as `data`. +func NextIteration(scope *Scope, data tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NextIteration", + Input: []tf.Input{ + data, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Exits the current frame to its parent frame. +// +// Exit makes its input `data` available to the parent frame. +// +// Arguments: +// data: The tensor to be made available to the parent frame. +// +// Returns The same tensor as `data`. +func Exit(scope *Scope, data tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Exit", + Input: []tf.Input{ + data, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnterAttr is an optional argument to Enter. +type EnterAttr func(optionalAttr) + +// EnterIsConstant sets the optional is_constant attribute to value. +// +// value: If true, the output is constant within the child frame. +// If not specified, defaults to false +func EnterIsConstant(value bool) EnterAttr { + return func(m optionalAttr) { + m["is_constant"] = value + } +} + +// EnterParallelIterations sets the optional parallel_iterations attribute to value. +// +// value: The number of iterations allowed to run in parallel. +// If not specified, defaults to 10 +func EnterParallelIterations(value int64) EnterAttr { + return func(m optionalAttr) { + m["parallel_iterations"] = value + } +} + +// Creates or finds a child frame, and makes `data` available to the child frame. +// +// This op is used together with `Exit` to create loops in the graph. +// The unique `frame_name` is used by the `Executor` to identify frames. If +// `is_constant` is true, `output` is a constant in the child frame; otherwise +// it may be changed in the child frame. At most `parallel_iterations` iterations +// are run in parallel in the child frame. +// +// Arguments: +// data: The tensor to be made available to the child frame. +// frame_name: The name of the child frame. +// +// Returns The same tensor as `data`. +func Enter(scope *Scope, data tf.Output, frame_name string, optional ...EnterAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"frame_name": frame_name} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Enter", + Input: []tf.Input{ + data, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DenseCountSparseOutputAttr is an optional argument to DenseCountSparseOutput. +type DenseCountSparseOutputAttr func(optionalAttr) + +// DenseCountSparseOutputMinlength sets the optional minlength attribute to value. +// +// value: Minimum value to count. Can be set to -1 for no minimum. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func DenseCountSparseOutputMinlength(value int64) DenseCountSparseOutputAttr { + return func(m optionalAttr) { + m["minlength"] = value + } +} + +// DenseCountSparseOutputMaxlength sets the optional maxlength attribute to value. +// +// value: Maximum value to count. Can be set to -1 for no maximum. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func DenseCountSparseOutputMaxlength(value int64) DenseCountSparseOutputAttr { + return func(m optionalAttr) { + m["maxlength"] = value + } +} + +// Performs sparse-output bin counting for a tf.tensor input. +// +// Counts the number of times each value occurs in the input. +// +// Arguments: +// values: Tensor containing data to count. +// weights: A Tensor of the same shape as indices containing per-index weight values. May +// also be the empty tensor if no weights are used. +// binary_output: Whether to output the number of occurrences of each value or 1. +// +// Returns: +// output_indices: Indices tensor for the resulting sparse tensor object. +// output_values: Values tensor for the resulting sparse tensor object. +// output_dense_shape: Shape tensor for the resulting sparse tensor object. +func DenseCountSparseOutput(scope *Scope, values tf.Output, weights tf.Output, binary_output bool, optional ...DenseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"binary_output": binary_output} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DenseCountSparseOutput", + Input: []tf.Input{ + values, weights, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder. +type CTCBeamSearchDecoderAttr func(optionalAttr) + +// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value. +// +// value: If true, merge repeated classes in output. +// If not specified, defaults to true +func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr { + return func(m optionalAttr) { + m["merge_repeated"] = value + } +} + +// Performs beam search decoding on the logits given in input. +// +// A note about the attribute merge_repeated: For the beam search decoder, +// this means that if consecutive entries in a beam are the same, only +// the first of these is emitted. That is, when the top path is "A B B B B", +// "A B" is returned if merge_repeated = True but "A B B B B" is +// returned if merge_repeated = False. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. +// sequence_length: A vector containing sequence lengths, size `(batch)`. +// beam_width: A scalar >= 0 (beam search beam width). +// top_paths: A scalar >= 0, <= beam_width (controls output size). +// +// Returns: +// decoded_indices: A list (length: top_paths) of indices matrices. Matrix j, +// size `(total_decoded_outputs[j] x 2)`, has indices of a +// `SparseTensor`. The rows store: [batch, time]. +// decoded_values: A list (length: top_paths) of values vectors. Vector j, +// size `(length total_decoded_outputs[j])`, has the values of a +// `SparseTensor`. The vector stores the decoded classes for beam j. +// decoded_shape: A list (length: top_paths) of shape vector. Vector j, +// size `(2)`, stores the shape of the decoded `SparseTensor[j]`. +// Its values are: `[batch_size, max_decoded_length[j]]`. +// log_probability: A matrix, shaped: `(batch_size x top_paths)`. The +// sequence log-probabilities. +func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCBeamSearchDecoder", + Input: []tf.Input{ + inputs, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + log_probability = op.Output(idx) + return decoded_indices, decoded_values, decoded_shape, log_probability +} + +// CTCGreedyDecoderAttr is an optional argument to CTCGreedyDecoder. +type CTCGreedyDecoderAttr func(optionalAttr) + +// CTCGreedyDecoderMergeRepeated sets the optional merge_repeated attribute to value. +// +// value: If True, merge repeated classes in output. +// If not specified, defaults to false +func CTCGreedyDecoderMergeRepeated(value bool) CTCGreedyDecoderAttr { + return func(m optionalAttr) { + m["merge_repeated"] = value + } +} + +// Performs greedy decoding on the logits given in inputs. +// +// A note about the attribute merge_repeated: if enabled, when +// consecutive logits' maximum indices are the same, only the first of +// these is emitted. Labeling the blank '*', the sequence "A B B * B B" +// becomes "A B B" if merge_repeated = True and "A B B B B" if +// merge_repeated = False. +// +// Regardless of the value of merge_repeated, if the maximum index of a given +// time and batch corresponds to the blank, index `(num_classes - 1)`, no new +// element is emitted. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. +// sequence_length: A vector containing sequence lengths, size `(batch_size)`. +// +// Returns: +// decoded_indices: Indices matrix, size `(total_decoded_outputs x 2)`, +// of a `SparseTensor`. The rows store: [batch, time]. +// decoded_values: Values vector, size: `(total_decoded_outputs)`, +// of a `SparseTensor`. The vector stores the decoded classes. +// decoded_shape: Shape vector, size `(2)`, of the decoded SparseTensor. +// Values are: `[batch_size, max_decoded_length]`. +// log_probability: Matrix, size `(batch_size x 1)`, containing sequence +// log-probabilities. +func CTCGreedyDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, optional ...CTCGreedyDecoderAttr) (decoded_indices tf.Output, decoded_values tf.Output, decoded_shape tf.Output, log_probability tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCGreedyDecoder", + Input: []tf.Input{ + inputs, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// CTCLossAttr is an optional argument to CTCLoss. +type CTCLossAttr func(optionalAttr) + +// CTCLossPreprocessCollapseRepeated sets the optional preprocess_collapse_repeated attribute to value. +// +// value: Scalar, if true then repeated labels are +// collapsed prior to the CTC calculation. +// If not specified, defaults to false +func CTCLossPreprocessCollapseRepeated(value bool) CTCLossAttr { + return func(m optionalAttr) { + m["preprocess_collapse_repeated"] = value + } +} + +// CTCLossCtcMergeRepeated sets the optional ctc_merge_repeated attribute to value. +// +// value: Scalar. If set to false, *during* CTC calculation +// repeated non-blank labels will not be merged and are interpreted as +// individual labels. This is a simplified version of CTC. +// If not specified, defaults to true +func CTCLossCtcMergeRepeated(value bool) CTCLossAttr { + return func(m optionalAttr) { + m["ctc_merge_repeated"] = value + } +} + +// CTCLossIgnoreLongerOutputsThanInputs sets the optional ignore_longer_outputs_than_inputs attribute to value. +// +// value: Scalar. If set to true, during CTC +// calculation, items that have longer output sequences than input sequences +// are skipped: they don't contribute to the loss term and have zero-gradient. +// If not specified, defaults to false +func CTCLossIgnoreLongerOutputsThanInputs(value bool) CTCLossAttr { + return func(m optionalAttr) { + m["ignore_longer_outputs_than_inputs"] = value + } +} + +// Calculates the CTC Loss (log probability) for each batch entry. Also calculates +// +// the gradient. This class performs the softmax operation for you, so inputs +// should be e.g. linear projections of outputs by an LSTM. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. +// labels_indices: The indices of a `SparseTensor`. +// `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for +// `(batch b, time t)`. +// labels_values: The values (labels) associated with the given batch and time. +// sequence_length: A vector containing sequence lengths (batch). +// +// Returns: +// loss: A vector (batch) containing log-probabilities. +// gradient: The gradient of `loss`. 3-D, shape: +// `(max_time x batch_size x num_classes)`. +func CTCLoss(scope *Scope, inputs tf.Output, labels_indices tf.Output, labels_values tf.Output, sequence_length tf.Output, optional ...CTCLossAttr) (loss tf.Output, gradient tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCLoss", + Input: []tf.Input{ + inputs, labels_indices, labels_values, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// CudnnRNNCanonicalToParamsAttr is an optional argument to CudnnRNNCanonicalToParams. +type CudnnRNNCanonicalToParamsAttr func(optionalAttr) + +// CudnnRNNCanonicalToParamsRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNCanonicalToParamsRnnMode(value string) CudnnRNNCanonicalToParamsAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNCanonicalToParamsInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNCanonicalToParamsInputMode(value string) CudnnRNNCanonicalToParamsAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNCanonicalToParamsDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNCanonicalToParamsDirection(value string) CudnnRNNCanonicalToParamsAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNCanonicalToParamsDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsDropout(value float32) CudnnRNNCanonicalToParamsAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNCanonicalToParamsSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsSeed(value int64) CudnnRNNCanonicalToParamsAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNCanonicalToParamsSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsSeed2(value int64) CudnnRNNCanonicalToParamsAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Converts CudnnRNN params from canonical form to usable form. +// +// Writes a set of weights into the opaque params buffer so they can be used in +// upcoming training or inferences. +// +// Note that the params buffer may not be compatible across different GPUs. So any +// save and restoration should be converted to and from the canonical weights and +// biases. +// +// num_layers: Specifies the number of layers in the RNN model. +// num_units: Specifies the size of the hidden state. +// input_size: Specifies the size of the input state. +// weights: the canonical form of weights that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// biases: the canonical form of biases that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// num_params: number of parameter sets for all layers. +// Each layer may contain multiple parameter sets, with each set consisting of +// a weight matrix and a bias vector. +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// The actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. +// dir = (direction == bidirectional) ? 2 : 1 +// dropout: dropout probability. When set to 0., dropout is disabled. +// seed: the 1st part of a seed to initialize dropout. +// seed2: the 2nd part of a seed to initialize dropout. +func CudnnRNNCanonicalToParams(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, weights []tf.Output, biases []tf.Output, optional ...CudnnRNNCanonicalToParamsAttr) (params tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNCanonicalToParams", + Input: []tf.Input{ + num_layers, num_units, input_size, tf.OutputList(weights), tf.OutputList(biases), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNParamsToCanonicalV2Attr is an optional argument to CudnnRNNParamsToCanonicalV2. +type CudnnRNNParamsToCanonicalV2Attr func(optionalAttr) + +// CudnnRNNParamsToCanonicalV2RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNParamsToCanonicalV2RnnMode(value string) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNParamsToCanonicalV2InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNParamsToCanonicalV2InputMode(value string) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNParamsToCanonicalV2Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNParamsToCanonicalV2Direction(value string) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNParamsToCanonicalV2Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalV2Dropout(value float32) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNParamsToCanonicalV2Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalV2Seed(value int64) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNParamsToCanonicalV2Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalV2Seed2(value int64) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNParamsToCanonicalV2NumProj sets the optional num_proj attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalV2NumProj(value int64) CudnnRNNParamsToCanonicalV2Attr { + return func(m optionalAttr) { + m["num_proj"] = value + } +} + +// Retrieves CudnnRNN params in canonical form. It supports the projection in LSTM. +// +// Retrieves a set of weights from the opaque params buffer that can be saved and +// restored in a way compatible with future runs. +// +// Note that the params buffer may not be compatible across different GPUs. So any +// save and restoration should be converted to and from the canonical weights and +// biases. +// +// num_layers: Specifies the number of layers in the RNN model. +// num_units: Specifies the size of the hidden state. +// input_size: Specifies the size of the input state. +// num_params_weights: number of weight parameter matrix for all layers. +// num_params_biases: number of bias parameter vector for all layers. +// weights: the canonical form of weights that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// biases: the canonical form of biases that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// The actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. +// dir = (direction == bidirectional) ? 2 : 1 +// dropout: dropout probability. When set to 0., dropout is disabled. +// seed: the 1st part of a seed to initialize dropout. +// seed2: the 2nd part of a seed to initialize dropout. +// num_proj: The output dimensionality for the projection matrices. If None or 0, +// no projection is performed. +func CudnnRNNParamsToCanonicalV2(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params_weights int64, num_params_biases int64, optional ...CudnnRNNParamsToCanonicalV2Attr) (weights []tf.Output, biases []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_params_weights": num_params_weights, "num_params_biases": num_params_biases} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNParamsToCanonicalV2", + Input: []tf.Input{ + num_layers, num_units, input_size, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil { + scope.UpdateErr("CudnnRNNParamsToCanonicalV2", err) + return + } + if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil { + scope.UpdateErr("CudnnRNNParamsToCanonicalV2", err) + return + } + return weights, biases +} + +// Returns the diagonal part of the tensor. +// +// This operation returns a tensor with the `diagonal` part +// of the `input`. The `diagonal` part is computed as follows: +// +// Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a +// tensor of rank `k` with dimensions `[D1,..., Dk]` where: +// +// `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`. +// +// For example: +// +// ``` +// # 'input' is [[1, 0, 0, 0] +// [0, 2, 0, 0] +// [0, 0, 3, 0] +// [0, 0, 0, 4]] +// +// tf.diag_part(input) ==> [1, 2, 3, 4] +// ``` +// +// Arguments: +// input: Rank k tensor where k is even and not zero. +// +// Returns The extracted diagonal. +func DiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DiagPart", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNParamsToCanonicalAttr is an optional argument to CudnnRNNParamsToCanonical. +type CudnnRNNParamsToCanonicalAttr func(optionalAttr) + +// CudnnRNNParamsToCanonicalRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNParamsToCanonicalRnnMode(value string) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNParamsToCanonicalInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNParamsToCanonicalInputMode(value string) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNParamsToCanonicalDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNParamsToCanonicalDirection(value string) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNParamsToCanonicalDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalDropout(value float32) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNParamsToCanonicalSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalSeed(value int64) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNParamsToCanonicalSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Retrieves CudnnRNN params in canonical form. +// +// Retrieves a set of weights from the opaque params buffer that can be saved and +// restored in a way compatible with future runs. +// +// Note that the params buffer may not be compatible across different GPUs. So any +// save and restoration should be converted to and from the canonical weights and +// biases. +// +// num_layers: Specifies the number of layers in the RNN model. +// num_units: Specifies the size of the hidden state. +// input_size: Specifies the size of the input state. +// num_params: number of parameter sets for all layers. +// Each layer may contain multiple parameter sets, with each set consisting of +// a weight matrix and a bias vector. +// weights: the canonical form of weights that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// biases: the canonical form of biases that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// The actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. +// dir = (direction == bidirectional) ? 2 : 1 +// dropout: dropout probability. When set to 0., dropout is disabled. +// seed: the 1st part of a seed to initialize dropout. +// seed2: the 2nd part of a seed to initialize dropout. +func CudnnRNNParamsToCanonical(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params int64, optional ...CudnnRNNParamsToCanonicalAttr) (weights []tf.Output, biases []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_params": num_params} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNParamsToCanonical", + Input: []tf.Input{ + num_layers, num_units, input_size, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil { + scope.UpdateErr("CudnnRNNParamsToCanonical", err) + return + } + if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil { + scope.UpdateErr("CudnnRNNParamsToCanonical", err) + return + } + return weights, biases +} + +// CudnnRNNBackpropV3Attr is an optional argument to CudnnRNNBackpropV3. +type CudnnRNNBackpropV3Attr func(optionalAttr) + +// CudnnRNNBackpropV3RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNBackpropV3RnnMode(value string) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNBackpropV3InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNBackpropV3InputMode(value string) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNBackpropV3Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNBackpropV3Direction(value string) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNBackpropV3Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3Dropout(value float32) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNBackpropV3Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3Seed(value int64) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNBackpropV3Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3Seed2(value int64) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNBackpropV3NumProj sets the optional num_proj attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV3NumProj(value int64) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["num_proj"] = value + } +} + +// CudnnRNNBackpropV3TimeMajor sets the optional time_major attribute to value. +// If not specified, defaults to true +func CudnnRNNBackpropV3TimeMajor(value bool) CudnnRNNBackpropV3Attr { + return func(m optionalAttr) { + m["time_major"] = value + } +} + +// Backprop step of CudnnRNNV3. +// +// Compute the backprop of both data and weights in a RNN. Takes an extra +// "sequence_lengths" input than CudnnRNNBackprop. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: If time_major is true, this is a 3-D tensor with the shape of +// [seq_length, batch_size, input_size]. If time_major is false, the shape is +// [batch_size, seq_length, input_size]. +// input_h: If time_major is true, this is a 3-D tensor with the shape of +// [num_layer * dir, batch_size, num_units]. If time_major is false, the shape +// is [batch_size, num_layer * dir, num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// sequence_lengths: a vector of lengths of each input sequence. +// output: If time_major is true, this is a 3-D tensor with the shape of +// [seq_length, batch_size, dir * num_units]. If time_major is false, the +// shape is [batch_size, seq_length, dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// output_backprop: A 3-D tensor with the same shape as output in the forward pass. +// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward +// pass. +// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward +// pass. +// time_major: Indicates whether the input/output format is time major or batch +// major. +// reserve_space: The same reserve_space produced in the forward operation. +// input_backprop: The backprop to input in the forward pass. Has the same shape +// as input. +// input_h_backprop: The backprop to input_h in the forward pass. Has the same +// shape as input_h. +// input_c_backprop: The backprop to input_c in the forward pass. Has the same +// shape as input_c. +// params_backprop: The backprop to the params buffer in the forward pass. Has the +// same shape as params. +func CudnnRNNBackpropV3(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, sequence_lengths tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, host_reserved tf.Output, optional ...CudnnRNNBackpropV3Attr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNBackpropV3", + Input: []tf.Input{ + input, input_h, input_c, params, sequence_lengths, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// CudnnRNNBackpropAttr is an optional argument to CudnnRNNBackprop. +type CudnnRNNBackpropAttr func(optionalAttr) + +// CudnnRNNBackpropRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNBackpropRnnMode(value string) CudnnRNNBackpropAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNBackpropInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNBackpropInputMode(value string) CudnnRNNBackpropAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNBackpropDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNBackpropDirection(value string) CudnnRNNBackpropAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNBackpropDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropDropout(value float32) CudnnRNNBackpropAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNBackpropSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropSeed(value int64) CudnnRNNBackpropAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNBackpropSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropSeed2(value int64) CudnnRNNBackpropAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Backprop step of CudnnRNN. +// +// Compute the backprop of both data and weights in a RNN. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// output_backprop: A 3-D tensor with the same shape as output in the forward pass. +// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward +// pass. +// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward +// pass. +// reserve_space: The same reserve_space produced in for forward operation. +// input_backprop: The backprop to input in the forward pass. Has the same shape +// as input. +// input_h_backprop: The backprop to input_h in the forward pass. Has the same +// shape as input_h. +// input_c_backprop: The backprop to input_c in the forward pass. Has the same +// shape as input_c. +// params_backprop: The backprop to the params buffer in the forward pass. Has the +// same shape as params. +func CudnnRNNBackprop(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, optional ...CudnnRNNBackpropAttr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNBackprop", + Input: []tf.Input{ + input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// CudnnRNNV3Attr is an optional argument to CudnnRNNV3. +type CudnnRNNV3Attr func(optionalAttr) + +// CudnnRNNV3RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNV3RnnMode(value string) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNV3InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNV3InputMode(value string) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNV3Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNV3Direction(value string) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNV3Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV3Dropout(value float32) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNV3Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV3Seed(value int64) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNV3Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV3Seed2(value int64) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNV3NumProj sets the optional num_proj attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV3NumProj(value int64) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["num_proj"] = value + } +} + +// CudnnRNNV3IsTraining sets the optional is_training attribute to value. +// If not specified, defaults to true +func CudnnRNNV3IsTraining(value bool) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// CudnnRNNV3TimeMajor sets the optional time_major attribute to value. +// If not specified, defaults to true +func CudnnRNNV3TimeMajor(value bool) CudnnRNNV3Attr { + return func(m optionalAttr) { + m["time_major"] = value + } +} + +// A RNN backed by cuDNN. +// +// Computes the RNN from the input and initial states, with respect to the params +// buffer. Accepts one extra input "sequence_lengths" than CudnnRNN. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: If time_major is true, this is a 3-D tensor with the shape of +// [seq_length, batch_size, input_size]. If time_major is false, the shape is +// [batch_size, seq_length, input_size]. +// input_h: If time_major is true, this is a 3-D tensor with the shape of +// [num_layer * dir, batch_size, num_units]. If time_major is false, the shape +// is [batch_size, num_layer * dir, num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// sequence_lengths: a vector of lengths of each input sequence. +// output: If time_major is true, this is a 3-D tensor with the shape of +// [seq_length, batch_size, dir * num_units]. If time_major is false, the +// shape is [batch_size, seq_length, dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// is_training: Indicates whether this operation is used for inference or +// training. +// time_major: Indicates whether the input/output format is time major or batch +// major. +// reserve_space: An opaque tensor that can be used in backprop calculation. It +// is only produced if is_training is true. +func CudnnRNNV3(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, sequence_lengths tf.Output, optional ...CudnnRNNV3Attr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output, host_reserved tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNV3", + Input: []tf.Input{ + input, input_h, input_c, params, sequence_lengths, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Pads a tensor with zeros. +// +// This operation pads a `input` with zeros according to the `paddings` you +// specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the +// rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many zeros to add before the contents of `input` in that dimension, and +// `paddings[D, 1]` indicates how many zeros to add after the contents of `input` +// in that dimension. +// +// The padded size of each dimension D of the output is: +// +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 1], [2, 2]] +// # 'paddings' is [[1, 1], [2, 2]] +// # rank of 't' is 2 +// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] +// [0, 0, 1, 1, 0, 0] +// [0, 0, 2, 2, 0, 0] +// [0, 0, 0, 0, 0, 0]] +// ``` +// +func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Pad", + Input: []tf.Input{ + input, paddings, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNV2Attr is an optional argument to CudnnRNNV2. +type CudnnRNNV2Attr func(optionalAttr) + +// CudnnRNNV2RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNV2RnnMode(value string) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNV2InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNV2InputMode(value string) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNV2Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNV2Direction(value string) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNV2Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV2Dropout(value float32) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNV2Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV2Seed(value int64) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNV2Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV2Seed2(value int64) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNV2IsTraining sets the optional is_training attribute to value. +// If not specified, defaults to true +func CudnnRNNV2IsTraining(value bool) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// A RNN backed by cuDNN. +// +// Computes the RNN from the input and initial states, with respect to the params +// buffer. Produces one extra output "host_reserved" than CudnnRNN. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// is_training: Indicates whether this operation is used for inference or +// training. +// reserve_space: An opaque tensor that can be used in backprop calculation. It +// is only produced if is_training is true. +// host_reserved: An opaque tensor that can be used in backprop calculation. It is +// only produced if is_training is true. It is output on host memory rather than +// device memory. +func CudnnRNNV2(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNV2Attr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output, host_reserved tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNV2", + Input: []tf.Input{ + input, input_h, input_c, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// CudnnRNNParamsSizeAttr is an optional argument to CudnnRNNParamsSize. +type CudnnRNNParamsSizeAttr func(optionalAttr) + +// CudnnRNNParamsSizeRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNParamsSizeRnnMode(value string) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNParamsSizeInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNParamsSizeInputMode(value string) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNParamsSizeDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNParamsSizeDirection(value string) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNParamsSizeDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsSizeDropout(value float32) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNParamsSizeSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsSizeSeed(value int64) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNParamsSizeSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsSizeSeed2(value int64) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNParamsSizeNumProj sets the optional num_proj attribute to value. +// If not specified, defaults to 0 +func CudnnRNNParamsSizeNumProj(value int64) CudnnRNNParamsSizeAttr { + return func(m optionalAttr) { + m["num_proj"] = value + } +} + +// Computes size of weights that can be used by a Cudnn RNN model. +// +// Return the params size that can be used by the Cudnn RNN model. Subsequent +// weight allocation and initialization should use this size. +// +// num_layers: Specifies the number of layers in the RNN model. +// num_units: Specifies the size of the hidden state. +// input_size: Specifies the size of the input state. +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// The actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. +// dir = (direction == bidirectional) ? 2 : 1 +// dropout: dropout probability. When set to 0., dropout is disabled. +// seed: the 1st part of a seed to initialize dropout. +// seed2: the 2nd part of a seed to initialize dropout. +// params_size: The size of the params buffer that should be allocated and +// initialized for this RNN model. Note that this params buffer may not be +// compatible across GPUs. Please use CudnnRNNParamsWeights and +// CudnnRNNParamsBiases to save and restore them in a way that is compatible +// across different runs. +func CudnnRNNParamsSize(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, T tf.DataType, S tf.DataType, optional ...CudnnRNNParamsSizeAttr) (params_size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T, "S": S} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNParamsSize", + Input: []tf.Input{ + num_layers, num_units, input_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RecordInputAttr is an optional argument to RecordInput. +type RecordInputAttr func(optionalAttr) + +// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value. +// +// value: Random seeds used to produce randomized records. +// If not specified, defaults to 301 +func RecordInputFileRandomSeed(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["file_random_seed"] = value + } +} + +// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value. +// +// value: Shifts the list of files after the list is randomly +// shuffled. +// If not specified, defaults to 0 +func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr { + return func(m optionalAttr) { + m["file_shuffle_shift_ratio"] = value + } +} + +// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value. +// +// value: The randomization shuffling buffer. +// If not specified, defaults to 10000 +func RecordInputFileBufferSize(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["file_buffer_size"] = value + } +} + +// RecordInputFileParallelism sets the optional file_parallelism attribute to value. +// +// value: How many sstables are opened and concurrently iterated over. +// If not specified, defaults to 16 +func RecordInputFileParallelism(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["file_parallelism"] = value + } +} + +// RecordInputBatchSize sets the optional batch_size attribute to value. +// +// value: The batch size. +// If not specified, defaults to 32 +func RecordInputBatchSize(value int64) RecordInputAttr { + return func(m optionalAttr) { + m["batch_size"] = value + } +} + +// RecordInputCompressionType sets the optional compression_type attribute to value. +// +// value: The type of compression for the file. Currently ZLIB and +// GZIP are supported. Defaults to none. +// If not specified, defaults to "" +func RecordInputCompressionType(value string) RecordInputAttr { + return func(m optionalAttr) { + m["compression_type"] = value + } +} + +// Emits randomized records. +// +// Arguments: +// file_pattern: Glob pattern for the data files. +// +// Returns A tensor of shape [batch_size]. +func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"file_pattern": file_pattern} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RecordInput", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapIncompleteSizeAttr is an optional argument to OrderedMapIncompleteSize. +type OrderedMapIncompleteSizeAttr func(optionalAttr) + +// OrderedMapIncompleteSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapIncompleteSizeCapacity(value int64) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapIncompleteSizeMemoryLimit(value int64) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapIncompleteSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapIncompleteSizeContainer(value string) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapIncompleteSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapIncompleteSizeSharedName(value string) OrderedMapIncompleteSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of incomplete elements in the underlying container. +func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapIncompleteSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapIncompleteSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapSizeAttr is an optional argument to OrderedMapSize. +type OrderedMapSizeAttr func(optionalAttr) + +// OrderedMapSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapSizeCapacity(value int64) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapSizeMemoryLimit(value int64) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapSizeContainer(value string) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapSizeSharedName(value string) OrderedMapSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func OrderedMapSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapUnstageNoKeyAttr is an optional argument to OrderedMapUnstageNoKey. +type OrderedMapUnstageNoKeyAttr func(optionalAttr) + +// OrderedMapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapUnstageNoKeyCapacity(value int64) OrderedMapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapUnstageNoKeyMemoryLimit(value int64) OrderedMapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapUnstageNoKeyContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapUnstageNoKeyContainer(value string) OrderedMapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapUnstageNoKeySharedName(value string) OrderedMapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns the (key, value) element with the smallest +// +// key from the underlying container. If the underlying container +// does not contain elements, the op will block until it does. +func OrderedMapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapUnstageNoKey", + Input: []tf.Input{ + indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + key = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("OrderedMapUnstageNoKey", err) + return + } + return key, values +} + +// OrderedMapPeekAttr is an optional argument to OrderedMapPeek. +type OrderedMapPeekAttr func(optionalAttr) + +// OrderedMapPeekCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapPeekCapacity(value int64) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapPeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapPeekMemoryLimit(value int64) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapPeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapPeekContainer(value string) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapPeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapPeekSharedName(value string) OrderedMapPeekAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op peeks at the values at the specified key. If the +// +// underlying container does not contain this key +// this op will block until it does. This Op is optimized for +// performance. +func OrderedMapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapPeekAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapPeek", + Input: []tf.Input{ + key, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("OrderedMapPeek", err) + return + } + return values +} + +// MapIncompleteSizeAttr is an optional argument to MapIncompleteSize. +type MapIncompleteSizeAttr func(optionalAttr) + +// MapIncompleteSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapIncompleteSizeCapacity(value int64) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapIncompleteSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapIncompleteSizeMemoryLimit(value int64) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapIncompleteSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapIncompleteSizeContainer(value string) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapIncompleteSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapIncompleteSizeSharedName(value string) MapIncompleteSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of incomplete elements in the underlying container. +func MapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...MapIncompleteSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapIncompleteSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MapSizeAttr is an optional argument to MapSize. +type MapSizeAttr func(optionalAttr) + +// MapSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapSizeCapacity(value int64) MapSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapSizeMemoryLimit(value int64) MapSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapSizeContainer(value string) MapSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapSizeSharedName(value string) MapSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func MapSize(scope *Scope, dtypes []tf.DataType, optional ...MapSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey. +type MapUnstageNoKeyAttr func(optionalAttr) + +// MapUnstageNoKeyCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapUnstageNoKeyContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapUnstageNoKeySharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns a random (key, value) +// +// from the underlying container. If the underlying container +// does not contain elements, the op will block until it does. +func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapUnstageNoKey", + Input: []tf.Input{ + indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + key = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapUnstageNoKey", err) + return + } + return key, values +} + +// UnbatchAttr is an optional argument to Unbatch. +type UnbatchAttr func(optionalAttr) + +// UnbatchContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func UnbatchContainer(value string) UnbatchAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// UnbatchSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func UnbatchSharedName(value string) UnbatchAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Reverses the operation of Batch for a single output Tensor. +// +// An instance of Unbatch either receives an empty batched_tensor, in which case it +// asynchronously waits until the values become available from a concurrently +// running instance of Unbatch with the same container and shared_name, or receives +// a non-empty batched_tensor in which case it finalizes all other concurrently +// running instances and outputs its own element from the batch. +// +// batched_tensor: The possibly transformed output of Batch. The size of the first +// dimension should remain unchanged by the transformations for the operation to +// work. +// batch_index: The matching batch_index obtained from Batch. +// id: The id scalar emitted by Batch. +// unbatched_tensor: The Tensor corresponding to this execution. +// timeout_micros: Maximum amount of time (in microseconds) to wait to receive the +// batched input tensor associated with a given invocation of the op. +// container: Container to control resource sharing. +// shared_name: Instances of Unbatch with the same container and shared_name are +// assumed to possibly belong to the same batch. If left empty, the op name will +// be used as the shared name. +func Unbatch(scope *Scope, batched_tensor tf.Output, batch_index tf.Output, id tf.Output, timeout_micros int64, optional ...UnbatchAttr) (unbatched_tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"timeout_micros": timeout_micros} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Unbatch", + Input: []tf.Input{ + batched_tensor, batch_index, id, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MapUnstageAttr is an optional argument to MapUnstage. +type MapUnstageAttr func(optionalAttr) + +// MapUnstageCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapUnstageCapacity(value int64) MapUnstageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapUnstageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapUnstageMemoryLimit(value int64) MapUnstageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapUnstageContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapUnstageContainer(value string) MapUnstageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapUnstageSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapUnstageSharedName(value string) MapUnstageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns the values associated with the key +// +// from the underlying container. If the underlying container +// does not contain this key, the op will block until it does. +func MapUnstage(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapUnstage", + Input: []tf.Input{ + key, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapUnstage", err) + return + } + return values +} + +// StageSizeAttr is an optional argument to StageSize. +type StageSizeAttr func(optionalAttr) + +// StageSizeCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeCapacity(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageSizeMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageSizeMemoryLimit(value int64) StageSizeAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageSizeContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageSizeContainer(value string) StageSizeAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageSizeSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageSizeSharedName(value string) StageSizeAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op returns the number of elements in the underlying container. +func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StageSize", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StagePeekAttr is an optional argument to StagePeek. +type StagePeekAttr func(optionalAttr) + +// StagePeekCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StagePeekCapacity(value int64) StagePeekAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StagePeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StagePeekMemoryLimit(value int64) StagePeekAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StagePeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StagePeekContainer(value string) StagePeekAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StagePeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StagePeekSharedName(value string) StagePeekAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op peeks at the values at the specified index. If the +// +// underlying container does not contain sufficient elements +// this op will block until it does. This Op is optimized for +// performance. +func StagePeek(scope *Scope, index tf.Output, dtypes []tf.DataType, optional ...StagePeekAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StagePeek", + Input: []tf.Input{ + index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("StagePeek", err) + return + } + return values +} + +// UnstageAttr is an optional argument to Unstage. +type UnstageAttr func(optionalAttr) + +// UnstageCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func UnstageCapacity(value int64) UnstageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// UnstageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func UnstageMemoryLimit(value int64) UnstageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// UnstageContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func UnstageContainer(value string) UnstageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// UnstageSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func UnstageSharedName(value string) UnstageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op is similar to a lightweight Dequeue. +// +// The basic functionality is similar to dequeue with many fewer +// capabilities and options. This Op is optimized for performance. +func Unstage(scope *Scope, dtypes []tf.DataType, optional ...UnstageAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Unstage", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("Unstage", err) + return + } + return values +} + +// StageAttr is an optional argument to Stage. +type StageAttr func(optionalAttr) + +// StageCapacity sets the optional capacity attribute to value. +// +// value: Maximum number of elements in the Staging Area. If > 0, inserts +// on the container will block when the capacity is reached. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageCapacity(value int64) StageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageMemoryLimit sets the optional memory_limit attribute to value. +// +// value: The maximum number of bytes allowed for Tensors in the Staging Area. +// If > 0, inserts will block until sufficient space is available. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageMemoryLimit(value int64) StageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageContainer sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. Otherwise, +// a default container is used. +// If not specified, defaults to "" +func StageContainer(value string) StageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageSharedName sets the optional shared_name attribute to value. +// +// value: It is necessary to match this name to the matching Unstage Op. +// If not specified, defaults to "" +func StageSharedName(value string) StageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Stage values similar to a lightweight Enqueue. +// +// The basic functionality of this Op is similar to a queue with many +// fewer capabilities and options. This Op is optimized for performance. +// +// Arguments: +// values: a list of tensors +// dtypes A list of data types that inserted values should adhere to. +// +// Returns the created operation. +func Stage(scope *Scope, values []tf.Output, optional ...StageAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Stage", + Input: []tf.Input{ + tf.OutputList(values), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Delete the tensor specified by its handle in the session. +// +// Arguments: +// handle: The handle for a tensor stored in the session state. +// +// Returns the created operation. +func DeleteSessionTensor(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeleteSessionTensor", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// Store the input tensor in the state of the current session. +// +// Arguments: +// value: The tensor to be stored. +// +// Returns The handle for the tensor stored in the session state, represented +// as a ResourceHandle object. +func GetSessionHandleV2(scope *Scope, value tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GetSessionHandleV2", + Input: []tf.Input{ + value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Store the input tensor in the state of the current session. +// +// Arguments: +// value: The tensor to be stored. +// +// Returns The handle for the tensor stored in the session state, represented +// as a string. +func GetSessionHandle(scope *Scope, value tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GetSessionHandle", + Input: []tf.Input{ + value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Copy a tensor setting everything outside a central band in each innermost matrix to zero. +// +// The `band` part is computed as follows: +// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a +// tensor with the same shape where +// +// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. +// +// The indicator function +// +// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && +// (num_upper < 0 || (n-m) <= num_upper)`. +// +// For example: +// +// ``` +// # if 'input' is [[ 0, 1, 2, 3] +// [-1, 0, 1, 2] +// [-2, -1, 0, 1] +// [-3, -2, -1, 0]], +// +// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] +// [-1, 0, 1, 2] +// [ 0, -1, 0, 1] +// [ 0, 0, -1, 0]], +// +// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] +// [-1, 0, 1, 0] +// [-2, -1, 0, 1] +// [ 0, -2, -1, 0]] +// ``` +// +// Useful special cases: +// +// ``` +// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. +// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. +// tf.matrix_band_part(input, 0, 0) ==> Diagonal. +// ``` +// +// Arguments: +// input: Rank `k` tensor. +// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire +// lower triangle. +// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep +// entire upper triangle. +// +// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor. +func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixBandPart", + Input: []tf.Input{ + input, num_lower, num_upper, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ListDiffAttr is an optional argument to ListDiff. +type ListDiffAttr func(optionalAttr) + +// ListDiffOutIdx sets the optional out_idx attribute to value. +// If not specified, defaults to DT_INT32 +func ListDiffOutIdx(value tf.DataType) ListDiffAttr { + return func(m optionalAttr) { + m["out_idx"] = value + } +} + +// Computes the difference between two lists of numbers or strings. +// +// Given a list `x` and a list `y`, this operation returns a list `out` that +// represents all values that are in `x` but not in `y`. The returned list `out` +// is sorted in the same order that the numbers appear in `x` (duplicates are +// preserved). This operation also returns a list `idx` that represents the +// position of each `out` element in `x`. In other words: +// +// `out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]` +// +// For example, given this input: +// +// ``` +// x = [1, 2, 3, 4, 5, 6] +// y = [1, 3, 5] +// ``` +// +// This operation would return: +// +// ``` +// out ==> [2, 4, 6] +// idx ==> [1, 3, 5] +// ``` +// +// Arguments: +// x: 1-D. Values to keep. +// y: 1-D. Values to remove. +// +// Returns: +// out: 1-D. Values present in `x` but not in `y`. +// idx: 1-D. Positions of `x` values preserved in `out`. +func ListDiff(scope *Scope, x tf.Output, y tf.Output, optional ...ListDiffAttr) (out tf.Output, idx tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ListDiff", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Deprecated. Use TensorArrayScatterV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayScatterV3 +func TensorArrayScatterV2(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayScatterV2", + Input: []tf.Input{ + handle, indices, value, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayReadV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayReadV3 +func TensorArrayReadV2(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "TensorArrayReadV2", + Input: []tf.Input{ + handle, index, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayGradV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayGradV3 +func TensorArrayGradV2(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"source": source} + opspec := tf.OpSpec{ + Type: "TensorArrayGradV2", + Input: []tf.Input{ + handle, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorArrayV2Attr is an optional argument to TensorArrayV2. +type TensorArrayV2Attr func(optionalAttr) + +// TensorArrayV2ElementShape sets the optional element_shape attribute to value. +// If not specified, defaults to +func TensorArrayV2ElementShape(value tf.Shape) TensorArrayV2Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// TensorArrayV2DynamicSize sets the optional dynamic_size attribute to value. +// If not specified, defaults to false +func TensorArrayV2DynamicSize(value bool) TensorArrayV2Attr { + return func(m optionalAttr) { + m["dynamic_size"] = value + } +} + +// TensorArrayV2ClearAfterRead sets the optional clear_after_read attribute to value. +// If not specified, defaults to true +func TensorArrayV2ClearAfterRead(value bool) TensorArrayV2Attr { + return func(m optionalAttr) { + m["clear_after_read"] = value + } +} + +// TensorArrayV2TensorArrayName sets the optional tensor_array_name attribute to value. +// If not specified, defaults to "" +func TensorArrayV2TensorArrayName(value string) TensorArrayV2Attr { + return func(m optionalAttr) { + m["tensor_array_name"] = value + } +} + +// Deprecated. Use TensorArrayV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayV3 +func TensorArrayV2(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayV2", + Input: []tf.Input{ + size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Get the current size of the TensorArray. +// +// Arguments: +// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// flow_in: A float scalar that enforces proper chaining of operations. +// +// Returns The current size of the TensorArray. +func TensorArraySizeV3(scope *Scope, handle tf.Output, flow_in tf.Output) (size tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArraySizeV3", + Input: []tf.Input{ + handle, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Split the data from the input value into TensorArray elements. +// +// Assuming that `lengths` takes on values +// +// ```(n0, n1, ..., n(T-1))``` +// +// and that `value` has shape +// +// ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```, +// +// this splits values into a TensorArray with T tensors. +// +// TensorArray index t will be the subtensor of values with starting position +// +// ```(n0 + n1 + ... + n(t-1), 0, 0, ...)``` +// +// and having size +// +// ```nt x d0 x d1 x ...``` +// +// Arguments: +// handle: The handle to a TensorArray. +// value: The concatenated tensor to write to the TensorArray. +// lengths: The vector of lengths, how to split the rows of value into the +// TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// +// Returns A float scalar that enforces proper chaining of operations. +func TensorArraySplitV3(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArraySplitV3", + Input: []tf.Input{ + handle, value, lengths, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorArrayConcatV3Attr is an optional argument to TensorArrayConcatV3. +type TensorArrayConcatV3Attr func(optionalAttr) + +// TensorArrayConcatV3ElementShapeExcept0 sets the optional element_shape_except0 attribute to value. +// +// value: The expected shape of an element, if known, +// excluding the first dimension. Used to validate the shapes of +// TensorArray elements. If this shape is not fully specified, concatenating +// zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayConcatV3ElementShapeExcept0(value tf.Shape) TensorArrayConcatV3Attr { + return func(m optionalAttr) { + m["element_shape_except0"] = value + } +} + +// Concat the elements from the TensorArray into value `value`. +// +// Takes `T` elements of shapes +// +// ``` +// (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...) +// ``` +// +// and concatenates them into a Tensor of shape: +// +// ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)``` +// +// All elements must have the same shape (excepting the first dimension). +// +// Arguments: +// handle: The handle to a TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. +// +// Returns: +// value: All of the elements in the TensorArray, concatenated along the first +// axis. +// lengths: A vector of the row sizes of the original T elements in the +// value output. In the example above, this would be the values: +// `(n1, n2, ..., n(T-1))`. +func TensorArrayConcatV3(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV3Attr) (value tf.Output, lengths tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayConcatV3", + Input: []tf.Input{ + handle, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3. +type TensorArrayGatherV3Attr func(optionalAttr) + +// TensorArrayGatherV3ElementShape sets the optional element_shape attribute to value. +// +// value: The expected shape of an element, if known. Used to +// validate the shapes of TensorArray elements. If this shape is not +// fully specified, gathering zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayGatherV3ElementShape(value tf.Shape) TensorArrayGatherV3Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// Gather specific elements from the TensorArray into output `value`. +// +// All elements selected by `indices` must have the same shape. +// +// Arguments: +// handle: The handle to a TensorArray. +// indices: The locations in the TensorArray from which to read tensor elements. +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. +// +// Returns All of the elements in the TensorArray, concatenated along a new +// axis (the new dimension 0). +func TensorArrayGatherV3(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV3Attr) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayGatherV3", + Input: []tf.Input{ + handle, indices, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// GatherAttr is an optional argument to Gather. +type GatherAttr func(optionalAttr) + +// GatherValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func GatherValidateIndices(value bool) GatherAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Gather slices from `params` according to `indices`. +// +// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). +// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: +// +// ```python +// # Scalar indices +// output[:, ..., :] = params[indices, :, ... :] +// +// # Vector indices +// output[i, :, ..., :] = params[indices[i], :, ... :] +// +// # Higher rank indices +// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] +// ``` +// +// If `indices` is a permutation and `len(indices) == params.shape[0]` then +// this operation will permute `params` accordingly. +// +// `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in +// `indices` are always validated to be within range. If assigned to GPU, +// out-of-bound indices result in safe but unspecified behavior, which may include +// raising an error. +// +//
+// +//
+func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...GatherAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Gather", + Input: []tf.Input{ + params, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Read an element from the TensorArray into output `value`. +// +// Arguments: +// handle: The handle to a TensorArray. +// +// flow_in: A float scalar that enforces proper chaining of operations. +// dtype: The type of the elem that is returned. +// +// Returns The tensor that is read from the TensorArray. +func TensorArrayReadV3(scope *Scope, handle tf.Output, index tf.Output, flow_in tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "TensorArrayReadV3", + Input: []tf.Input{ + handle, index, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Push an element onto the tensor_array. +// +// Arguments: +// handle: The handle to a TensorArray. +// index: The position to write to inside the TensorArray. +// value: The tensor to write to the TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// +// Returns A float scalar that enforces proper chaining of operations. +func TensorArrayWriteV3(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayWriteV3", + Input: []tf.Input{ + handle, index, value, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a TensorArray for storing multiple gradients of values in the given handle. +// +// Similar to TensorArrayGradV3. However it creates an accumulator with an +// expanded shape compared to the input TensorArray whose gradient is being +// computed. This enables multiple gradients for the same TensorArray to be +// calculated using the same accumulator. +// +// Arguments: +// handle: The handle to the forward TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// shape_to_prepend: An int32 vector representing a shape. Elements in the gradient accumulator will +// have shape which is this shape_to_prepend value concatenated with shape of the +// elements in the TensorArray corresponding to the input handle. +// source: The gradient source string, used to decide which gradient TensorArray +// to return. +func TensorArrayGradWithShape(scope *Scope, handle tf.Output, flow_in tf.Output, shape_to_prepend tf.Output, source string) (grad_handle tf.Output, flow_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"source": source} + opspec := tf.OpSpec{ + Type: "TensorArrayGradWithShape", + Input: []tf.Input{ + handle, flow_in, shape_to_prepend, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Delete the stack from its resource container. +// +// Arguments: +// handle: The handle to a stack. +// +// Returns the created operation. +func StackCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StackCloseV2", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// Pop the element at the top of the stack. +// +// Arguments: +// handle: The handle to a stack. +// elem_type: The type of the elem that is popped. +// +// Returns The tensor that is popped from the top of the stack. +func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"elem_type": elem_type} + opspec := tf.OpSpec{ + Type: "StackPopV2", + Input: []tf.Input{ + handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StackPushV2Attr is an optional argument to StackPushV2. +type StackPushV2Attr func(optionalAttr) + +// StackPushV2SwapMemory sets the optional swap_memory attribute to value. +// +// value: Swap `elem` to CPU. Default to false. +// If not specified, defaults to false +func StackPushV2SwapMemory(value bool) StackPushV2Attr { + return func(m optionalAttr) { + m["swap_memory"] = value + } +} + +// Push an element onto the stack. +// +// Arguments: +// handle: The handle to a stack. +// elem: The tensor to be pushed onto the stack. +// +// Returns The same tensor as the input 'elem'. +func StackPushV2(scope *Scope, handle tf.Output, elem tf.Output, optional ...StackPushV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StackPushV2", + Input: []tf.Input{ + handle, elem, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StackV2Attr is an optional argument to StackV2. +type StackV2Attr func(optionalAttr) + +// StackV2StackName sets the optional stack_name attribute to value. +// +// value: Overrides the name used for the temporary stack resource. Default +// value is the name of the 'Stack' op (which is guaranteed unique). +// If not specified, defaults to "" +func StackV2StackName(value string) StackV2Attr { + return func(m optionalAttr) { + m["stack_name"] = value + } +} + +// A stack that produces elements in first-in last-out order. +// +// Arguments: +// max_size: The maximum size of the stack if non-negative. If negative, the stack +// size is unlimited. +// elem_type: The type of the elements on the stack. +// +// Returns The handle to the stack. +func StackV2(scope *Scope, max_size tf.Output, elem_type tf.DataType, optional ...StackV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"elem_type": elem_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StackV2", + Input: []tf.Input{ + max_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Checks a tensor for NaN, -Inf and +Inf values. +// +// When run, reports an `InvalidArgument` error if `tensor` has any values +// that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is. +// Unlike CheckNumerics (V1), CheckNumericsV2 distinguishes -Inf and +Inf in the +// errors it throws. +// +// Arguments: +// +// message: Prefix of the error message. +func CheckNumericsV2(scope *Scope, tensor tf.Output, message string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"message": message} + opspec := tf.OpSpec{ + Type: "CheckNumericsV2", + Input: []tf.Input{ + tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Applies a gradient to a given accumulator. +// +// Does not add if local_step is lesser than the accumulator's global_step. +// +// Arguments: +// handle: The handle to a accumulator. +// local_step: The local_step value at which the gradient was computed. +// gradient: A tensor of the gradient to be accumulated. +// +// Returns the created operation. +func ResourceAccumulatorApplyGradient(scope *Scope, handle tf.Output, local_step tf.Output, gradient tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceAccumulatorApplyGradient", + Input: []tf.Input{ + handle, local_step, gradient, + }, + } + return scope.AddOperation(opspec) +} + +// Updates the accumulator with a new value for global_step. +// +// Logs warning if the accumulator's value is already higher than +// new_global_step. +// +// Arguments: +// handle: The handle to an accumulator. +// new_global_step: The new global_step value to set. +// +// Returns the created operation. +func ResourceAccumulatorSetGlobalStep(scope *Scope, handle tf.Output, new_global_step tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceAccumulatorSetGlobalStep", + Input: []tf.Input{ + handle, new_global_step, + }, + } + return scope.AddOperation(opspec) +} + +// Computes the number of elements in the given queue. +// +// Arguments: +// handle: The handle to a queue. +// +// Returns The number of elements in the given queue. +func QueueSizeV2(scope *Scope, handle tf.Output) (size tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QueueSizeV2", + Input: []tf.Input{ + handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QueueEnqueueManyV2Attr is an optional argument to QueueEnqueueManyV2. +type QueueEnqueueManyV2Attr func(optionalAttr) + +// QueueEnqueueManyV2TimeoutMs sets the optional timeout_ms attribute to value. +// +// value: If the queue is too full, this operation will block for up +// to timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueEnqueueManyV2TimeoutMs(value int64) QueueEnqueueManyV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Enqueues zero or more tuples of one or more tensors in the given queue. +// +// This operation slices each component tensor along the 0th dimension to +// make multiple queue elements. All of the tuple components must have the +// same size in the 0th dimension. +// +// The components input has k elements, which correspond to the components of +// tuples stored in the given queue. +// +// N.B. If the queue is full, this operation will block until the given +// elements have been enqueued (or 'timeout_ms' elapses, if specified). +// +// Arguments: +// handle: The handle to a queue. +// components: One or more tensors from which the enqueued tensors should +// be taken. +// +// Returns the created operation. +func QueueEnqueueManyV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueManyV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueEnqueueManyV2", + Input: []tf.Input{ + handle, tf.OutputList(components), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QueueEnqueueV2Attr is an optional argument to QueueEnqueueV2. +type QueueEnqueueV2Attr func(optionalAttr) + +// QueueEnqueueV2TimeoutMs sets the optional timeout_ms attribute to value. +// +// value: If the queue is full, this operation will block for up to +// timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueEnqueueV2TimeoutMs(value int64) QueueEnqueueV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Enqueues a tuple of one or more tensors in the given queue. +// +// The components input has k elements, which correspond to the components of +// tuples stored in the given queue. +// +// N.B. If the queue is full, this operation will block until the given +// element has been enqueued (or 'timeout_ms' elapses, if specified). +// +// Arguments: +// handle: The handle to a queue. +// components: One or more tensors from which the enqueued tensors should be taken. +// +// Returns the created operation. +func QueueEnqueueV2(scope *Scope, handle tf.Output, components []tf.Output, optional ...QueueEnqueueV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueEnqueueV2", + Input: []tf.Input{ + handle, tf.OutputList(components), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// PriorityQueueV2Attr is an optional argument to PriorityQueueV2. +type PriorityQueueV2Attr func(optionalAttr) + +// PriorityQueueV2ComponentTypes sets the optional component_types attribute to value. +// +// value: The type of each component in a value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func PriorityQueueV2ComponentTypes(value []tf.DataType) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["component_types"] = value + } +} + +// PriorityQueueV2Capacity sets the optional capacity attribute to value. +// +// value: The upper bound on the number of elements in this queue. +// Negative numbers mean no limit. +// If not specified, defaults to -1 +func PriorityQueueV2Capacity(value int64) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// PriorityQueueV2Container sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func PriorityQueueV2Container(value string) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// PriorityQueueV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this queue will be shared under the given name +// across multiple sessions. +// If not specified, defaults to "" +func PriorityQueueV2SharedName(value string) PriorityQueueV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A queue that produces elements sorted by the first component value. +// +// Note that the PriorityQueue requires the first component of any element +// to be a scalar int64, in addition to the other elements declared by +// component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue +// and DequeueMany) on a PriorityQueue will all require (resp. output) one extra +// entry in their input (resp. output) lists. +// +// Arguments: +// shapes: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. If the length of +// this attr is 0, the shapes of queue elements are not constrained, and +// only one element may be dequeued at a time. +// +// Returns The handle to the queue. +func PriorityQueueV2(scope *Scope, shapes []tf.Shape, optional ...PriorityQueueV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PriorityQueueV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Does nothing. Serves as a control trigger for scheduling. +// +// Only useful as a placeholder for control edges. +// +// Returns the created operation. +func ControlTrigger(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ControlTrigger", + } + return scope.AddOperation(opspec) +} + +// Interleave the values from the `data` tensors into a single tensor. +// +// Builds a merged tensor such that +// +// ```python +// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...] +// ``` +// +// For example, if each `indices[m]` is scalar or vector, we have +// +// ```python +// # Scalar indices: +// merged[indices[m], ...] = data[m][...] +// +// # Vector indices: +// merged[indices[m][i], ...] = data[m][i, ...] +// ``` +// +// Each `data[i].shape` must start with the corresponding `indices[i].shape`, +// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we +// must have `data[i].shape = indices[i].shape + constant`. In terms of this +// `constant`, the output shape is +// +// merged.shape = [max(indices)] + constant +// +// Values may be merged in parallel, so if an index appears in both `indices[m][i]` +// and `indices[n][j]`, the result may be invalid. This differs from the normal +// DynamicStitch operator that defines the behavior in that case. +// +// For example: +// +// ```python +// indices[0] = 6 +// indices[1] = [4, 1] +// indices[2] = [[5, 2], [0, 3]] +// data[0] = [61, 62] +// data[1] = [[41, 42], [11, 12]] +// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] +// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], +// [51, 52], [61, 62]] +// ``` +// +// This method can be used to merge partitions created by `dynamic_partition` +// as illustrated on the following example: +// +// ```python +// # Apply function (increments x_i) on elements for which a certain condition +// # apply (x_i != -1 in this example). +// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4]) +// condition_mask=tf.not_equal(x,tf.constant(-1.)) +// partitioned_data = tf.dynamic_partition( +// x, tf.cast(condition_mask, tf.int32) , 2) +// partitioned_data[1] = partitioned_data[1] + 1.0 +// condition_indices = tf.dynamic_partition( +// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2) +// x = tf.dynamic_stitch(condition_indices, partitioned_data) +// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain +// # unchanged. +// ``` +// +//
+// +//
+func ParallelDynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ParallelDynamicStitch", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(data), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Partitions `data` into `num_partitions` tensors using indices from `partitions`. +// +// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` +// becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` +// are placed in `outputs[i]` in lexicographic order of `js`, and the first +// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. +// In detail, +// +// ```python +// outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] +// +// outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) +// ``` +// +// `data.shape` must start with `partitions.shape`. +// +// For example: +// +// ```python +// # Scalar partitions. +// partitions = 1 +// num_partitions = 2 +// data = [10, 20] +// outputs[0] = [] # Empty with shape [0, 2] +// outputs[1] = [[10, 20]] +// +// # Vector partitions. +// partitions = [0, 0, 1, 1, 0] +// num_partitions = 2 +// data = [10, 20, 30, 40, 50] +// outputs[0] = [10, 20, 50] +// outputs[1] = [30, 40] +// ``` +// +// See `dynamic_stitch` for an example on how to merge partitions back. +// +//
+// +//
+// +// Arguments: +// +// partitions: Any shape. Indices in the range `[0, num_partitions)`. +// num_partitions: The number of partitions to output. +func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_partitions": num_partitions} + opspec := tf.OpSpec{ + Type: "DynamicPartition", + Input: []tf.Input{ + data, partitions, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("DynamicPartition", err) + return + } + return outputs +} + +// ResourceConditionalAccumulatorAttr is an optional argument to ResourceConditionalAccumulator. +type ResourceConditionalAccumulatorAttr func(optionalAttr) + +// ResourceConditionalAccumulatorContainer sets the optional container attribute to value. +// +// value: If non-empty, this accumulator is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func ResourceConditionalAccumulatorContainer(value string) ResourceConditionalAccumulatorAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// ResourceConditionalAccumulatorSharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this accumulator will be shared under the +// given name across multiple sessions. +// If not specified, defaults to "" +func ResourceConditionalAccumulatorSharedName(value string) ResourceConditionalAccumulatorAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// ResourceConditionalAccumulatorReductionType sets the optional reduction_type attribute to value. +// If not specified, defaults to "MEAN" +func ResourceConditionalAccumulatorReductionType(value string) ResourceConditionalAccumulatorAttr { + return func(m optionalAttr) { + m["reduction_type"] = value + } +} + +// A conditional accumulator for aggregating gradients. +// +// The accumulator accepts gradients marked with local_step greater or +// equal to the most recent global_step known to the accumulator. The +// average can be extracted from the accumulator, provided sufficient +// gradients have been accumulated. Extracting the average automatically +// resets the aggregate to 0, and increments the global_step recorded by +// the accumulator. +// This is a resource version of ConditionalAccumulator that will work in TF2.0 +// with tf.cond version 2. +// +// Arguments: +// dtype: The type of the value being accumulated. +// shape: The shape of the values, can be [], in which case shape is unknown. +// +// Returns The handle to the accumulator. +func ResourceConditionalAccumulator(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...ResourceConditionalAccumulatorAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceConditionalAccumulator", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MultiDeviceIteratorFromStringHandleAttr is an optional argument to MultiDeviceIteratorFromStringHandle. +type MultiDeviceIteratorFromStringHandleAttr func(optionalAttr) + +// MultiDeviceIteratorFromStringHandleOutputTypes sets the optional output_types attribute to value. +// +// value: The type list for the return values. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func MultiDeviceIteratorFromStringHandleOutputTypes(value []tf.DataType) MultiDeviceIteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_types"] = value + } +} + +// MultiDeviceIteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value. +// +// value: The list of shapes being produced. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func MultiDeviceIteratorFromStringHandleOutputShapes(value []tf.Shape) MultiDeviceIteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_shapes"] = value + } +} + +// Generates a MultiDeviceIterator resource from its provided string handle. +// +// Arguments: +// string_handle: String representing the resource. +// +// Returns A MultiDeviceIterator resource. +func MultiDeviceIteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...MultiDeviceIteratorFromStringHandleAttr) (multi_device_iterator tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorFromStringHandle", + Input: []tf.Input{ + string_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a TensorArray for storing the gradients of values in the given handle. +// +// If the given TensorArray gradient already exists, returns a reference to it. +// +// Locks the size of the original TensorArray by disabling its dynamic size flag. +// +// **A note about the input flow_in:** +// +// The handle flow_in forces the execution of the gradient lookup to occur +// only after certain other operations have occurred. For example, when +// the forward TensorArray is dynamically sized, writes to this TensorArray +// may resize the object. The gradient TensorArray is statically sized based +// on the size of the forward TensorArray when this operation executes. +// Furthermore, the size of the forward TensorArray is frozen by this call. +// As a result, the flow is used to ensure that the call to generate the gradient +// TensorArray only happens after all writes are executed. +// +// In the case of dynamically sized TensorArrays, gradient computation should +// only be performed on read operations that have themselves been chained via +// flow to occur only after all writes have executed. That way the final size +// of the forward TensorArray is known when this operation is called. +// +// **A note about the source attribute:** +// +// TensorArray gradient calls use an accumulator TensorArray object. If +// multiple gradients are calculated and run in the same session, the multiple +// gradient nodes may accidentally flow through the same accumulator TensorArray. +// This double counts and generally breaks the TensorArray gradient flow. +// +// The solution is to identify which gradient call this particular +// TensorArray gradient is being called in. This is performed by identifying +// a unique string (e.g. "gradients", "gradients_1", ...) from the input +// gradient Tensor's name. This string is used as a suffix when creating +// the TensorArray gradient object here (the attribute `source`). +// +// The attribute `source` is added as a suffix to the forward TensorArray's +// name when performing the creation / lookup, so that each separate gradient +// calculation gets its own TensorArray accumulator. +// +// Arguments: +// handle: The handle to the forward TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// source: The gradient source string, used to decide which gradient TensorArray +// to return. +func TensorArrayGradV3(scope *Scope, handle tf.Output, flow_in tf.Output, source string) (grad_handle tf.Output, flow_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"source": source} + opspec := tf.OpSpec{ + Type: "TensorArrayGradV3", + Input: []tf.Input{ + handle, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Produces a string handle for the given MultiDeviceIterator. +// +// Arguments: +// multi_device_iterator: A MultiDeviceIterator resource. +// +// Returns A string representing the resource. +func MultiDeviceIteratorToStringHandle(scope *Scope, multi_device_iterator tf.Output) (string_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorToStringHandle", + Input: []tf.Input{ + multi_device_iterator, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gets next element for the provided shard number. +// +// Arguments: +// multi_device_iterator: A MultiDeviceIterator resource. +// shard_num: Integer representing which shard to fetch data for. +// incarnation_id: Which incarnation of the MultiDeviceIterator is running. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +// +// Returns Result of the get_next on the dataset. +func MultiDeviceIteratorGetNextFromShard(scope *Scope, multi_device_iterator tf.Output, shard_num tf.Output, incarnation_id tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorGetNextFromShard", + Input: []tf.Input{ + multi_device_iterator, shard_num, incarnation_id, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("MultiDeviceIteratorGetNextFromShard", err) + return + } + return components +} + +// Creates a MultiDeviceIterator resource. +// +// Arguments: +// devices: A list of devices the iterator works across. +// shared_name: If non-empty, this resource will be shared under the given name +// across multiple sessions. +// container: If non-empty, this resource is placed in the given container. +// Otherwise, a default container is used. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +// +// Returns Handle to the resource created. +func MultiDeviceIterator(scope *Scope, devices []string, shared_name string, container string, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"devices": devices, "shared_name": shared_name, "container": container, "output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "MultiDeviceIterator", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesCalculateBestFeatureSplitAttr is an optional argument to BoostedTreesCalculateBestFeatureSplit. +type BoostedTreesCalculateBestFeatureSplitAttr func(optionalAttr) + +// BoostedTreesCalculateBestFeatureSplitSplitType sets the optional split_type attribute to value. +// +// value: A string indicating if this Op should perform inequality split or equality split. +// If not specified, defaults to "inequality" +func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCalculateBestFeatureSplitAttr { + return func(m optionalAttr) { + m["split_type"] = value + } +} + +// Calculates gains for each feature and returns the best possible split information for the feature. +// +// The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature. +// +// It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return `node_ids_list` for each feature, containing the list of nodes that this feature can be used to split. +// +// In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features). +// +// The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature. +// +// Arguments: +// node_id_range: A Rank 1 tensor (shape=[2]) to specify the range [first, last) of node ids to process within `stats_summary_list`. The nodes are iterated between the two nodes specified by the tensor, as like `for node_id in range(node_id_range[0], node_id_range[1])` (Note that the last index node_id_range[1] is exclusive). +// stats_summary: A Rank 4 tensor (#shape=[max_splits, feature_dims, bucket, stats_dims]) for accumulated stats summary (gradient/hessian) per node, per dimension, per buckets for each feature. +// The first dimension of the tensor is the maximum number of splits, and thus not all elements of it will be used, but only the indexes specified by node_ids will be used. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// tree_complexity: adjustment to the gain, per leaf based. +// min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting. +// logits_dimension: The dimension of logit, i.e., number of classes. +// +// Returns: +// node_ids: A Rank 1 tensors indicating possible split node ids for each feature. The length of the list is num_features, but each tensor has different size as each feature provides different possible nodes. See above for details like shapes and sizes. +// gains: A Rank 1 tensors indicating the best gains for each feature to split for certain nodes. See above for details like shapes and sizes. +// feature_dimensions: A Rank 1 tensors indicating the best feature dimension for each feature to split for certain nodes if the feature is multi-dimension. See above for details like shapes and sizes. +// thresholds: A Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node. See above for details like shapes and sizes. +// left_node_contribs: A Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature. This value will be used to make the left node value by adding to the parent node value. Second dimension size is 1 for 1-dimensional logits, but would be larger for multi-class problems. See above for details like shapes and sizes. +// right_node_contribs: A Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node. +// split_with_default_directions: A Rank 1 tensors indicating the which direction to go if data is missing. See above for details like shapes and sizes. +// Inequality with default left returns 0, inequality with default right returns 1, equality with default right returns 2. +func BoostedTreesCalculateBestFeatureSplit(scope *Scope, node_id_range tf.Output, stats_summary tf.Output, l1 tf.Output, l2 tf.Output, tree_complexity tf.Output, min_node_weight tf.Output, logits_dimension int64, optional ...BoostedTreesCalculateBestFeatureSplitAttr) (node_ids tf.Output, gains tf.Output, feature_dimensions tf.Output, thresholds tf.Output, left_node_contribs tf.Output, right_node_contribs tf.Output, split_with_default_directions tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCalculateBestFeatureSplit", + Input: []tf.Input{ + node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// ModelDatasetAttr is an optional argument to ModelDataset. +type ModelDatasetAttr func(optionalAttr) + +// ModelDatasetAlgorithm sets the optional algorithm attribute to value. +// If not specified, defaults to 0 +func ModelDatasetAlgorithm(value int64) ModelDatasetAttr { + return func(m optionalAttr) { + m["algorithm"] = value + } +} + +// ModelDatasetCpuBudget sets the optional cpu_budget attribute to value. +// If not specified, defaults to 0 +func ModelDatasetCpuBudget(value int64) ModelDatasetAttr { + return func(m optionalAttr) { + m["cpu_budget"] = value + } +} + +// Identity transformation that models performance. +// +// Identity transformation that models performance. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// +// +func ModelDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ModelDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ModelDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a list of tensors with the same shapes and contents as the input +// +// tensors. +// +// This op can be used to override the gradient for complicated functions. For +// example, suppose y = f(x) and we wish to apply a custom function g for backprop +// such that dx = g(dy). In Python, +// +// ```python +// with tf.get_default_graph().gradient_override_map( +// {'IdentityN': 'OverrideGradientWithG'}): +// y, _ = identity_n([f(x), x]) +// +// @tf.RegisterGradient('OverrideGradientWithG') +// def ApplyG(op, dy, _): +// return [None, g(dy)] # Do not backprop to f(x). +// ``` +func IdentityN(scope *Scope, input []tf.Output) (output []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IdentityN", + Input: []tf.Input{ + tf.OutputList(input), + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("IdentityN", err) + return + } + return output +} + +// Returns true if and only if the given Optional variant has a value. +func OptionalHasValue(scope *Scope, optional tf.Output) (has_value tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OptionalHasValue", + Input: []tf.Input{ + optional, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Constructs an Optional variant from a tuple of tensors. +func OptionalFromValue(scope *Scope, components []tf.Output) (optional tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OptionalFromValue", + Input: []tf.Input{ + tf.OutputList(components), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OptimizeDatasetAttr is an optional argument to OptimizeDataset. +type OptimizeDatasetAttr func(optionalAttr) + +// OptimizeDatasetOptimizationConfigs sets the optional optimization_configs attribute to value. +// If not specified, defaults to <> +func OptimizeDatasetOptimizationConfigs(value []string) OptimizeDatasetAttr { + return func(m optionalAttr) { + m["optimization_configs"] = value + } +} + +// Creates a dataset by applying optimizations to `input_dataset`. +// +// Creates a dataset by applying optimizations to `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// optimizations: A `tf.string` vector `tf.Tensor` identifying optimizations to use. +// +// +func OptimizeDataset(scope *Scope, input_dataset tf.Output, optimizations tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...OptimizeDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OptimizeDataset", + Input: []tf.Input{ + input_dataset, optimizations, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts the given `resource_handle` representing an iterator to a string. +// +// Arguments: +// resource_handle: A handle to an iterator resource. +// +// Returns A string representation of the given handle. +func IteratorToStringHandle(scope *Scope, resource_handle tf.Output) (string_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IteratorToStringHandle", + Input: []tf.Input{ + resource_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gets the next output from the given iterator. +// +// This operation is a synchronous version IteratorGetNext. It should only be used +// in situations where the iterator does not block the calling thread, or where +// the calling thread is not a member of the thread pool used to execute parallel +// operations (e.g. in eager mode). +func IteratorGetNextSync(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "IteratorGetNextSync", + Input: []tf.Input{ + iterator, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("IteratorGetNextSync", err) + return + } + return components +} + +// RaggedCountSparseOutputAttr is an optional argument to RaggedCountSparseOutput. +type RaggedCountSparseOutputAttr func(optionalAttr) + +// RaggedCountSparseOutputMinlength sets the optional minlength attribute to value. +// +// value: Minimum value to count. Can be set to -1 for no minimum. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RaggedCountSparseOutputMinlength(value int64) RaggedCountSparseOutputAttr { + return func(m optionalAttr) { + m["minlength"] = value + } +} + +// RaggedCountSparseOutputMaxlength sets the optional maxlength attribute to value. +// +// value: Maximum value to count. Can be set to -1 for no maximum. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func RaggedCountSparseOutputMaxlength(value int64) RaggedCountSparseOutputAttr { + return func(m optionalAttr) { + m["maxlength"] = value + } +} + +// Performs sparse-output bin counting for a ragged tensor input. +// +// Counts the number of times each value occurs in the input. +// +// Arguments: +// splits: Tensor containing the row splits of the ragged tensor to count. +// values: Tensor containing values of the sparse tensor to count. +// weights: A Tensor of the same shape as indices containing per-index weight values. +// May also be the empty tensor if no weights are used. +// binary_output: Whether to output the number of occurrences of each value or 1. +// +// Returns: +// output_indices: Indices tensor for the resulting sparse tensor object. +// output_values: Values tensor for the resulting sparse tensor object. +// output_dense_shape: Shape tensor for the resulting sparse tensor object. +// END +// } +// attr { +// name: "T" +// description: < 0. +func FixedLengthRecordDataset(scope *Scope, filenames tf.Output, header_bytes tf.Output, record_bytes tf.Output, footer_bytes tf.Output, buffer_size tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FixedLengthRecordDataset", + Input: []tf.Input{ + filenames, header_bytes, record_bytes, footer_bytes, buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the lines of one or more text files. +// +// Arguments: +// filenames: A scalar or a vector containing the name(s) of the file(s) to be +// read. +// compression_type: A scalar containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// buffer_size: A scalar containing the number of bytes to buffer. +func TextLineDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TextLineDataset", + Input: []tf.Input{ + filenames, compression_type, buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A container for an iterator resource. +// +// Arguments: +// multi_device_iterator: A handle to the multi device iterator to delete. +// iterators: A list of iterator handles (unused). This is added so that automatic control dependencies get added during function tracing that ensure this op runs after all the dependent iterators are deleted. +// deleter: A variant deleter. +// +// Returns the created operation. +func DeleteMultiDeviceIterator(scope *Scope, multi_device_iterator tf.Output, iterators []tf.Output, deleter tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeleteMultiDeviceIterator", + Input: []tf.Input{ + multi_device_iterator, tf.OutputList(iterators), deleter, + }, + } + return scope.AddOperation(opspec) +} + +// Creates a dataset with a range of values. Corresponds to python's xrange. +// +// Arguments: +// start: corresponds to start in python's xrange(). +// stop: corresponds to stop in python's xrange(). +// step: corresponds to step in python's xrange(). +// +// +func RangeDataset(scope *Scope, start tf.Output, stop tf.Output, step tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "RangeDataset", + Input: []tf.Input{ + start, stop, step, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that batches and pads `batch_size` elements from the input. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// padded_shapes: A list of int64 tensors representing the desired padded shapes +// of the corresponding output components. These shapes may be partially +// specified, using `-1` to indicate that a particular dimension should be +// padded to the maximum size of all batch elements. +// padding_values: A list of scalars containing the padding value to use for +// each of the outputs. +// +func PaddedBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "PaddedBatchDataset", + Input: []tf.Input{ + input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BatchDatasetV2Attr is an optional argument to BatchDatasetV2. +type BatchDatasetV2Attr func(optionalAttr) + +// BatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value. +// If not specified, defaults to false +func BatchDatasetV2ParallelCopy(value bool) BatchDatasetV2Attr { + return func(m optionalAttr) { + m["parallel_copy"] = value + } +} + +// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a batch. +// drop_remainder: A scalar representing whether the last batch should be dropped in case its size +// is smaller than desired. +// +// +func BatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...BatchDatasetV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BatchDatasetV2", + Input: []tf.Input{ + input_dataset, batch_size, drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ShuffleDatasetAttr is an optional argument to ShuffleDataset. +type ShuffleDatasetAttr func(optionalAttr) + +// ShuffleDatasetReshuffleEachIteration sets the optional reshuffle_each_iteration attribute to value. +// +// value: If true, each iterator over this dataset will be given +// a different pseudorandomly generated seed, based on a sequence seeded by the +// `seed` and `seed2` inputs. If false, each iterator will be given the same +// seed, and repeated iteration over this dataset will yield the exact same +// sequence of results. +// If not specified, defaults to true +func ShuffleDatasetReshuffleEachIteration(value bool) ShuffleDatasetAttr { + return func(m optionalAttr) { + m["reshuffle_each_iteration"] = value + } +} + +// Creates a dataset that shuffles elements from `input_dataset` pseudorandomly. +// +// Arguments: +// +// buffer_size: The number of output elements to buffer in an iterator over +// this dataset. Compare with the `min_after_dequeue` attr when creating a +// `RandomShuffleQueue`. +// seed: A scalar seed for the random number generator. If either `seed` or +// `seed2` is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// +// +func ShuffleDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShuffleDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShuffleDataset", + Input: []tf.Input{ + input_dataset, buffer_size, seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset containing elements of first component of `input_dataset` having true in the last component. +func FilterByLastComponentDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "FilterByLastComponentDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PrefetchDatasetAttr is an optional argument to PrefetchDataset. +type PrefetchDatasetAttr func(optionalAttr) + +// PrefetchDatasetSlackPeriod sets the optional slack_period attribute to value. +// If not specified, defaults to 0 +func PrefetchDatasetSlackPeriod(value int64) PrefetchDatasetAttr { + return func(m optionalAttr) { + m["slack_period"] = value + } +} + +// PrefetchDatasetLegacyAutotune sets the optional legacy_autotune attribute to value. +// If not specified, defaults to true +func PrefetchDatasetLegacyAutotune(value bool) PrefetchDatasetAttr { + return func(m optionalAttr) { + m["legacy_autotune"] = value + } +} + +// Creates a dataset that asynchronously prefetches elements from `input_dataset`. +// +// Arguments: +// +// buffer_size: The maximum number of elements to buffer in an iterator over +// this dataset. +// +// +func PrefetchDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...PrefetchDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PrefetchDataset", + Input: []tf.Input{ + input_dataset, buffer_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Forwards the input to the output. +// +// This operator represents the loop termination condition used by the +// "pivot" switches of a loop. +// +// Arguments: +// input: A boolean scalar, representing the branch predicate of the Switch op. +// +// Returns The same tensor as `input`. +func LoopCond(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LoopCond", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that skips `count` elements from the `input_dataset`. +// +// Arguments: +// +// count: A scalar representing the number of elements from the `input_dataset` +// that should be skipped. If count is -1, skips everything. +// +// +func SkipDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "SkipDataset", + Input: []tf.Input{ + input_dataset, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the outputs of `input_dataset` `count` times. +// +// Arguments: +// +// count: A scalar representing the number of times that `input_dataset` should +// be repeated. A value of `-1` indicates that it should be repeated infinitely. +// +// +func RepeatDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "RepeatDataset", + Input: []tf.Input{ + input_dataset, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnpackAttr is an optional argument to Unpack. +type UnpackAttr func(optionalAttr) + +// UnpackAxis sets the optional axis attribute to value. +// +// value: Dimension along which to unpack. Negative values wrap around, so the +// valid range is `[-R, R)`. +// If not specified, defaults to 0 +func UnpackAxis(value int64) UnpackAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors. +// +// Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. +// For example, given a tensor of shape `(A, B, C, D)`; +// +// If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` +// and each tensor in `output` will have shape `(B, C, D)`. (Note that the +// dimension unpacked along is gone, unlike `split`). +// +// If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` +// and each tensor in `output` will have shape `(A, C, D)`. +// Etc. +// +// This is the opposite of `pack`. +// +// Arguments: +// value: 1-D or higher, with `axis` dimension size equal to `num`. +// +// +// Returns The list of tensors unpacked from `value`. +func Unpack(scope *Scope, value tf.Output, num int64, optional ...UnpackAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num": num} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Unpack", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("Unpack", err) + return + } + return output +} + +// Creates a dataset that concatenates `input_dataset` with `another_dataset`. +func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ConcatenateDataset", + Input: []tf.Input{ + input_dataset, another_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A placeholder op for a value that will be fed into the computation. +// +// DEPRECATED at GraphDef version 23: Placeholder now behaves the same as PlaceholderV2. +// +// N.B. This operation will fail with an error if it is executed. It is +// intended as a way to represent a value that will always be fed, and to +// provide attrs that enable the fed value to be checked at runtime. +// +// Arguments: +// dtype: The type of elements in the tensor. +// shape: The shape of the tensor. The shape can be any partially-specified +// shape. To be unconstrained, pass in a shape with unknown rank. +// +// Returns A placeholder tensor that must be replaced using the feed mechanism. +func PlaceholderV2(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + opspec := tf.OpSpec{ + Type: "PlaceholderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomShuffleQueueV2Attr is an optional argument to RandomShuffleQueueV2. +type RandomShuffleQueueV2Attr func(optionalAttr) + +// RandomShuffleQueueV2Shapes sets the optional shapes attribute to value. +// +// value: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. If the length of +// this attr is 0, the shapes of queue elements are not constrained, and +// only one element may be dequeued at a time. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func RandomShuffleQueueV2Shapes(value []tf.Shape) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["shapes"] = value + } +} + +// RandomShuffleQueueV2Capacity sets the optional capacity attribute to value. +// +// value: The upper bound on the number of elements in this queue. +// Negative numbers mean no limit. +// If not specified, defaults to -1 +func RandomShuffleQueueV2Capacity(value int64) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// RandomShuffleQueueV2MinAfterDequeue sets the optional min_after_dequeue attribute to value. +// +// value: Dequeue will block unless there would be this +// many elements after the dequeue or the queue is closed. This +// ensures a minimum level of mixing of elements. +// If not specified, defaults to 0 +func RandomShuffleQueueV2MinAfterDequeue(value int64) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["min_after_dequeue"] = value + } +} + +// RandomShuffleQueueV2Seed sets the optional seed attribute to value. +// +// value: If either seed or seed2 is set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func RandomShuffleQueueV2Seed(value int64) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomShuffleQueueV2Seed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomShuffleQueueV2Seed2(value int64) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// RandomShuffleQueueV2Container sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func RandomShuffleQueueV2Container(value string) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// RandomShuffleQueueV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this queue will be shared under the given name +// across multiple sessions. +// If not specified, defaults to "" +func RandomShuffleQueueV2SharedName(value string) RandomShuffleQueueV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A queue that randomizes the order of elements. +// +// Arguments: +// component_types: The type of each component in a value. +// +// Returns The handle to the queue. +func RandomShuffleQueueV2(scope *Scope, component_types []tf.DataType, optional ...RandomShuffleQueueV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomShuffleQueueV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that splits a SparseTensor into elements row-wise. +func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorSliceDataset", + Input: []tf.Input{ + indices, values, dense_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits `components` as a tuple of tensors once. +func TensorDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "TensorDataset", + Input: []tf.Input{ + tf.OutputList(components), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QueueCloseV2Attr is an optional argument to QueueCloseV2. +type QueueCloseV2Attr func(optionalAttr) + +// QueueCloseV2CancelPendingEnqueues sets the optional cancel_pending_enqueues attribute to value. +// +// value: If true, all pending enqueue requests that are +// blocked on the given queue will be canceled. +// If not specified, defaults to false +func QueueCloseV2CancelPendingEnqueues(value bool) QueueCloseV2Attr { + return func(m optionalAttr) { + m["cancel_pending_enqueues"] = value + } +} + +// Closes the given queue. +// +// This operation signals that no more elements will be enqueued in the +// given queue. Subsequent Enqueue(Many) operations will fail. +// Subsequent Dequeue(Many) operations will continue to succeed if +// sufficient elements remain in the queue. Subsequent Dequeue(Many) +// operations that would block will fail immediately. +// +// Arguments: +// handle: The handle to a queue. +// +// Returns the created operation. +func QueueCloseV2(scope *Scope, handle tf.Output, optional ...QueueCloseV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueCloseV2", + Input: []tf.Input{ + handle, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// DebugIdentityV2Attr is an optional argument to DebugIdentityV2. +type DebugIdentityV2Attr func(optionalAttr) + +// DebugIdentityV2TfdbgContextId sets the optional tfdbg_context_id attribute to value. +// +// value: A tfdbg-generated ID for the context that the op belongs to, +// e.g., a concrete compiled tf.function. +// If not specified, defaults to "" +func DebugIdentityV2TfdbgContextId(value string) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["tfdbg_context_id"] = value + } +} + +// DebugIdentityV2OpName sets the optional op_name attribute to value. +// +// value: Optional. Name of the op that the debug op is concerned with. +// Used only for single-tensor trace. +// If not specified, defaults to "" +func DebugIdentityV2OpName(value string) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["op_name"] = value + } +} + +// DebugIdentityV2OutputSlot sets the optional output_slot attribute to value. +// +// value: Optional. Output slot index of the tensor that the debug op +// is concerned with. Used only for single-tensor trace. +// If not specified, defaults to -1 +func DebugIdentityV2OutputSlot(value int64) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["output_slot"] = value + } +} + +// DebugIdentityV2TensorDebugMode sets the optional tensor_debug_mode attribute to value. +// +// value: TensorDebugMode enum value. See debug_event.proto for details. +// If not specified, defaults to -1 +func DebugIdentityV2TensorDebugMode(value int64) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["tensor_debug_mode"] = value + } +} + +// DebugIdentityV2DebugUrls sets the optional debug_urls attribute to value. +// +// value: List of URLs to debug targets, e.g., file:///foo/tfdbg_dump. +// If not specified, defaults to <> +func DebugIdentityV2DebugUrls(value []string) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["debug_urls"] = value + } +} + +// DebugIdentityV2CircularBufferSize sets the optional circular_buffer_size attribute to value. +// If not specified, defaults to 1000 +func DebugIdentityV2CircularBufferSize(value int64) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["circular_buffer_size"] = value + } +} + +// DebugIdentityV2TfdbgRunId sets the optional tfdbg_run_id attribute to value. +// If not specified, defaults to "" +func DebugIdentityV2TfdbgRunId(value string) DebugIdentityV2Attr { + return func(m optionalAttr) { + m["tfdbg_run_id"] = value + } +} + +// Debug Identity V2 Op. +// +// Provides an identity mapping from input to output, while writing the content of +// the input tensor by calling DebugEventsWriter. +// +// The semantics of the input tensor depends on tensor_debug_mode. In typical +// usage, the input tensor comes directly from the user computation only when +// graph_debug_mode is FULL_TENSOR (see protobuf/debug_event.proto for a +// list of all the possible values of graph_debug_mode). For the other debug modes, +// the input tensor should be produced by an additional op or subgraph that +// computes summary information about one or more tensors. +// +// Arguments: +// input: Input tensor, non-Reference type +func DebugIdentityV2(scope *Scope, input tf.Output, optional ...DebugIdentityV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DebugIdentityV2", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DebugNanCountAttr is an optional argument to DebugNanCount. +type DebugNanCountAttr func(optionalAttr) + +// DebugNanCountDeviceName sets the optional device_name attribute to value. +// If not specified, defaults to "" +func DebugNanCountDeviceName(value string) DebugNanCountAttr { + return func(m optionalAttr) { + m["device_name"] = value + } +} + +// DebugNanCountTensorName sets the optional tensor_name attribute to value. +// +// value: Name of the input tensor. +// If not specified, defaults to "" +func DebugNanCountTensorName(value string) DebugNanCountAttr { + return func(m optionalAttr) { + m["tensor_name"] = value + } +} + +// DebugNanCountDebugUrls sets the optional debug_urls attribute to value. +// +// value: List of URLs to debug targets, e.g., +// file:///foo/tfdbg_dump, grpc:://localhost:11011. +// If not specified, defaults to <> +func DebugNanCountDebugUrls(value []string) DebugNanCountAttr { + return func(m optionalAttr) { + m["debug_urls"] = value + } +} + +// DebugNanCountGatedGrpc sets the optional gated_grpc attribute to value. +// +// value: Whether this op will be gated. If any of the debug_urls of this +// debug node is of the grpc:// scheme, when the value of this attribute is set +// to True, the data will not actually be sent via the grpc stream unless this +// debug op has been enabled at the debug_url. If all of the debug_urls of this +// debug node are of the grpc:// scheme and the debug op is enabled at none of +// them, the output will be an empty Tensor. +// If not specified, defaults to false +func DebugNanCountGatedGrpc(value bool) DebugNanCountAttr { + return func(m optionalAttr) { + m["gated_grpc"] = value + } +} + +// Debug NaN Value Counter Op. +// +// Counts number of NaNs in the input tensor, for debugging. +// +// Arguments: +// input: Input tensor, non-Reference type. +func DebugNanCount(scope *Scope, input tf.Output, optional ...DebugNanCountAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DebugNanCount", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DebugIdentityAttr is an optional argument to DebugIdentity. +type DebugIdentityAttr func(optionalAttr) + +// DebugIdentityDeviceName sets the optional device_name attribute to value. +// +// value: Name of the device on which the tensor resides. +// If not specified, defaults to "" +func DebugIdentityDeviceName(value string) DebugIdentityAttr { + return func(m optionalAttr) { + m["device_name"] = value + } +} + +// DebugIdentityTensorName sets the optional tensor_name attribute to value. +// +// value: Name of the input tensor. +// If not specified, defaults to "" +func DebugIdentityTensorName(value string) DebugIdentityAttr { + return func(m optionalAttr) { + m["tensor_name"] = value + } +} + +// DebugIdentityDebugUrls sets the optional debug_urls attribute to value. +// +// value: List of URLs to debug targets, e.g., +// file:///foo/tfdbg_dump, grpc:://localhost:11011 +// If not specified, defaults to <> +func DebugIdentityDebugUrls(value []string) DebugIdentityAttr { + return func(m optionalAttr) { + m["debug_urls"] = value + } +} + +// DebugIdentityGatedGrpc sets the optional gated_grpc attribute to value. +// +// value: Whether this op will be gated. If any of the debug_urls of this +// debug node is of the grpc:// scheme, when the value of this attribute is set +// to True, the data will not actually be sent via the grpc stream unless this +// debug op has been enabled at the debug_url. If all of the debug_urls of this +// debug node are of the grpc:// scheme and the debug op is enabled at none of +// them, the output will be an empty Tensor. +// If not specified, defaults to false +func DebugIdentityGatedGrpc(value bool) DebugIdentityAttr { + return func(m optionalAttr) { + m["gated_grpc"] = value + } +} + +// Provides an identity mapping of the non-Ref type input tensor for debugging. +// +// Provides an identity mapping of the non-Ref type input tensor for debugging. +// +// Arguments: +// input: Input tensor, non-Reference type +func DebugIdentity(scope *Scope, input tf.Output, optional ...DebugIdentityAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DebugIdentity", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Aggregates the summary of accumulated stats for the batch. +// +// The summary stats contains gradients and hessians accumulated for each node, bucket and dimension id. +// +// Arguments: +// node_ids: int32; Rank 1 Tensor containing node ids for each example, shape [batch_size]. +// gradients: float32; Rank 2 Tensor (shape=[batch_size, logits_dimension]) with gradients for each example. +// hessians: float32; Rank 2 Tensor (shape=[batch_size, hessian_dimension]) with hessians for each example. +// feature_indices: int32; Rank 2 indices of feature sparse Tensors (shape=[number of sparse entries, 2]). +// Number of sparse entries across all instances from the batch. The first value is +// the index of the instance, the second is dimension of the feature. The second axis +// can only have 2 values, i.e., the input dense version of Tensor can only be matrix. +// feature_values: int32; Rank 1 values of feature sparse Tensors (shape=[number of sparse entries]). +// Number of sparse entries across all instances from the batch. The first value is +// the index of the instance, the second is dimension of the feature. +// feature_shape: int32; Rank 1 dense shape of feature sparse Tensors (shape=[2]). +// The first axis can only have 2 values, [batch_size, feature_dimension]. +// max_splits: int; the maximum number of splits possible in the whole tree. +// num_buckets: int; equals to the maximum possible value of bucketized feature + 1. +// +// Returns: +// stats_summary_indices: int32; Rank 2 indices of summary sparse Tensors (shape=[number of non zero statistics, 4]) +// The second axis can only be 4 including node id, feature dimension, bucket id, and statistics_dimension. +// statistics_dimension = logits_dimension + hessian_dimension. +// stats_summary_values: output Rank 1 Tensor (shape=[number of non zero statistics]) +// stats_summary_shape: output Rank 1 Tensor (shape=[4]) +// The tensor has following 4 values: [max_splits, feature_dimension, num_buckets, statistics_dimension], +// where statistics_dimension = gradient_dimension + hessian_dimension. gradient_dimension +// is the same as label_dimension, i.e., the output space. hessian_dimension can be the same +// as logits dimension when diagonal hessian is used, or label_dimension^2 when full +// hessian is used. +func BoostedTreesSparseAggregateStats(scope *Scope, node_ids tf.Output, gradients tf.Output, hessians tf.Output, feature_indices tf.Output, feature_values tf.Output, feature_shape tf.Output, max_splits int64, num_buckets int64) (stats_summary_indices tf.Output, stats_summary_values tf.Output, stats_summary_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"max_splits": max_splits, "num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "BoostedTreesSparseAggregateStats", + Input: []tf.Input{ + node_ids, gradients, hessians, feature_indices, feature_values, feature_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// DecodeProtoV2Attr is an optional argument to DecodeProtoV2. +type DecodeProtoV2Attr func(optionalAttr) + +// DecodeProtoV2DescriptorSource sets the optional descriptor_source attribute to value. +// +// value: Either the special value `local://` or a path to a file containing +// a serialized `FileDescriptorSet`. +// If not specified, defaults to "local://" +func DecodeProtoV2DescriptorSource(value string) DecodeProtoV2Attr { + return func(m optionalAttr) { + m["descriptor_source"] = value + } +} + +// DecodeProtoV2MessageFormat sets the optional message_format attribute to value. +// +// value: Either `binary` or `text`. +// If not specified, defaults to "binary" +func DecodeProtoV2MessageFormat(value string) DecodeProtoV2Attr { + return func(m optionalAttr) { + m["message_format"] = value + } +} + +// DecodeProtoV2Sanitize sets the optional sanitize attribute to value. +// +// value: Whether to sanitize the result or not. +// If not specified, defaults to false +func DecodeProtoV2Sanitize(value bool) DecodeProtoV2Attr { + return func(m optionalAttr) { + m["sanitize"] = value + } +} + +// The op extracts fields from a serialized protocol buffers message into tensors. +// +// The `decode_proto` op extracts fields from a serialized protocol buffers +// message into tensors. The fields in `field_names` are decoded and converted +// to the corresponding `output_types` if possible. +// +// A `message_type` name must be provided to give context for the field names. +// The actual message descriptor can be looked up either in the linked-in +// descriptor pool or a filename provided by the caller using the +// `descriptor_source` attribute. +// +// Each output tensor is a dense tensor. This means that it is padded to hold +// the largest number of repeated elements seen in the input minibatch. (The +// shape is also padded by one to prevent zero-sized dimensions). The actual +// repeat counts for each example in the minibatch can be found in the `sizes` +// output. In many cases the output of `decode_proto` is fed immediately into +// tf.squeeze if missing values are not a concern. When using tf.squeeze, always +// pass the squeeze dimension explicitly to avoid surprises. +// +// For the most part, the mapping between Proto field types and TensorFlow dtypes +// is straightforward. However, there are a few special cases: +// +// - A proto field that contains a submessage or group can only be converted +// to `DT_STRING` (the serialized submessage). This is to reduce the complexity +// of the API. The resulting string can be used as input to another instance of +// the decode_proto op. +// +// - TensorFlow lacks support for unsigned integers. The ops represent uint64 +// types as a `DT_INT64` with the same twos-complement bit pattern (the obvious +// way). Unsigned int32 values can be represented exactly by specifying type +// `DT_INT64`, or using twos-complement if the caller specifies `DT_INT32` in +// the `output_types` attribute. +// +// Both binary and text proto serializations are supported, and can be +// chosen using the `format` attribute. +// +// The `descriptor_source` attribute selects the source of protocol +// descriptors to consult when looking up `message_type`. This may be: +// +// - An empty string or "local://", in which case protocol descriptors are +// created for C++ (not Python) proto definitions linked to the binary. +// +// - A file, in which case protocol descriptors are created from the file, +// which is expected to contain a `FileDescriptorSet` serialized as a string. +// NOTE: You can build a `descriptor_source` file using the `--descriptor_set_out` +// and `--include_imports` options to the protocol compiler `protoc`. +// +// - A "bytes://", in which protocol descriptors are created from ``, +// which is expected to be a `FileDescriptorSet` serialized as a string. +// +// Arguments: +// bytes: Tensor of serialized protos with shape `batch_shape`. +// message_type: Name of the proto message type to decode. +// field_names: List of strings containing proto field names. An extension field can be decoded +// by using its full name, e.g. EXT_PACKAGE.EXT_FIELD_NAME. +// output_types: List of TF types to use for the respective field in field_names. +// +// Returns: +// sizes: Tensor of int32 with shape `[batch_shape, len(field_names)]`. +// Each entry is the number of values found for the corresponding field. +// Optional fields may have 0 or 1 values. +// values: List of tensors containing values for the corresponding field. +// `values[i]` has datatype `output_types[i]` +// and shape `[batch_shape, max(sizes[...,i])]`. +func DecodeProtoV2(scope *Scope, bytes tf.Output, message_type string, field_names []string, output_types []tf.DataType, optional ...DecodeProtoV2Attr) (sizes tf.Output, values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"message_type": message_type, "field_names": field_names, "output_types": output_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeProtoV2", + Input: []tf.Input{ + bytes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + sizes = op.Output(idx) + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("DecodeProtoV2", err) + return + } + return sizes, values +} + +// Output the logits for the given input data +// +// Arguments: +// tree_handle: Handle to the tree resource. +// dense_features: Rank 2 dense features tensor. +// logits_dimension: Scalar, dimension of the logits. +// +// Returns The logits predictions from the tree for each instance in the batch. +func TensorForestTreePredict(scope *Scope, tree_handle tf.Output, dense_features tf.Output, logits_dimension int64) (logits tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "TensorForestTreePredict", + Input: []tf.Input{ + tree_handle, dense_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EncodeProtoAttr is an optional argument to EncodeProto. +type EncodeProtoAttr func(optionalAttr) + +// EncodeProtoDescriptorSource sets the optional descriptor_source attribute to value. +// If not specified, defaults to "local://" +func EncodeProtoDescriptorSource(value string) EncodeProtoAttr { + return func(m optionalAttr) { + m["descriptor_source"] = value + } +} + +// The op serializes protobuf messages provided in the input tensors. +// +// The types of the tensors in `values` must match the schema for the fields +// specified in `field_names`. All the tensors in `values` must have a common +// shape prefix, *batch_shape*. +// +// The `sizes` tensor specifies repeat counts for each field. The repeat count +// (last dimension) of a each tensor in `values` must be greater than or equal +// to corresponding repeat count in `sizes`. +// +// A `message_type` name must be provided to give context for the field names. +// The actual message descriptor can be looked up either in the linked-in +// descriptor pool or a filename provided by the caller using the +// `descriptor_source` attribute. +// +// For the most part, the mapping between Proto field types and TensorFlow dtypes +// is straightforward. However, there are a few special cases: +// +// - A proto field that contains a submessage or group can only be converted +// to `DT_STRING` (the serialized submessage). This is to reduce the complexity +// of the API. The resulting string can be used as input to another instance of +// the decode_proto op. +// +// - TensorFlow lacks support for unsigned integers. The ops represent uint64 +// types as a `DT_INT64` with the same twos-complement bit pattern (the obvious +// way). Unsigned int32 values can be represented exactly by specifying type +// `DT_INT64`, or using twos-complement if the caller specifies `DT_INT32` in +// the `output_types` attribute. +// +// The `descriptor_source` attribute selects the source of protocol +// descriptors to consult when looking up `message_type`. This may be: +// +// - An empty string or "local://", in which case protocol descriptors are +// created for C++ (not Python) proto definitions linked to the binary. +// +// - A file, in which case protocol descriptors are created from the file, +// which is expected to contain a `FileDescriptorSet` serialized as a string. +// NOTE: You can build a `descriptor_source` file using the `--descriptor_set_out` +// and `--include_imports` options to the protocol compiler `protoc`. +// +// - A "bytes://", in which protocol descriptors are created from ``, +// which is expected to be a `FileDescriptorSet` serialized as a string. +// +// Arguments: +// sizes: Tensor of int32 with shape `[batch_shape, len(field_names)]`. +// values: List of tensors containing values for the corresponding field. +// field_names: List of strings containing proto field names. +// message_type: Name of the proto message type to decode. +// +// Returns Tensor of serialized protos with shape `batch_shape`. +func EncodeProto(scope *Scope, sizes tf.Output, values []tf.Output, field_names []string, message_type string, optional ...EncodeProtoAttr) (bytes tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"field_names": field_names, "message_type": message_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodeProto", + Input: []tf.Input{ + sizes, tf.OutputList(values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Registers a dataset with the tf.data service. +func RegisterDataset(scope *Scope, dataset tf.Output, address tf.Output, protocol tf.Output, external_state_policy int64) (dataset_id tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"external_state_policy": external_state_policy} + opspec := tf.OpSpec{ + Type: "RegisterDataset", + Input: []tf.Input{ + dataset, address, protocol, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DataServiceDatasetAttr is an optional argument to DataServiceDataset. +type DataServiceDatasetAttr func(optionalAttr) + +// DataServiceDatasetTaskRefreshIntervalHintMs sets the optional task_refresh_interval_hint_ms attribute to value. +// If not specified, defaults to -1 +func DataServiceDatasetTaskRefreshIntervalHintMs(value int64) DataServiceDatasetAttr { + return func(m optionalAttr) { + m["task_refresh_interval_hint_ms"] = value + } +} + +// Creates a dataset that reads data from the tf.data service. +func DataServiceDataset(scope *Scope, dataset_id tf.Output, processing_mode tf.Output, address tf.Output, protocol tf.Output, job_name tf.Output, max_outstanding_requests tf.Output, iteration_counter tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...DataServiceDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DataServiceDataset", + Input: []tf.Input{ + dataset_id, processing_mode, address, protocol, job_name, max_outstanding_requests, iteration_counter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains the unique elements of `input_dataset`. +func UniqueDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "UniqueDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A dataset that splits the elements of its input into multiple elements. +func ExperimentalUnbatchDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalUnbatchDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A dataset that splits the elements of its input into multiple elements. +func UnbatchDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "UnbatchDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// +// thread_pool: A resource produced by the ThreadPoolHandle op. +// +// +func ExperimentalThreadPoolDataset(scope *Scope, input_dataset tf.Output, thread_pool tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalThreadPoolDataset", + Input: []tf.Input{ + input_dataset, thread_pool, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gets the next output from the given iterator as an Optional variant. +func IteratorGetNextAsOptional(scope *Scope, iterator tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (optional tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "IteratorGetNextAsOptional", + Input: []tf.Input{ + iterator, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces a summary of any statistics recorded by the given statistics manager. +func StatsAggregatorSummary(scope *Scope, iterator tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatsAggregatorSummary", + Input: []tf.Input{ + iterator, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExperimentalStatsAggregatorHandleAttr is an optional argument to ExperimentalStatsAggregatorHandle. +type ExperimentalStatsAggregatorHandleAttr func(optionalAttr) + +// ExperimentalStatsAggregatorHandleContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func ExperimentalStatsAggregatorHandleContainer(value string) ExperimentalStatsAggregatorHandleAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// ExperimentalStatsAggregatorHandleSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func ExperimentalStatsAggregatorHandleSharedName(value string) ExperimentalStatsAggregatorHandleAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a statistics manager resource. +func ExperimentalStatsAggregatorHandle(scope *Scope, optional ...ExperimentalStatsAggregatorHandleAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalStatsAggregatorHandle", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatsAggregatorHandleAttr is an optional argument to StatsAggregatorHandle. +type StatsAggregatorHandleAttr func(optionalAttr) + +// StatsAggregatorHandleContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StatsAggregatorHandleContainer(value string) StatsAggregatorHandleAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StatsAggregatorHandleSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StatsAggregatorHandleSharedName(value string) StatsAggregatorHandleAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a statistics manager resource. +func StatsAggregatorHandle(scope *Scope, optional ...StatsAggregatorHandleAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatsAggregatorHandle", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that executes a SQL query and emits rows of the result set. +// +// Arguments: +// driver_name: The database type. Currently, the only supported type is 'sqlite'. +// data_source_name: A connection string to connect to the database. +// query: A SQL query to execute. +// +// +func ExperimentalSqlDataset(scope *Scope, driver_name tf.Output, data_source_name tf.Output, query tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalSqlDataset", + Input: []tf.Input{ + driver_name, data_source_name, query, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generate the bucket boundaries for each feature based on accumulated summaries. +// +// An op that returns a list of float tensors for a quantile stream resource. Each +// tensor is Rank 1 containing bucket boundaries for a single feature. +// +// Arguments: +// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. +// num_features: inferred int; number of features to get bucket boundaries for. +// +// Returns float; List of Rank 1 Tensors each containing the bucket boundaries for a feature. +func BoostedTreesQuantileStreamResourceGetBucketBoundaries(scope *Scope, quantile_stream_resource_handle tf.Output, num_features int64) (bucket_boundaries []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_features": num_features} + opspec := tf.OpSpec{ + Type: "BoostedTreesQuantileStreamResourceGetBucketBoundaries", + Input: []tf.Input{ + quantile_stream_resource_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if bucket_boundaries, idx, err = makeOutputList(op, idx, "bucket_boundaries"); err != nil { + scope.UpdateErr("BoostedTreesQuantileStreamResourceGetBucketBoundaries", err) + return + } + return bucket_boundaries +} + +// Creates a dataset that passes a sliding window over `input_dataset`. +// +// Arguments: +// +// window_size: A scalar representing the number of elements in the +// sliding window. +// window_shift: A scalar representing the steps moving the sliding window +// forward in one iteration. It must be positive. +// window_stride: A scalar representing the stride of the input elements of the sliding window. +// It must be positive. +// +// +func ExperimentalSlidingWindowDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, window_shift tf.Output, window_stride tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalSlidingWindowDataset", + Input: []tf.Input{ + input_dataset, window_size, window_shift, window_stride, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArraySizeV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArraySizeV3 +func TensorArraySizeV2(scope *Scope, handle tf.Output, flow_in tf.Output) (size tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArraySizeV2", + Input: []tf.Input{ + handle, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RebatchDatasetAttr is an optional argument to RebatchDataset. +type RebatchDatasetAttr func(optionalAttr) + +// RebatchDatasetUseFallback sets the optional use_fallback attribute to value. +// If not specified, defaults to true +func RebatchDatasetUseFallback(value bool) RebatchDatasetAttr { + return func(m optionalAttr) { + m["use_fallback"] = value + } +} + +// Creates a dataset that changes the batch size. +// +// Creates a dataset that changes the batch size of the dataset to current batch +// size // num_workers. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// num_replicas: A scalar representing the number of replicas to distribute this batch across. As +// a result of this transformation the current batch size would end up being +// divided by this parameter. +// +// +func RebatchDataset(scope *Scope, input_dataset tf.Output, num_replicas tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...RebatchDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RebatchDataset", + Input: []tf.Input{ + input_dataset, num_replicas, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// +// num_threads: Identifies the number of threads to use for the private threadpool. +// +// +func ExperimentalPrivateThreadPoolDataset(scope *Scope, input_dataset tf.Output, num_threads tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalPrivateThreadPoolDataset", + Input: []tf.Input{ + input_dataset, num_threads, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// +// num_threads: Identifies the number of threads to use for the private threadpool. +// +// +func PrivateThreadPoolDataset(scope *Scope, input_dataset tf.Output, num_threads tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "PrivateThreadPoolDataset", + Input: []tf.Input{ + input_dataset, num_threads, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExperimentalParseExampleDatasetAttr is an optional argument to ExperimentalParseExampleDataset. +type ExperimentalParseExampleDatasetAttr func(optionalAttr) + +// ExperimentalParseExampleDatasetSloppy sets the optional sloppy attribute to value. +// If not specified, defaults to false +func ExperimentalParseExampleDatasetSloppy(value bool) ExperimentalParseExampleDatasetAttr { + return func(m optionalAttr) { + m["sloppy"] = value + } +} + +// Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. +// +// Arguments: +// +// +// dense_defaults: A dict mapping string keys to `Tensor`s. +// The keys of the dict must match the dense_keys of the feature. +// sparse_keys: A list of string keys in the examples features. +// The results for these keys will be returned as `SparseTensor` objects. +// dense_keys: A list of Ndense string Tensors (scalars). +// The keys expected in the Examples features associated with dense values. +// sparse_types: A list of `DTypes` of the same length as `sparse_keys`. +// Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), +// and `tf.string` (`BytesList`) are supported. +// dense_shapes: List of tuples with the same length as `dense_keys`. +// The shape of the data for each dense feature referenced by `dense_keys`. +// Required for any input tensors identified by `dense_keys`. Must be +// either fully defined, or may contain an unknown first dimension. +// An unknown first dimension means the feature is treated as having +// a variable number of blocks, and the output shape along this dimension +// is considered unknown at graph build time. Padding is applied for +// minibatch elements smaller than the maximum number of blocks for the +// given feature along this dimension. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +func ExperimentalParseExampleDataset(scope *Scope, input_dataset tf.Output, num_parallel_calls tf.Output, dense_defaults []tf.Output, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ExperimentalParseExampleDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes, "output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalParseExampleDataset", + Input: []tf.Input{ + input_dataset, num_parallel_calls, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a batched matrix tensor with new batched diagonal values. +// +// Given `input` and `diagonal`, this operation returns a tensor with the +// same shape and values as `input`, except for the main diagonal of the +// innermost matrices. These will be overwritten by the values in `diagonal`. +// +// The output is computed as follows: +// +// Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has +// `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a +// tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where: +// +// * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`. +// * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`. +// +// Arguments: +// input: Rank `k+1`, where `k >= 1`. +// diagonal: Rank `k`, where `k >= 1`. +// +// Returns Rank `k+1`, with `output.shape = input.shape`. +func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixSetDiag", + Input: []tf.Input{ + input, diagonal, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParseExampleDatasetV2Attr is an optional argument to ParseExampleDatasetV2. +type ParseExampleDatasetV2Attr func(optionalAttr) + +// ParseExampleDatasetV2Deterministic sets the optional deterministic attribute to value. +// +// value: A string indicating the op-level determinism to use. Deterministic controls +// whether the dataset is allowed to return elements out of order if the next +// element to be returned isn't available, but a later element is. Options are +// "true", "false", and "default". "default" indicates that determinism should be +// decided by the `experimental_deterministic` parameter of `tf.data.Options`. +// If not specified, defaults to "default" +func ParseExampleDatasetV2Deterministic(value string) ParseExampleDatasetV2Attr { + return func(m optionalAttr) { + m["deterministic"] = value + } +} + +// ParseExampleDatasetV2RaggedKeys sets the optional ragged_keys attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseExampleDatasetV2RaggedKeys(value []string) ParseExampleDatasetV2Attr { + return func(m optionalAttr) { + m["ragged_keys"] = value + } +} + +// ParseExampleDatasetV2RaggedValueTypes sets the optional ragged_value_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseExampleDatasetV2RaggedValueTypes(value []tf.DataType) ParseExampleDatasetV2Attr { + return func(m optionalAttr) { + m["ragged_value_types"] = value + } +} + +// ParseExampleDatasetV2RaggedSplitTypes sets the optional ragged_split_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseExampleDatasetV2RaggedSplitTypes(value []tf.DataType) ParseExampleDatasetV2Attr { + return func(m optionalAttr) { + m["ragged_split_types"] = value + } +} + +// Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. +// +// Arguments: +// +// +// dense_defaults: A dict mapping string keys to `Tensor`s. +// The keys of the dict must match the dense_keys of the feature. +// sparse_keys: A list of string keys in the examples features. +// The results for these keys will be returned as `SparseTensor` objects. +// dense_keys: A list of Ndense string Tensors (scalars). +// The keys expected in the Examples features associated with dense values. +// sparse_types: A list of `DTypes` of the same length as `sparse_keys`. +// Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), +// and `tf.string` (`BytesList`) are supported. +// dense_shapes: List of tuples with the same length as `dense_keys`. +// The shape of the data for each dense feature referenced by `dense_keys`. +// Required for any input tensors identified by `dense_keys`. Must be +// either fully defined, or may contain an unknown first dimension. +// An unknown first dimension means the feature is treated as having +// a variable number of blocks, and the output shape along this dimension +// is considered unknown at graph build time. Padding is applied for +// minibatch elements smaller than the maximum number of blocks for the +// given feature along this dimension. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +func ParseExampleDatasetV2(scope *Scope, input_dataset tf.Output, num_parallel_calls tf.Output, dense_defaults []tf.Output, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ParseExampleDatasetV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes, "output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParseExampleDatasetV2", + Input: []tf.Input{ + input_dataset, num_parallel_calls, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// GenerateVocabRemappingAttr is an optional argument to GenerateVocabRemapping. +type GenerateVocabRemappingAttr func(optionalAttr) + +// GenerateVocabRemappingOldVocabSize sets the optional old_vocab_size attribute to value. +// +// value: Number of entries in the old vocab file to consider. If -1, +// use the entire old vocabulary. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func GenerateVocabRemappingOldVocabSize(value int64) GenerateVocabRemappingAttr { + return func(m optionalAttr) { + m["old_vocab_size"] = value + } +} + +// Given a path to new and old vocabulary files, returns a remapping Tensor of +// +// length `num_new_vocab`, where `remapping[i]` contains the row number in the old +// vocabulary that corresponds to row `i` in the new vocabulary (starting at line +// `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` +// in the new vocabulary is not in the old vocabulary. The old vocabulary is +// constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the +// default value of -1. +// +// `num_vocab_offset` enables +// use in the partitioned variable case, and should generally be set through +// examining partitioning info. The format of the files should be a text file, +// with each line containing a single entity within the vocabulary. +// +// For example, with `new_vocab_file` a text file containing each of the following +// elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], +// `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be +// `[0, -1, 2]`. +// +// The op also returns a count of how many entries in the new vocabulary +// were present in the old vocabulary, which is used to calculate the number of +// values to initialize in a weight matrix remapping +// +// This functionality can be used to remap both row vocabularies (typically, +// features) and column vocabularies (typically, classes) from TensorFlow +// checkpoints. Note that the partitioning logic relies on contiguous vocabularies +// corresponding to div-partitioned variables. Moreover, the underlying remapping +// uses an IndexTable (as opposed to an inexact CuckooTable), so client code should +// use the corresponding index_table_from_file() as the FeatureColumn framework +// does (as opposed to tf.feature_to_id(), which uses a CuckooTable). +// +// Arguments: +// new_vocab_file: Path to the new vocab file. +// old_vocab_file: Path to the old vocab file. +// new_vocab_offset: How many entries into the new vocab file to start reading. +// num_new_vocab: Number of entries in the new vocab file to remap. +// +// Returns: +// remapping: A Tensor of length num_new_vocab where the element at index i +// is equal to the old ID that maps to the new ID i. This element is -1 for any +// new ID that is not found in the old vocabulary. +// num_present: Number of new vocab entries found in old vocab. +func GenerateVocabRemapping(scope *Scope, new_vocab_file tf.Output, old_vocab_file tf.Output, new_vocab_offset int64, num_new_vocab int64, optional ...GenerateVocabRemappingAttr) (remapping tf.Output, num_present tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"new_vocab_offset": new_vocab_offset, "num_new_vocab": num_new_vocab} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "GenerateVocabRemapping", + Input: []tf.Input{ + new_vocab_file, old_vocab_file, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Creates a dataset that overrides the maximum intra-op parallelism. +// +// Arguments: +// +// max_intra_op_parallelism: Identifies the maximum intra-op parallelism to use. +// +// +func ExperimentalMaxIntraOpParallelismDataset(scope *Scope, input_dataset tf.Output, max_intra_op_parallelism tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalMaxIntraOpParallelismDataset", + Input: []tf.Input{ + input_dataset, max_intra_op_parallelism, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SpaceToBatch for N-D tensors of type T. +// +// This operation divides "spatial" dimensions `[1, ..., M]` of the input into a +// grid of blocks of shape `block_shape`, and interleaves these blocks with the +// "batch" dimension (0) such that in the output, the spatial dimensions +// `[1, ..., M]` correspond to the position within the grid, and the batch +// dimension combines both the position within a spatial block and the original +// batch position. Prior to division into blocks, the spatial dimensions of the +// input are optionally zero padded according to `paddings`. See below for a +// precise description. +// +// Arguments: +// input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, +// where spatial_shape has `M` dimensions. +// block_shape: 1-D with shape `[M]`, all values must be >= 1. +// paddings: 2-D with shape `[M, 2]`, all values must be >= 0. +// `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension +// `i + 1`, which corresponds to spatial dimension `i`. It is required that +// `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`. +// +// This operation is equivalent to the following steps: +// +// 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the +// input according to `paddings` to produce `padded` of shape `padded_shape`. +// +// 2. Reshape `padded` to `reshaped_padded` of shape: +// +// [batch] + +// [padded_shape[1] / block_shape[0], +// block_shape[0], +// ..., +// padded_shape[M] / block_shape[M-1], +// block_shape[M-1]] + +// remaining_shape +// +// 3. Permute dimensions of `reshaped_padded` to produce +// `permuted_reshaped_padded` of shape: +// +// block_shape + +// [batch] + +// [padded_shape[1] / block_shape[0], +// ..., +// padded_shape[M] / block_shape[M-1]] + +// remaining_shape +// +// 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch +// dimension, producing an output tensor of shape: +// +// [batch * prod(block_shape)] + +// [padded_shape[1] / block_shape[0], +// ..., +// padded_shape[M] / block_shape[M-1]] + +// remaining_shape +// +// Some examples: +// +// (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and +// `paddings = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1], [2]], [[3], [4]]]] +// ``` +// +// The output tensor has shape `[4, 1, 1, 1]` and value: +// +// ``` +// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// ``` +// +// (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and +// `paddings = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// The output tensor has shape `[4, 1, 1, 3]` and value: +// +// ``` +// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] +// ``` +// +// (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and +// `paddings = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]], +// [[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// The output tensor has shape `[4, 2, 2, 1]` and value: +// +// ``` +// x = [[[[1], [3]], [[9], [11]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +// +// (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and +// paddings = `[[0, 0], [2, 0]]`: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]]], +// [[[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// The output tensor has shape `[8, 1, 3, 1]` and value: +// +// ``` +// x = [[[[0], [1], [3]]], [[[0], [9], [11]]], +// [[[0], [2], [4]]], [[[0], [10], [12]]], +// [[[0], [5], [7]]], [[[0], [13], [15]]], +// [[[0], [6], [8]]], [[[0], [14], [16]]]] +// ``` +// +// Among others, this operation is useful for reducing atrous convolution into +// regular convolution. +func SpaceToBatchND(scope *Scope, input tf.Output, block_shape tf.Output, paddings tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SpaceToBatchND", + Input: []tf.Input{ + input, block_shape, paddings, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a batched diagonal tensor with given batched diagonal values. +// +// Returns a tensor with the contents in `diagonal` as `k[0]`-th to `k[1]`-th +// diagonals of a matrix, with everything else padded with `padding`. `num_rows` +// and `num_cols` specify the dimension of the innermost matrix of the output. If +// both are not specified, the op assumes the innermost matrix is square and infers +// its size from `k` and the innermost dimension of `diagonal`. If only one of them +// is specified, the op assumes the unspecified value is the smallest possible +// based on other criteria. +// +// Let `diagonal` have `r` dimensions `[I, J, ..., L, M, N]`. The output tensor has +// rank `r+1` with shape `[I, J, ..., L, M, num_rows, num_cols]` when only one +// diagonal is given (`k` is an integer or `k[0] == k[1]`). Otherwise, it has rank +// `r` with shape `[I, J, ..., L, num_rows, num_cols]`. +// +// The second innermost dimension of `diagonal` has double meaning. +// When `k` is scalar or `k[0] == k[1]`, `M` is part of the batch size +// [I, J, ..., M], and the output tensor is: +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, n-max(d_upper, 0)] ; if n - m == d_upper +// padding_value ; otherwise +// ``` +// +// Otherwise, `M` is treated as the number of diagonals for the matrix in the +// same batch (`M = k[1]-k[0]+1`), and the output tensor is: +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1] +// padding_value ; otherwise +// ``` +// where `d = n - m`, `diag_index = k[1] - d`, and `index_in_diag = n - max(d, 0)`. +// +// For example: +// +// ``` +// # The main diagonal. +// diagonal = np.array([[1, 2, 3, 4], # Input shape: (2, 4) +// [5, 6, 7, 8]]) +// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0], # Output shape: (2, 4, 4) +// [0, 2, 0, 0], +// [0, 0, 3, 0], +// [0, 0, 0, 4]], +// [[5, 0, 0, 0], +// [0, 6, 0, 0], +// [0, 0, 7, 0], +// [0, 0, 0, 8]]] +// +// # A superdiagonal (per batch). +// diagonal = np.array([[1, 2, 3], # Input shape: (2, 3) +// [4, 5, 6]]) +// tf.matrix_diag(diagonal, k = 1) +// ==> [[[0, 1, 0, 0], # Output shape: (2, 4, 4) +// [0, 0, 2, 0], +// [0, 0, 0, 3], +// [0, 0, 0, 0]], +// [[0, 4, 0, 0], +// [0, 0, 5, 0], +// [0, 0, 0, 6], +// [0, 0, 0, 0]]] +// +// # A band of diagonals. +// diagonals = np.array([[[1, 2, 3], # Input shape: (2, 2, 3) +// [4, 5, 0]], +// [[6, 7, 9], +// [9, 1, 0]]]) +// tf.matrix_diag(diagonals, k = (-1, 0)) +// ==> [[[1, 0, 0], # Output shape: (2, 3, 3) +// [4, 2, 0], +// [0, 5, 3]], +// [[6, 0, 0], +// [9, 7, 0], +// [0, 1, 9]]] +// +// # Rectangular matrix. +// diagonal = np.array([1, 2]) # Input shape: (2) +// tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4) +// ==> [[0, 0, 0, 0], # Output shape: (3, 4) +// [1, 0, 0, 0], +// [0, 2, 0, 0]] +// +// # Rectangular matrix with inferred num_cols and padding_value = 9. +// tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9) +// ==> [[9, 9], # Output shape: (3, 2) +// [1, 9], +// [9, 2]] +// ``` +// +// Arguments: +// diagonal: Rank `r`, where `r >= 1` +// k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main +// diagonal, and negative value means subdiagonals. `k` can be a single integer +// (for a single diagonal) or a pair of integers specifying the low and high ends +// of a matrix band. `k[0]` must not be larger than `k[1]`. +// num_rows: The number of rows of the output matrix. If it is not provided, the op assumes +// the output matrix is a square matrix and infers the matrix size from k and the +// innermost dimension of `diagonal`. +// num_cols: The number of columns of the output matrix. If it is not provided, the op +// assumes the output matrix is a square matrix and infers the matrix size from +// k and the innermost dimension of `diagonal`. +// padding_value: The number to fill the area outside the specified diagonal band with. +// Default is 0. +// +// Returns Has rank `r+1` when `k` is an integer or `k[0] == k[1]`, rank `r` otherwise. +func MatrixDiagV2(scope *Scope, diagonal tf.Output, k tf.Output, num_rows tf.Output, num_cols tf.Output, padding_value tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDiagV2", + Input: []tf.Input{ + diagonal, k, num_rows, num_cols, padding_value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that overrides the maximum intra-op parallelism. +// +// Arguments: +// +// max_intra_op_parallelism: Identifies the maximum intra-op parallelism to use. +// +// +func MaxIntraOpParallelismDataset(scope *Scope, input_dataset tf.Output, max_intra_op_parallelism tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "MaxIntraOpParallelismDataset", + Input: []tf.Input{ + input_dataset, max_intra_op_parallelism, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StageClearAttr is an optional argument to StageClear. +type StageClearAttr func(optionalAttr) + +// StageClearCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageClearCapacity(value int64) StageClearAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// StageClearMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func StageClearMemoryLimit(value int64) StageClearAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// StageClearContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func StageClearContainer(value string) StageClearAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// StageClearSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func StageClearSharedName(value string) StageClearAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes all elements in the underlying container. +// +// Returns the created operation. +func StageClear(scope *Scope, dtypes []tf.DataType, optional ...StageClearAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StageClear", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Records the latency of producing `input_dataset` elements in a StatsAggregator. +func ExperimentalLatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalLatencyStatsDataset", + Input: []tf.Input{ + input_dataset, tag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the name of the device on which `resource` has been placed. +func IteratorGetDevice(scope *Scope, resource tf.Output) (device tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IteratorGetDevice", + Input: []tf.Input{ + resource, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a Dataset that returns pseudorandom numbers. +// +// Arguments: +// seed: A scalar seed for the random number generator. If either seed or +// seed2 is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// +// +func ExperimentalRandomDataset(scope *Scope, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalRandomDataset", + Input: []tf.Input{ + seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains the elements of `input_dataset` ignoring errors. +func ExperimentalIgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalIgnoreErrorsDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNBackpropV2Attr is an optional argument to CudnnRNNBackpropV2. +type CudnnRNNBackpropV2Attr func(optionalAttr) + +// CudnnRNNBackpropV2RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNBackpropV2RnnMode(value string) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNBackpropV2InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNBackpropV2InputMode(value string) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNBackpropV2Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNBackpropV2Direction(value string) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNBackpropV2Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV2Dropout(value float32) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNBackpropV2Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV2Seed(value int64) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNBackpropV2Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV2Seed2(value int64) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Backprop step of CudnnRNN. +// +// Compute the backprop of both data and weights in a RNN. Takes an extra +// "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN +// cudnnRNNAlgo_t and cudnnMathType_t. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// output_backprop: A 3-D tensor with the same shape as output in the forward pass. +// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward +// pass. +// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward +// pass. +// reserve_space: The same reserve_space produced in the forward operation. +// host_reserved: The same host_reserved produced in the forward operation. +// input_backprop: The backprop to input in the forward pass. Has the same shape +// as input. +// input_h_backprop: The backprop to input_h in the forward pass. Has the same +// shape as input_h. +// input_c_backprop: The backprop to input_c in the forward pass. Has the same +// shape as input_c. +// params_backprop: The backprop to the params buffer in the forward pass. Has the +// same shape as params. +func CudnnRNNBackpropV2(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, host_reserved tf.Output, optional ...CudnnRNNBackpropV2Attr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNBackpropV2", + Input: []tf.Input{ + input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// A substitute for `InterleaveDataset` on a fixed list of `N` datasets. +// +// Arguments: +// selector_input_dataset: A dataset of scalar `DT_INT64` elements that determines which of the +// `N` data inputs should produce the next output element. +// data_input_datasets: `N` datasets with the same type that will be interleaved according to +// the values of `selector_input_dataset`. +// +// +func DirectedInterleaveDataset(scope *Scope, selector_input_dataset tf.Output, data_input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "DirectedInterleaveDataset", + Input: []tf.Input{ + selector_input_dataset, tf.OutputList(data_input_datasets), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that batches input elements into a SparseTensor. +// +// Arguments: +// input_dataset: A handle to an input dataset. Must have a single component. +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// row_shape: A vector representing the dense shape of each row in the produced +// SparseTensor. The shape may be partially specified, using `-1` to indicate +// that a particular dimension should use the maximum size of all batch elements. +// +// +func ExperimentalDenseToSparseBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, row_shape tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalDenseToSparseBatchDataset", + Input: []tf.Input{ + input_dataset, batch_size, row_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Writes the given dataset to the given file using the TFRecord format. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to write. +// filename: A scalar string tensor representing the filename to use. +// compression_type: A scalar string tensor containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// +// Returns the created operation. +func ExperimentalDatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExperimentalDatasetToTFRecord", + Input: []tf.Input{ + input_dataset, filename, compression_type, + }, + } + return scope.AddOperation(opspec) +} + +// Creates a dataset from the given `graph_def`. +// +// Creates a dataset from the provided `graph_def`. +// +// Arguments: +// graph_def: The graph representation of the dataset (as serialized GraphDef). +// +// Returns A variant tensor representing the dataset. +func DatasetFromGraph(scope *Scope, graph_def tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DatasetFromGraph", + Input: []tf.Input{ + graph_def, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the cardinality of `input_dataset`. +// +// Returns the cardinality of `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to return cardinality for. +// +// Returns The cardinality of `input_dataset`. Named constants are used to represent +// infinite and unknown cardinality. +func ExperimentalDatasetCardinality(scope *Scope, input_dataset tf.Output) (cardinality tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExperimentalDatasetCardinality", + Input: []tf.Input{ + input_dataset, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Interleave the values from the `data` tensors into a single tensor. +// +// Builds a merged tensor such that +// +// ```python +// merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...] +// ``` +// +// For example, if each `indices[m]` is scalar or vector, we have +// +// ```python +// # Scalar indices: +// merged[indices[m], ...] = data[m][...] +// +// # Vector indices: +// merged[indices[m][i], ...] = data[m][i, ...] +// ``` +// +// Each `data[i].shape` must start with the corresponding `indices[i].shape`, +// and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we +// must have `data[i].shape = indices[i].shape + constant`. In terms of this +// `constant`, the output shape is +// +// merged.shape = [max(indices)] + constant +// +// Values are merged in order, so if an index appears in both `indices[m][i]` and +// `indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the +// merged result. If you do not need this guarantee, ParallelDynamicStitch might +// perform better on some devices. +// +// For example: +// +// ```python +// indices[0] = 6 +// indices[1] = [4, 1] +// indices[2] = [[5, 2], [0, 3]] +// data[0] = [61, 62] +// data[1] = [[41, 42], [11, 12]] +// data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] +// merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], +// [51, 52], [61, 62]] +// ``` +// +// This method can be used to merge partitions created by `dynamic_partition` +// as illustrated on the following example: +// +// ```python +// # Apply function (increments x_i) on elements for which a certain condition +// # apply (x_i != -1 in this example). +// x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4]) +// condition_mask=tf.not_equal(x,tf.constant(-1.)) +// partitioned_data = tf.dynamic_partition( +// x, tf.cast(condition_mask, tf.int32) , 2) +// partitioned_data[1] = partitioned_data[1] + 1.0 +// condition_indices = tf.dynamic_partition( +// tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2) +// x = tf.dynamic_stitch(condition_indices, partitioned_data) +// # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain +// # unchanged. +// ``` +// +//
+// +//
+func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DynamicStitch", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(data), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Uncompresses a compressed dataset element. +func UncompressElement(scope *Scope, compressed tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "UncompressElement", + Input: []tf.Input{ + compressed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("UncompressElement", err) + return + } + return components +} + +// Records the bytes size of each element of `input_dataset` in a StatsAggregator. +func BytesProducedStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "BytesProducedStatsDataset", + Input: []tf.Input{ + input_dataset, tag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExperimentalAutoShardDatasetAttr is an optional argument to ExperimentalAutoShardDataset. +type ExperimentalAutoShardDatasetAttr func(optionalAttr) + +// ExperimentalAutoShardDatasetAutoShardPolicy sets the optional auto_shard_policy attribute to value. +// If not specified, defaults to 0 +func ExperimentalAutoShardDatasetAutoShardPolicy(value int64) ExperimentalAutoShardDatasetAttr { + return func(m optionalAttr) { + m["auto_shard_policy"] = value + } +} + +// Creates a dataset that shards the input dataset. +// +// Creates a dataset that shards the input dataset by num_workers, returning a +// sharded dataset for the index-th worker. This attempts to automatically shard +// a dataset by examining the Dataset graph and inserting a shard op before the +// inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset). +// +// This dataset will throw a NotFound error if we cannot shard the dataset +// automatically. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// num_workers: A scalar representing the number of workers to distribute this dataset across. +// index: A scalar representing the index of the current worker out of num_workers. +// +// +func ExperimentalAutoShardDataset(scope *Scope, input_dataset tf.Output, num_workers tf.Output, index tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ExperimentalAutoShardDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalAutoShardDataset", + Input: []tf.Input{ + input_dataset, num_workers, index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A transformation that asserts which transformations happen next. +// +// This transformation checks whether the camel-case names (i.e. "FlatMap", not +// "flat_map") of the transformations following this transformation match the list +// of names in the `transformations` argument. If there is a mismatch, the +// transformation raises an exception. +// +// The check occurs when iterating over the contents of the dataset, which +// means that the check happens *after* any static optimizations are applied +// to the dataset graph. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// `AssertNextDataset` passes through the outputs of its input dataset. +// transformations: A `tf.string` vector `tf.Tensor` identifying the transformations that are +// expected to happen next. +// +// +func AssertNextDataset(scope *Scope, input_dataset tf.Output, transformations tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "AssertNextDataset", + Input: []tf.Input{ + input_dataset, transformations, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Return the index of device the op runs. +// +// Given a list of device names, this operation returns the index of the device +// this op runs. The length of the list is returned in two cases: +// (1) Device does not exist in the given device list. +// (2) It is in XLA compilation. +func DeviceIndex(scope *Scope, device_names []string) (index tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"device_names": device_names} + opspec := tf.OpSpec{ + Type: "DeviceIndex", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ShardDatasetAttr is an optional argument to ShardDataset. +type ShardDatasetAttr func(optionalAttr) + +// ShardDatasetRequireNonEmpty sets the optional require_non_empty attribute to value. +// If not specified, defaults to false +func ShardDatasetRequireNonEmpty(value bool) ShardDatasetAttr { + return func(m optionalAttr) { + m["require_non_empty"] = value + } +} + +// Creates a `Dataset` that includes only 1/`num_shards` of this dataset. +// +// Arguments: +// +// num_shards: An integer representing the number of shards operating in parallel. +// index: An integer representing the current worker index. +// +// +func ShardDataset(scope *Scope, input_dataset tf.Output, num_shards tf.Output, index tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShardDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShardDataset", + Input: []tf.Input{ + input_dataset, num_shards, index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NonMaxSuppressionV5Attr is an optional argument to NonMaxSuppressionV5. +type NonMaxSuppressionV5Attr func(optionalAttr) + +// NonMaxSuppressionV5PadToMaxOutputSize sets the optional pad_to_max_output_size attribute to value. +// +// value: If true, the output `selected_indices` is padded to be of length +// `max_output_size`. Defaults to false. +// If not specified, defaults to false +func NonMaxSuppressionV5PadToMaxOutputSize(value bool) NonMaxSuppressionV5Attr { + return func(m optionalAttr) { + m["pad_to_max_output_size"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// This op also supports a Soft-NMS (with Gaussian weighting) mode (c.f. +// Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score +// of other overlapping boxes instead of directly causing them to be pruned. +// To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be +// larger than 0. +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// soft_nms_sigma: A 0-D float tensor representing the sigma parameter for Soft NMS; see Bodla et +// al (c.f. https://arxiv.org/abs/1704.04503). When `soft_nms_sigma=0.0` (which +// is default), we fall back to standard (hard) NMS. +// +// Returns: +// selected_indices: A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +// selected_scores: A 1-D float tensor of shape `[M]` representing the corresponding +// scores for each selected box, where `M <= max_output_size`. Scores only differ +// from corresponding input scores when using Soft NMS (i.e. when +// `soft_nms_sigma>0`) +// valid_outputs: A 0-D integer tensor representing the number of valid elements in +// `selected_indices`, with the valid elements appearing first. +func NonMaxSuppressionV5(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, soft_nms_sigma tf.Output, optional ...NonMaxSuppressionV5Attr) (selected_indices tf.Output, selected_scores tf.Output, valid_outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV5", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// NonMaxSuppressionV4Attr is an optional argument to NonMaxSuppressionV4. +type NonMaxSuppressionV4Attr func(optionalAttr) + +// NonMaxSuppressionV4PadToMaxOutputSize sets the optional pad_to_max_output_size attribute to value. +// +// value: If true, the output `selected_indices` is padded to be of length +// `max_output_size`. Defaults to false. +// If not specified, defaults to false +func NonMaxSuppressionV4PadToMaxOutputSize(value bool) NonMaxSuppressionV4Attr { + return func(m optionalAttr) { + m["pad_to_max_output_size"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns: +// selected_indices: A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +// valid_outputs: A 0-D integer tensor representing the number of valid elements in +// `selected_indices`, with the valid elements appearing first. +func NonMaxSuppressionV4(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, optional ...NonMaxSuppressionV4Attr) (selected_indices tf.Output, valid_outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV4", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, score_threshold, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionV3(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV3", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, score_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Note that this +// algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionV2(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV2", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NonMaxSuppressionAttr is an optional argument to NonMaxSuppression. +type NonMaxSuppressionAttr func(optionalAttr) + +// NonMaxSuppressionIouThreshold sets the optional iou_threshold attribute to value. +// +// value: A float representing the threshold for deciding whether boxes +// overlap too much with respect to IOU. +// If not specified, defaults to 0.5 +func NonMaxSuppressionIouThreshold(value float32) NonMaxSuppressionAttr { + return func(m optionalAttr) { + m["iou_threshold"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Note that this +// algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression( +// boxes, scores, max_output_size, iou_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, optional ...NonMaxSuppressionAttr) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppression", + Input: []tf.Input{ + boxes, scores, max_output_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CropAndResizeGradBoxesAttr is an optional argument to CropAndResizeGradBoxes. +type CropAndResizeGradBoxesAttr func(optionalAttr) + +// CropAndResizeGradBoxesMethod sets the optional method attribute to value. +// +// value: A string specifying the interpolation method. Only 'bilinear' is +// supported for now. +// If not specified, defaults to "bilinear" +func CropAndResizeGradBoxesMethod(value string) CropAndResizeGradBoxesAttr { + return func(m optionalAttr) { + m["method"] = value + } +} + +// Computes the gradient of the crop_and_resize op wrt the input boxes tensor. +// +// Arguments: +// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +// Both `image_height` and `image_width` need to be positive. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// +// Returns A 2-D tensor of shape `[num_boxes, 4]`. +func CropAndResizeGradBoxes(scope *Scope, grads tf.Output, image tf.Output, boxes tf.Output, box_ind tf.Output, optional ...CropAndResizeGradBoxesAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CropAndResizeGradBoxes", + Input: []tf.Input{ + grads, image, boxes, box_ind, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExtractGlimpseV2Attr is an optional argument to ExtractGlimpseV2. +type ExtractGlimpseV2Attr func(optionalAttr) + +// ExtractGlimpseV2Centered sets the optional centered attribute to value. +// +// value: indicates if the offset coordinates are centered relative to +// the image, in which case the (0, 0) offset is relative to the center +// of the input images. If false, the (0,0) offset corresponds to the +// upper left corner of the input images. +// If not specified, defaults to true +func ExtractGlimpseV2Centered(value bool) ExtractGlimpseV2Attr { + return func(m optionalAttr) { + m["centered"] = value + } +} + +// ExtractGlimpseV2Normalized sets the optional normalized attribute to value. +// +// value: indicates if the offset coordinates are normalized. +// If not specified, defaults to true +func ExtractGlimpseV2Normalized(value bool) ExtractGlimpseV2Attr { + return func(m optionalAttr) { + m["normalized"] = value + } +} + +// ExtractGlimpseV2UniformNoise sets the optional uniform_noise attribute to value. +// +// value: indicates if the noise should be generated using a +// uniform distribution or a Gaussian distribution. +// If not specified, defaults to true +func ExtractGlimpseV2UniformNoise(value bool) ExtractGlimpseV2Attr { + return func(m optionalAttr) { + m["uniform_noise"] = value + } +} + +// ExtractGlimpseV2Noise sets the optional noise attribute to value. +// +// value: indicates if the noise should `uniform`, `gaussian`, or +// `zero`. The default is `uniform` which means the the noise type +// will be decided by `uniform_noise`. +// If not specified, defaults to "uniform" +func ExtractGlimpseV2Noise(value string) ExtractGlimpseV2Attr { + return func(m optionalAttr) { + m["noise"] = value + } +} + +// Extracts a glimpse from the input tensor. +// +// Returns a set of windows called glimpses extracted at location +// `offsets` from the input tensor. If the windows only partially +// overlaps the inputs, the non overlapping areas will be filled with +// random noise. +// +// The result is a 4-D tensor of shape `[batch_size, glimpse_height, +// glimpse_width, channels]`. The channels and batch dimensions are the +// same as that of the input tensor. The height and width of the output +// windows are specified in the `size` parameter. +// +// The argument `normalized` and `centered` controls how the windows are built: +// +// * If the coordinates are normalized but not centered, 0.0 and 1.0 +// correspond to the minimum and maximum of each height and width +// dimension. +// * If the coordinates are both normalized and centered, they range from +// -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper +// left corner, the lower right corner is located at (1.0, 1.0) and the +// center is at (0, 0). +// * If the coordinates are not normalized they are interpreted as +// numbers of pixels. +// +// Arguments: +// input: A 4-D float tensor of shape `[batch_size, height, width, channels]`. +// size: A 1-D tensor of 2 elements containing the size of the glimpses +// to extract. The glimpse height must be specified first, following +// by the glimpse width. +// offsets: A 2-D integer tensor of shape `[batch_size, 2]` containing +// the y, x locations of the center of each window. +// +// Returns A tensor representing the glimpses `[batch_size, +// glimpse_height, glimpse_width, channels]`. +func ExtractGlimpseV2(scope *Scope, input tf.Output, size tf.Output, offsets tf.Output, optional ...ExtractGlimpseV2Attr) (glimpse tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExtractGlimpseV2", + Input: []tf.Input{ + input, size, offsets, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExtractGlimpseAttr is an optional argument to ExtractGlimpse. +type ExtractGlimpseAttr func(optionalAttr) + +// ExtractGlimpseCentered sets the optional centered attribute to value. +// +// value: indicates if the offset coordinates are centered relative to +// the image, in which case the (0, 0) offset is relative to the center +// of the input images. If false, the (0,0) offset corresponds to the +// upper left corner of the input images. +// If not specified, defaults to true +func ExtractGlimpseCentered(value bool) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["centered"] = value + } +} + +// ExtractGlimpseNormalized sets the optional normalized attribute to value. +// +// value: indicates if the offset coordinates are normalized. +// If not specified, defaults to true +func ExtractGlimpseNormalized(value bool) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["normalized"] = value + } +} + +// ExtractGlimpseUniformNoise sets the optional uniform_noise attribute to value. +// +// value: indicates if the noise should be generated using a +// uniform distribution or a Gaussian distribution. +// If not specified, defaults to true +func ExtractGlimpseUniformNoise(value bool) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["uniform_noise"] = value + } +} + +// ExtractGlimpseNoise sets the optional noise attribute to value. +// +// value: indicates if the noise should `uniform`, `gaussian`, or +// `zero`. The default is `uniform` which means the the noise type +// will be decided by `uniform_noise`. +// If not specified, defaults to "uniform" +func ExtractGlimpseNoise(value string) ExtractGlimpseAttr { + return func(m optionalAttr) { + m["noise"] = value + } +} + +// Extracts a glimpse from the input tensor. +// +// Returns a set of windows called glimpses extracted at location +// `offsets` from the input tensor. If the windows only partially +// overlaps the inputs, the non overlapping areas will be filled with +// random noise. +// +// The result is a 4-D tensor of shape `[batch_size, glimpse_height, +// glimpse_width, channels]`. The channels and batch dimensions are the +// same as that of the input tensor. The height and width of the output +// windows are specified in the `size` parameter. +// +// The argument `normalized` and `centered` controls how the windows are built: +// +// * If the coordinates are normalized but not centered, 0.0 and 1.0 +// correspond to the minimum and maximum of each height and width +// dimension. +// * If the coordinates are both normalized and centered, they range from +// -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper +// left corner, the lower right corner is located at (1.0, 1.0) and the +// center is at (0, 0). +// * If the coordinates are not normalized they are interpreted as +// numbers of pixels. +// +// Arguments: +// input: A 4-D float tensor of shape `[batch_size, height, width, channels]`. +// size: A 1-D tensor of 2 elements containing the size of the glimpses +// to extract. The glimpse height must be specified first, following +// by the glimpse width. +// offsets: A 2-D integer tensor of shape `[batch_size, 2]` containing +// the y, x locations of the center of each window. +// +// Returns A tensor representing the glimpses `[batch_size, +// glimpse_height, glimpse_width, channels]`. +func ExtractGlimpse(scope *Scope, input tf.Output, size tf.Output, offsets tf.Output, optional ...ExtractGlimpseAttr) (glimpse tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExtractGlimpse", + Input: []tf.Input{ + input, size, offsets, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SampleDistortedBoundingBoxAttr is an optional argument to SampleDistortedBoundingBox. +type SampleDistortedBoundingBoxAttr func(optionalAttr) + +// SampleDistortedBoundingBoxSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to non-zero, the random number +// generator is seeded by the given `seed`. Otherwise, it is seeded by a random +// seed. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// SampleDistortedBoundingBoxSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxSeed2(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// SampleDistortedBoundingBoxMinObjectCovered sets the optional min_object_covered attribute to value. +// +// value: The cropped area of the image must contain at least this +// fraction of any bounding box supplied. The value of this parameter should be +// non-negative. In the case of 0, the cropped area does not need to overlap +// any of the bounding boxes supplied. +// If not specified, defaults to 0.1 +func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["min_object_covered"] = value + } +} + +// SampleDistortedBoundingBoxAspectRatioRange sets the optional aspect_ratio_range attribute to value. +// +// value: The cropped area of the image must have an aspect ratio = +// width / height within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["aspect_ratio_range"] = value + } +} + +// SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. +// +// value: The cropped area of the image must contain a fraction of the +// supplied image within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["area_range"] = value + } +} + +// SampleDistortedBoundingBoxMaxAttempts sets the optional max_attempts attribute to value. +// +// value: Number of attempts at generating a cropped region of the image +// of the specified constraints. After `max_attempts` failures, return the entire +// image. +// If not specified, defaults to 100 +func SampleDistortedBoundingBoxMaxAttempts(value int64) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["max_attempts"] = value + } +} + +// SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// +// value: Controls behavior if no bounding boxes supplied. +// If true, assume an implicit bounding box covering the whole input. If false, +// raise an error. +// If not specified, defaults to false +func SampleDistortedBoundingBoxUseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxAttr { + return func(m optionalAttr) { + m["use_image_if_no_bounding_boxes"] = value + } +} + +// Generate a single randomly distorted bounding box for an image. +// +// Bounding box annotations are often supplied in addition to ground-truth labels +// in image recognition or object localization tasks. A common technique for +// training such a system is to randomly distort an image while preserving +// its content, i.e. *data augmentation*. This Op outputs a randomly distorted +// localization of an object, i.e. bounding box, given an `image_size`, +// `bounding_boxes` and a series of constraints. +// +// The output of this Op is a single bounding box that may be used to crop the +// original image. The output is returned as 3 tensors: `begin`, `size` and +// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the +// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize +// what the bounding box looks like. +// +// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, +// +// ```python +// # Generate a single distorted bounding box. +// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( +// tf.shape(image), +// bounding_boxes=bounding_boxes) +// +// # Draw the bounding box in an image summary. +// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), +// bbox_for_draw) +// tf.summary.image('images_with_box', image_with_box) +// +// # Employ the bounding box to distort the image. +// distorted_image = tf.slice(image, begin, size) +// ``` +// +// Note that if no bounding box information is available, setting +// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit +// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is +// false and no bounding boxes are supplied, an error is raised. +// +// Arguments: +// image_size: 1-D, containing `[height, width, channels]`. +// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes +// associated with the image. +// +// Returns: +// begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to +// `tf.slice`. +// size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to +// `tf.slice`. +// bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box. +// Provide as input to `tf.image.draw_bounding_boxes`. +func SampleDistortedBoundingBox(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, optional ...SampleDistortedBoundingBoxAttr) (begin tf.Output, size tf.Output, bboxes tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SampleDistortedBoundingBox", + Input: []tf.Input{ + image_size, bounding_boxes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Draw bounding boxes on a batch of images. +// +// Outputs a copy of `images` but draws on top of the pixels zero or more bounding +// boxes specified by the locations in `boxes`. The coordinates of the each +// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, if an image is 100 x 200 pixels (height x width) and the bounding +// box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of +// the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates). +// +// Parts of the bounding box may fall outside the image. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, depth]`. A batch of images. +// boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding +// boxes. +// colors: 2-D. A list of RGBA colors to cycle through for the boxes. +// +// Returns 4-D with the same shape as `images`. The batch of input images with +// bounding boxes drawn on the images. +func DrawBoundingBoxesV2(scope *Scope, images tf.Output, boxes tf.Output, colors tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DrawBoundingBoxesV2", + Input: []tf.Input{ + images, boxes, colors, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Draw bounding boxes on a batch of images. +// +// Outputs a copy of `images` but draws on top of the pixels zero or more bounding +// boxes specified by the locations in `boxes`. The coordinates of the each +// bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, if an image is 100 x 200 pixels (height x width) and the bounding +// box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of +// the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates). +// +// Parts of the bounding box may fall outside the image. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, depth]`. A batch of images. +// boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding +// boxes. +// +// Returns 4-D with the same shape as `images`. The batch of input images with +// bounding boxes drawn on the images. +func DrawBoundingBoxes(scope *Scope, images tf.Output, boxes tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DrawBoundingBoxes", + Input: []tf.Input{ + images, boxes, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Convert one or more images from HSV to RGB. +// +// Outputs a tensor of the same shape as the `images` tensor, containing the RGB +// value of the pixels. The output is only well defined if the value in `images` +// are in `[0,1]`. +// +// See `rgb_to_hsv` for a description of the HSV encoding. +// +// Arguments: +// images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3. +// +// Returns `images` converted to RGB. +func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "HSVToRGB", + Input: []tf.Input{ + images, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts one or more images from RGB to HSV. +// +// Outputs a tensor of the same shape as the `images` tensor, containing the HSV +// value of the pixels. The output is only well defined if the value in `images` +// are in `[0,1]`. +// +// `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and +// `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0 +// corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. +// +// Usage Example: +// +// >>> blue_image = tf.stack([ +// ... tf.zeros([5,5]), +// ... tf.zeros([5,5]), +// ... tf.ones([5,5])], +// ... axis=-1) +// >>> blue_hsv_image = tf.image.rgb_to_hsv(blue_image) +// >>> blue_hsv_image[0,0].numpy() +// array([0.6666667, 1. , 1. ], dtype=float32) +// +// +// Arguments: +// images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3. +// +// Returns `images` converted to HSV. +func RGBToHSV(scope *Scope, images tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RGBToHSV", + Input: []tf.Input{ + images, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Decode the frame(s) of a GIF-encoded image to a uint8 tensor. +// +// GIF images with frame or transparency compression are not supported. +// On Linux and MacOS systems, convert animated GIFs from compressed to +// uncompressed by running: +// +// convert $src.gif -coalesce $dst.gif +// +// This op also supports decoding JPEGs and PNGs, though it is cleaner to use +// `tf.io.decode_image`. +// +// Arguments: +// contents: 0-D. The GIF-encoded image. +// +// Returns 4-D with shape `[num_frames, height, width, 3]`. RGB channel order. +func DecodeGif(scope *Scope, contents tf.Output) (image tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DecodeGif", + Input: []tf.Input{ + contents, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeBmpAttr is an optional argument to DecodeBmp. +type DecodeBmpAttr func(optionalAttr) + +// DecodeBmpChannels sets the optional channels attribute to value. +// If not specified, defaults to 0 +func DecodeBmpChannels(value int64) DecodeBmpAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// Decode the first frame of a BMP-encoded image to a uint8 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the BMP-encoded image. +// * 3: output an RGB image. +// * 4: output an RGBA image. +// +// Arguments: +// contents: 0-D. The BMP-encoded image. +// +// Returns 3-D with shape `[height, width, channels]`. RGB order +func DecodeBmp(scope *Scope, contents tf.Output, optional ...DecodeBmpAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeBmp", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EncodePngAttr is an optional argument to EncodePng. +type EncodePngAttr func(optionalAttr) + +// EncodePngCompression sets the optional compression attribute to value. +// +// value: Compression level. +// If not specified, defaults to -1 +func EncodePngCompression(value int64) EncodePngAttr { + return func(m optionalAttr) { + m["compression"] = value + } +} + +// PNG-encode an image. +// +// `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]` +// where `channels` is: +// +// * 1: for grayscale. +// * 2: for grayscale + alpha. +// * 3: for RGB. +// * 4: for RGBA. +// +// The ZLIB compression level, `compression`, can be -1 for the PNG-encoder +// default or a value from 0 to 9. 9 is the highest compression level, generating +// the smallest output, but is slower. +// +// Arguments: +// image: 3-D with shape `[height, width, channels]`. +// +// Returns 0-D. PNG-encoded image. +func EncodePng(scope *Scope, image tf.Output, optional ...EncodePngAttr) (contents tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodePng", + Input: []tf.Input{ + image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010. +// +// Flip each bit of supported types. For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101. +// This operation is performed on each element of the tensor argument `x`. +// +// Example: +// ```python +// import tensorflow as tf +// from tensorflow.python.ops import bitwise_ops +// +// # flip 2 (00000010) to -3 (11111101) +// tf.assert_equal(-3, bitwise_ops.invert(2)) +// +// dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, +// dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64] +// +// inputs = [0, 5, 3, 14] +// for dtype in dtype_list: +// # Because of issues with negative numbers, let's test this indirectly. +// # 1. invert(a) and a = 0 +// # 2. invert(a) or a = invert(0) +// input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype) +// not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and( +// input_tensor, bitwise_ops.invert(input_tensor)), +// bitwise_ops.bitwise_or( +// input_tensor, bitwise_ops.invert(input_tensor)), +// bitwise_ops.invert( +// tf.constant(0, dtype=dtype))] +// +// expected = tf.constant([0, 0, 0, 0], dtype=tf.float32) +// tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected) +// +// expected = tf.cast([not_0] * 4, tf.float32) +// tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected) +// +// # For unsigned dtypes let's also check the result directly. +// if dtype.is_unsigned: +// inverted = bitwise_ops.invert(input_tensor) +// expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32) +// tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32)) +// ``` +func Invert(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Invert", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodePngAttr is an optional argument to DecodePng. +type DecodePngAttr func(optionalAttr) + +// DecodePngChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodePngChannels(value int64) DecodePngAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// DecodePngDtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_UINT8 +func DecodePngDtype(value tf.DataType) DecodePngAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Decode a PNG-encoded image to a uint8 or uint16 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the PNG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. +// * 4: output an RGBA image. +// +// If needed, the PNG-encoded image is transformed to match the requested number +// of color channels. +// +// This op also supports decoding JPEGs and non-animated GIFs since the interface +// is the same, though it is cleaner to use `tf.io.decode_image`. +// +// Arguments: +// contents: 0-D. The PNG-encoded image. +// +// Returns 3-D with shape `[height, width, channels]`. +func DecodePng(scope *Scope, contents tf.Output, optional ...DecodePngAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodePng", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adjust the saturation of one or more images. +// +// `images` is a tensor of at least 3 dimensions. The last dimension is +// interpreted as channels, and must be three. +// +// The input image is considered in the RGB colorspace. Conceptually, the RGB +// colors are first mapped into HSV. A scale is then applied all the saturation +// values, and then remapped back to RGB colorspace. +// +// Arguments: +// images: Images to adjust. At least 3-D. +// scale: A float scale to add to the saturation. +// +// Returns The hue-adjusted image or images. +func AdjustSaturation(scope *Scope, images tf.Output, scale tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustSaturation", + Input: []tf.Input{ + images, scale, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adjust the contrast of one or more images. +// +// `images` is a tensor of at least 3 dimensions. The last 3 dimensions are +// interpreted as `[height, width, channels]`. The other dimensions only +// represent a collection of images, such as `[batch, height, width, channels].` +// +// Contrast is adjusted independently for each channel of each image. +// +// For each channel, the Op first computes the mean of the image pixels in the +// channel and then adjusts each component of each pixel to +// `(x - mean) * contrast_factor + mean`. +// +// Arguments: +// images: Images to adjust. At least 3-D. +// contrast_factor: A float multiplier for adjusting contrast. +// +// Returns The contrast-adjusted image or images. +func AdjustContrastv2(scope *Scope, images tf.Output, contrast_factor tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustContrastv2", + Input: []tf.Input{ + images, contrast_factor, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Initializes the multi device iterator with the given dataset. +// +// Arguments: +// dataset: Dataset to be iterated upon. +// multi_device_iterator: A MultiDeviceIteratorResource. +// max_buffer_size: The maximum size of the host side per device buffer to keep. +// +// Returns An int64 indicating which incarnation of the MultiDeviceIterator +// is running. +func MultiDeviceIteratorInit(scope *Scope, dataset tf.Output, multi_device_iterator tf.Output, max_buffer_size tf.Output) (incarnation_id tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MultiDeviceIteratorInit", + Input: []tf.Input{ + dataset, multi_device_iterator, max_buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Disallowed in GraphDef version >= 2. +// +// DEPRECATED at GraphDef version 2: Use AdjustContrastv2 instead +func AdjustContrast(scope *Scope, images tf.Output, contrast_factor tf.Output, min_value tf.Output, max_value tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustContrast", + Input: []tf.Input{ + images, contrast_factor, min_value, max_value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExtractJpegShapeAttr is an optional argument to ExtractJpegShape. +type ExtractJpegShapeAttr func(optionalAttr) + +// ExtractJpegShapeOutputType sets the optional output_type attribute to value. +// +// value: (Optional) The output type of the operation (int32 or int64). +// Defaults to int32. +// If not specified, defaults to DT_INT32 +func ExtractJpegShapeOutputType(value tf.DataType) ExtractJpegShapeAttr { + return func(m optionalAttr) { + m["output_type"] = value + } +} + +// Extract the shape information of a JPEG-encoded image. +// +// This op only parses the image header, so it is much faster than DecodeJpeg. +// +// Arguments: +// contents: 0-D. The JPEG-encoded image. +// +// Returns 1-D. The image shape with format [height, width, channels]. +func ExtractJpegShape(scope *Scope, contents tf.Output, optional ...ExtractJpegShapeAttr) (image_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExtractJpegShape", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// JPEG encode input image with provided compression quality. +// +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// `quality` is an int32 jpeg compression quality value between 0 and 100. +// +// +// Arguments: +// images: Images to adjust. At least 3-D. +// quality: An int quality to encode to. +// +// Returns 0-D. JPEG-encoded image. +func EncodeJpegVariableQuality(scope *Scope, images tf.Output, quality tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EncodeJpegVariableQuality", + Input: []tf.Input{ + images, quality, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the rank of a tensor. +// +// This operation returns an integer representing the rank of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// # shape of tensor 't' is [2, 2, 3] +// rank(t) ==> 3 +// ``` +// +// **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank +// of a tensor is the number of indices required to uniquely select each element +// of the tensor. Rank is also known as "order", "degree", or "ndims." +func Rank(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Rank", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EncodeJpegAttr is an optional argument to EncodeJpeg. +type EncodeJpegAttr func(optionalAttr) + +// EncodeJpegFormat sets the optional format attribute to value. +// +// value: Per pixel image format. +// If not specified, defaults to "" +func EncodeJpegFormat(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["format"] = value + } +} + +// EncodeJpegQuality sets the optional quality attribute to value. +// +// value: Quality of the compression from 0 to 100 (higher is better and slower). +// If not specified, defaults to 95 +func EncodeJpegQuality(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["quality"] = value + } +} + +// EncodeJpegProgressive sets the optional progressive attribute to value. +// +// value: If True, create a JPEG that loads progressively (coarse to fine). +// If not specified, defaults to false +func EncodeJpegProgressive(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["progressive"] = value + } +} + +// EncodeJpegOptimizeSize sets the optional optimize_size attribute to value. +// +// value: If True, spend CPU/RAM to reduce size with no quality change. +// If not specified, defaults to false +func EncodeJpegOptimizeSize(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["optimize_size"] = value + } +} + +// EncodeJpegChromaDownsampling sets the optional chroma_downsampling attribute to value. +// +// value: See http://en.wikipedia.org/wiki/Chroma_subsampling. +// If not specified, defaults to true +func EncodeJpegChromaDownsampling(value bool) EncodeJpegAttr { + return func(m optionalAttr) { + m["chroma_downsampling"] = value + } +} + +// EncodeJpegDensityUnit sets the optional density_unit attribute to value. +// +// value: Unit used to specify `x_density` and `y_density`: +// pixels per inch (`'in'`) or centimeter (`'cm'`). +// If not specified, defaults to "in" +func EncodeJpegDensityUnit(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["density_unit"] = value + } +} + +// EncodeJpegXDensity sets the optional x_density attribute to value. +// +// value: Horizontal pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegXDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["x_density"] = value + } +} + +// EncodeJpegYDensity sets the optional y_density attribute to value. +// +// value: Vertical pixels per density unit. +// If not specified, defaults to 300 +func EncodeJpegYDensity(value int64) EncodeJpegAttr { + return func(m optionalAttr) { + m["y_density"] = value + } +} + +// EncodeJpegXmpMetadata sets the optional xmp_metadata attribute to value. +// +// value: If not empty, embed this XMP metadata in the image header. +// If not specified, defaults to "" +func EncodeJpegXmpMetadata(value string) EncodeJpegAttr { + return func(m optionalAttr) { + m["xmp_metadata"] = value + } +} + +// JPEG-encode an image. +// +// `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. +// +// The attr `format` can be used to override the color format of the encoded +// output. Values can be: +// +// * `''`: Use a default format based on the number of channels in the image. +// * `grayscale`: Output a grayscale JPEG image. The `channels` dimension +// of `image` must be 1. +// * `rgb`: Output an RGB JPEG image. The `channels` dimension +// of `image` must be 3. +// +// If `format` is not specified or is the empty string, a default format is picked +// in function of the number of channels in `image`: +// +// * 1: Output a grayscale image. +// * 3: Output an RGB image. +// +// Arguments: +// image: 3-D with shape `[height, width, channels]`. +// +// Returns 0-D. JPEG-encoded image. +func EncodeJpeg(scope *Scope, image tf.Output, optional ...EncodeJpegAttr) (contents tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodeJpeg", + Input: []tf.Input{ + image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeAndCropJpegAttr is an optional argument to DecodeAndCropJpeg. +type DecodeAndCropJpegAttr func(optionalAttr) + +// DecodeAndCropJpegChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodeAndCropJpegChannels(value int64) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// DecodeAndCropJpegRatio sets the optional ratio attribute to value. +// +// value: Downscaling ratio. +// If not specified, defaults to 1 +func DecodeAndCropJpegRatio(value int64) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["ratio"] = value + } +} + +// DecodeAndCropJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). +// If not specified, defaults to true +func DecodeAndCropJpegFancyUpscaling(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["fancy_upscaling"] = value + } +} + +// DecodeAndCropJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// +// value: If true try to recover an image from truncated input. +// If not specified, defaults to false +func DecodeAndCropJpegTryRecoverTruncated(value bool) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["try_recover_truncated"] = value + } +} + +// DecodeAndCropJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeAndCropJpegAcceptableFraction(value float32) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} + +// DecodeAndCropJpegDctMethod sets the optional dct_method attribute to value. +// +// value: string specifying a hint about the algorithm used for +// decompression. Defaults to "" which maps to a system-specific +// default. Currently valid values are ["INTEGER_FAST", +// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal +// jpeg library changes to a version that does not have that specific +// option.) +// If not specified, defaults to "" +func DecodeAndCropJpegDctMethod(value string) DecodeAndCropJpegAttr { + return func(m optionalAttr) { + m["dct_method"] = value + } +} + +// Decode and Crop a JPEG-encoded image to a uint8 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the JPEG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. +// +// If needed, the JPEG-encoded image is transformed to match the requested number +// of color channels. +// +// The attr `ratio` allows downscaling the image by an integer factor during +// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +// downscaling the image later. +// +// +// It is equivalent to a combination of decode and crop, but much faster by only +// decoding partial jpeg image. +// +// Arguments: +// contents: 0-D. The JPEG-encoded image. +// crop_window: 1-D. The crop window: [crop_y, crop_x, crop_height, crop_width]. +// +// Returns 3-D with shape `[height, width, channels]`.. +func DecodeAndCropJpeg(scope *Scope, contents tf.Output, crop_window tf.Output, optional ...DecodeAndCropJpegAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeAndCropJpeg", + Input: []tf.Input{ + contents, crop_window, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomCropAttr is an optional argument to RandomCrop. +type RandomCropAttr func(optionalAttr) + +// RandomCropSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomCropSeed(value int64) RandomCropAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomCropSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomCropSeed2(value int64) RandomCropAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Randomly crop `image`. +// +// DEPRECATED at GraphDef version 8: Random crop is now pure Python +// +// `size` is a 1-D int64 tensor with 2 elements representing the crop height and +// width. The values must be non negative. +// +// This Op picks a random location in `image` and crops a `height` by `width` +// rectangle from that location. The random location is picked so the cropped +// area will fit inside the original image. +// +// Arguments: +// image: 3-D of shape `[height, width, channels]`. +// size: 1-D of length 2 containing: `crop_height`, `crop_width`.. +// +// Returns 3-D of shape `[crop_height, crop_width, channels].` +func RandomCrop(scope *Scope, image tf.Output, size tf.Output, optional ...RandomCropAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomCrop", + Input: []tf.Input{ + image, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeNearestNeighborGradAttr is an optional argument to ResizeNearestNeighborGrad. +type ResizeNearestNeighborGradAttr func(optionalAttr) + +// ResizeNearestNeighborGradAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and grad tensors are +// aligned. Defaults to false. +// If not specified, defaults to false +func ResizeNearestNeighborGradAlignCorners(value bool) ResizeNearestNeighborGradAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeNearestNeighborGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeNearestNeighborGradHalfPixelCenters(value bool) ResizeNearestNeighborGradAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Computes the gradient of nearest neighbor interpolation. +// +// Arguments: +// grads: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The +// original input size. +// +// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients +// with respect to the input image. +func ResizeNearestNeighborGrad(scope *Scope, grads tf.Output, size tf.Output, optional ...ResizeNearestNeighborGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeNearestNeighborGrad", + Input: []tf.Input{ + grads, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Runs multiple additive regression ensemble predictors on input instances and +// +// computes the logits. It is designed to be used during prediction. +// It traverses all the trees and calculates the final score for each instance. +// +// Arguments: +// +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for partial logits +// shape. +// +// Returns Output rank 2 Tensor containing logits for each example. +func BoostedTreesPredict(scope *Scope, tree_ensemble_handle tf.Output, bucketized_features []tf.Output, logits_dimension int64) (logits tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesPredict", + Input: []tf.Input{ + tree_ensemble_handle, tf.OutputList(bucketized_features), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomGammaAttr is an optional argument to RandomGamma. +type RandomGammaAttr func(optionalAttr) + +// RandomGammaSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomGammaSeed(value int64) RandomGammaAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomGammaSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomGammaSeed2(value int64) RandomGammaAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from the Gamma distribution(s) described by alpha. +// +// This op uses the algorithm by Marsaglia et al. to acquire samples via +// transformation-rejection from pairs of uniform and normal random variables. +// See http://dl.acm.org/citation.cfm?id=358414 +// +// Arguments: +// shape: 1-D integer tensor. Shape of independent samples to draw from each +// distribution described by the shape parameters given in alpha. +// alpha: A tensor in which each scalar is a "shape" parameter describing the +// associated gamma distribution. +// +// Returns A tensor with shape `shape + shape(alpha)`. Each slice +// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for +// `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha. +func RandomGamma(scope *Scope, shape tf.Output, alpha tf.Output, optional ...RandomGammaAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomGamma", + Input: []tf.Input{ + shape, alpha, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise. +func Xlog1py(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Xlog1py", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedResizeBilinearAttr is an optional argument to QuantizedResizeBilinear. +type QuantizedResizeBilinearAttr func(optionalAttr) + +// QuantizedResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func QuantizedResizeBilinearAlignCorners(value bool) QuantizedResizeBilinearAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// QuantizedResizeBilinearHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func QuantizedResizeBilinearHalfPixelCenters(value bool) QuantizedResizeBilinearAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize quantized `images` to `size` using quantized bilinear interpolation. +// +// Input images and output images must be quantized types. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// +// +// Returns: +// resized_images: 4-D with shape +// `[batch, new_height, new_width, channels]`. +// out_min +// out_max +func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min tf.Output, max tf.Output, optional ...QuantizedResizeBilinearAttr) (resized_images tf.Output, out_min tf.Output, out_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedResizeBilinear", + Input: []tf.Input{ + images, size, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResizeAreaAttr is an optional argument to ResizeArea. +type ResizeAreaAttr func(optionalAttr) + +// ResizeAreaAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeAreaAlignCorners(value bool) ResizeAreaAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// Resize `images` to `size` using area interpolation. +// +// Input images can be of different types but output images are always float. +// +// The range of pixel values for the output image might be slightly different +// from the range for the input image because of limited numerical precision. +// To guarantee an output range, for example `[0.0, 1.0]`, apply +// `tf.clip_by_value` to the output. +// +// Each output pixel is computed by first transforming the pixel's footprint into +// the input tensor and then averaging the pixels that intersect the footprint. An +// input pixel's contribution to the average is weighted by the fraction of its +// area that intersects the footprint. This is the same as OpenCV's INTER_AREA. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeArea(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeAreaAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeArea", + Input: []tf.Input{ + images, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Restore a reader to a previously saved state. +// +// Not all Readers support being restored, so this can produce an +// Unimplemented error. +// +// Arguments: +// reader_handle: Handle to a Reader. +// state: Result of a ReaderSerializeState of a Reader with type +// matching reader_handle. +// +// Returns the created operation. +func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderRestoreStateV2", + Input: []tf.Input{ + reader_handle, state, + }, + } + return scope.AddOperation(opspec) +} + +// Computes rectified linear 6: `min(max(features, 0), 6)`. +func Relu6(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu6", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RaggedRangeAttr is an optional argument to RaggedRange. +type RaggedRangeAttr func(optionalAttr) + +// RaggedRangeTsplits sets the optional Tsplits attribute to value. +// If not specified, defaults to DT_INT64 +func RaggedRangeTsplits(value tf.DataType) RaggedRangeAttr { + return func(m optionalAttr) { + m["Tsplits"] = value + } +} + +// Returns a `RaggedTensor` containing the specified sequences of numbers. +// +// +// Returns a `RaggedTensor` `result` composed from `rt_dense_values` and +// `rt_nested_splits`, such that +// `result[i] = range(starts[i], limits[i], deltas[i])`. +// +// ```python +// (rt_nested_splits, rt_dense_values) = ragged_range( +// starts=[2, 5, 8], limits=[3, 5, 12], deltas=1) +// result = tf.ragged.from_row_splits(rt_dense_values, rt_nested_splits) +// print(result) +// +// ``` +// +// The input tensors `starts`, `limits`, and `deltas` may be scalars or vectors. +// The vector inputs must all have the same size. Scalar inputs are broadcast +// to match the size of the vector inputs. +// +// Arguments: +// starts: The starts of each range. +// limits: The limits of each range. +// deltas: The deltas of each range. +// +// Returns: +// rt_nested_splits: The `row_splits` for the returned `RaggedTensor`. +// rt_dense_values: The `flat_values` for the returned `RaggedTensor`. +func RaggedRange(scope *Scope, starts tf.Output, limits tf.Output, deltas tf.Output, optional ...RaggedRangeAttr) (rt_nested_splits tf.Output, rt_dense_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RaggedRange", + Input: []tf.Input{ + starts, limits, deltas, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Split a `SparseTensor` into `num_split` tensors along one dimension. +// +// If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices +// `[0 : shape[split_dim] % num_split]` gets one extra dimension. +// For example, if `split_dim = 1` and `num_split = 2` and the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// output_tensor[0] = shape = [2, 4] +// [ a ] +// [b c ] +// +// output_tensor[1] = shape = [2, 3] +// [ d e ] +// [ ] +// +// Arguments: +// split_dim: 0-D. The dimension along which to split. Must be in the range +// `[0, rank(shape))`. +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. +// num_split: The number of ways to split. +// +// Returns: +// output_indices +// output_values: A list of 1-D tensors represents the values of the output sparse +// tensors. +// output_shape: A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSplit(scope *Scope, split_dim tf.Output, indices tf.Output, values tf.Output, shape tf.Output, num_split int64) (output_indices []tf.Output, output_values []tf.Output, output_shape []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_split": num_split} + opspec := tf.OpSpec{ + Type: "SparseSplit", + Input: []tf.Input{ + split_dim, indices, values, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output_indices, idx, err = makeOutputList(op, idx, "output_indices"); err != nil { + scope.UpdateErr("SparseSplit", err) + return + } + if output_values, idx, err = makeOutputList(op, idx, "output_values"); err != nil { + scope.UpdateErr("SparseSplit", err) + return + } + if output_shape, idx, err = makeOutputList(op, idx, "output_shape"); err != nil { + scope.UpdateErr("SparseSplit", err) + return + } + return output_indices, output_values, output_shape +} + +// Produce a string tensor that encodes the state of a Reader. +// +// Not all Readers support being serialized, so this can produce an +// Unimplemented error. +// +// Arguments: +// reader_handle: Handle to a Reader. +func ReaderSerializeStateV2(scope *Scope, reader_handle tf.Output) (state tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderSerializeStateV2", + Input: []tf.Input{ + reader_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns up to `num_records` (key, value) pairs produced by a Reader. +// +// Will dequeue from the input queue if necessary (e.g. when the +// Reader needs to start reading from a new file since it has finished +// with the previous file). +// It may return less than `num_records` even before the last batch. +// +// Arguments: +// reader_handle: Handle to a `Reader`. +// queue_handle: Handle to a `Queue`, with string work items. +// num_records: number of records to read from `Reader`. +// +// Returns: +// keys: A 1-D tensor. +// values: A 1-D tensor. +func ReaderReadUpToV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output, num_records tf.Output) (keys tf.Output, values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderReadUpToV2", + Input: []tf.Input{ + reader_handle, queue_handle, num_records, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// QueueDequeueV2Attr is an optional argument to QueueDequeueV2. +type QueueDequeueV2Attr func(optionalAttr) + +// QueueDequeueV2TimeoutMs sets the optional timeout_ms attribute to value. +// +// value: If the queue is empty, this operation will block for up to +// timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueDequeueV2TimeoutMs(value int64) QueueDequeueV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Dequeues a tuple of one or more tensors from the given queue. +// +// This operation has k outputs, where k is the number of components +// in the tuples stored in the given queue, and output i is the ith +// component of the dequeued tuple. +// +// N.B. If the queue is empty, this operation will block until an element +// has been dequeued (or 'timeout_ms' elapses, if specified). +// +// Arguments: +// handle: The handle to a queue. +// component_types: The type of each component in a tuple. +// +// Returns One or more tensors that were dequeued as a tuple. +func QueueDequeueV2(scope *Scope, handle tf.Output, component_types []tf.DataType, optional ...QueueDequeueV2Attr) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueDequeueV2", + Input: []tf.Input{ + handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("QueueDequeueV2", err) + return + } + return components +} + +// Return a slice from 'input'. +// +// The output tensor is a tensor with dimensions described by 'size' +// whose values are extracted from 'input' starting at the offsets in +// 'begin'. +// +// *Requirements*: +// 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n) +// +// Arguments: +// +// begin: begin[i] specifies the offset into the 'i'th dimension of +// 'input' to slice from. +// size: size[i] specifies the number of elements of the 'i'th dimension +// of 'input' to slice. If size[i] is -1, all remaining elements in dimension +// i are included in the slice (i.e. this is equivalent to setting +// size[i] = input.dim_size(i) - begin[i]). +func Slice(scope *Scope, input tf.Output, begin tf.Output, size tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Slice", + Input: []tf.Input{ + input, begin, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TFRecordReaderV2Attr is an optional argument to TFRecordReaderV2. +type TFRecordReaderV2Attr func(optionalAttr) + +// TFRecordReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func TFRecordReaderV2Container(value string) TFRecordReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// TFRecordReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func TFRecordReaderV2SharedName(value string) TFRecordReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// TFRecordReaderV2CompressionType sets the optional compression_type attribute to value. +// If not specified, defaults to "" +func TFRecordReaderV2CompressionType(value string) TFRecordReaderV2Attr { + return func(m optionalAttr) { + m["compression_type"] = value + } +} + +// A Reader that outputs the records from a TensorFlow Records file. +// +// Returns The handle to reference the Reader. +func TFRecordReaderV2(scope *Scope, optional ...TFRecordReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TFRecordReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParseExampleDatasetAttr is an optional argument to ParseExampleDataset. +type ParseExampleDatasetAttr func(optionalAttr) + +// ParseExampleDatasetSloppy sets the optional sloppy attribute to value. +// If not specified, defaults to false +func ParseExampleDatasetSloppy(value bool) ParseExampleDatasetAttr { + return func(m optionalAttr) { + m["sloppy"] = value + } +} + +// ParseExampleDatasetRaggedKeys sets the optional ragged_keys attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseExampleDatasetRaggedKeys(value []string) ParseExampleDatasetAttr { + return func(m optionalAttr) { + m["ragged_keys"] = value + } +} + +// ParseExampleDatasetRaggedValueTypes sets the optional ragged_value_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseExampleDatasetRaggedValueTypes(value []tf.DataType) ParseExampleDatasetAttr { + return func(m optionalAttr) { + m["ragged_value_types"] = value + } +} + +// ParseExampleDatasetRaggedSplitTypes sets the optional ragged_split_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseExampleDatasetRaggedSplitTypes(value []tf.DataType) ParseExampleDatasetAttr { + return func(m optionalAttr) { + m["ragged_split_types"] = value + } +} + +// Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. +// +// Arguments: +// +// +// dense_defaults: A dict mapping string keys to `Tensor`s. +// The keys of the dict must match the dense_keys of the feature. +// sparse_keys: A list of string keys in the examples features. +// The results for these keys will be returned as `SparseTensor` objects. +// dense_keys: A list of Ndense string Tensors (scalars). +// The keys expected in the Examples features associated with dense values. +// sparse_types: A list of `DTypes` of the same length as `sparse_keys`. +// Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), +// and `tf.string` (`BytesList`) are supported. +// dense_shapes: List of tuples with the same length as `dense_keys`. +// The shape of the data for each dense feature referenced by `dense_keys`. +// Required for any input tensors identified by `dense_keys`. Must be +// either fully defined, or may contain an unknown first dimension. +// An unknown first dimension means the feature is treated as having +// a variable number of blocks, and the output shape along this dimension +// is considered unknown at graph build time. Padding is applied for +// minibatch elements smaller than the maximum number of blocks for the +// given feature along this dimension. +// output_types: The type list for the return values. +// output_shapes: The list of shapes being produced. +func ParseExampleDataset(scope *Scope, input_dataset tf.Output, num_parallel_calls tf.Output, dense_defaults []tf.Output, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ParseExampleDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes, "output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParseExampleDataset", + Input: []tf.Input{ + input_dataset, num_parallel_calls, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// IdentityReaderV2Attr is an optional argument to IdentityReaderV2. +type IdentityReaderV2Attr func(optionalAttr) + +// IdentityReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func IdentityReaderV2Container(value string) IdentityReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// IdentityReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func IdentityReaderV2SharedName(value string) IdentityReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A Reader that outputs the queued work as both the key and value. +// +// To use, enqueue strings in a Queue. ReaderRead will take the front +// work string and output (work, work). +// +// Returns The handle to reference the Reader. +func IdentityReaderV2(scope *Scope, optional ...IdentityReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IdentityReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FixedLengthRecordReaderV2Attr is an optional argument to FixedLengthRecordReaderV2. +type FixedLengthRecordReaderV2Attr func(optionalAttr) + +// FixedLengthRecordReaderV2HeaderBytes sets the optional header_bytes attribute to value. +// +// value: Number of bytes in the header, defaults to 0. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2HeaderBytes(value int64) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["header_bytes"] = value + } +} + +// FixedLengthRecordReaderV2FooterBytes sets the optional footer_bytes attribute to value. +// +// value: Number of bytes in the footer, defaults to 0. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2FooterBytes(value int64) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["footer_bytes"] = value + } +} + +// FixedLengthRecordReaderV2HopBytes sets the optional hop_bytes attribute to value. +// +// value: Number of bytes to hop before each read. Default of 0 means using +// record_bytes. +// If not specified, defaults to 0 +func FixedLengthRecordReaderV2HopBytes(value int64) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["hop_bytes"] = value + } +} + +// FixedLengthRecordReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2Container(value string) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// FixedLengthRecordReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2SharedName(value string) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// FixedLengthRecordReaderV2Encoding sets the optional encoding attribute to value. +// +// value: The type of encoding for the file. Currently ZLIB and GZIP +// are supported. Defaults to none. +// If not specified, defaults to "" +func FixedLengthRecordReaderV2Encoding(value string) FixedLengthRecordReaderV2Attr { + return func(m optionalAttr) { + m["encoding"] = value + } +} + +// A Reader that outputs fixed-length records from a file. +// +// Arguments: +// record_bytes: Number of bytes in the record. +// +// Returns The handle to reference the Reader. +func FixedLengthRecordReaderV2(scope *Scope, record_bytes int64, optional ...FixedLengthRecordReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"record_bytes": record_bytes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FixedLengthRecordReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Saves the input tensors to disk. +// +// The size of `tensor_names` must match the number of tensors in `data`. `data[i]` +// is written to `filename` with name `tensor_names[i]`. +// +// See also `SaveSlices`. +// +// Arguments: +// filename: Must have a single element. The name of the file to which we write +// the tensor. +// tensor_names: Shape `[N]`. The names of the tensors to be saved. +// data: `N` tensors to save. +// +// Returns the created operation. +func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Save", + Input: []tf.Input{ + filename, tensor_names, tf.OutputList(data), + }, + } + return scope.AddOperation(opspec) +} + +// DatasetToGraphV2Attr is an optional argument to DatasetToGraphV2. +type DatasetToGraphV2Attr func(optionalAttr) + +// DatasetToGraphV2ExternalStatePolicy sets the optional external_state_policy attribute to value. +// If not specified, defaults to 0 +func DatasetToGraphV2ExternalStatePolicy(value int64) DatasetToGraphV2Attr { + return func(m optionalAttr) { + m["external_state_policy"] = value + } +} + +// DatasetToGraphV2StripDeviceAssignment sets the optional strip_device_assignment attribute to value. +// If not specified, defaults to false +func DatasetToGraphV2StripDeviceAssignment(value bool) DatasetToGraphV2Attr { + return func(m optionalAttr) { + m["strip_device_assignment"] = value + } +} + +// Returns a serialized GraphDef representing `input_dataset`. +// +// Returns a graph representation for `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to return the graph representation for. +// +// Returns The graph representation of the dataset (as serialized GraphDef). +func DatasetToGraphV2(scope *Scope, input_dataset tf.Output, optional ...DatasetToGraphV2Attr) (graph tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DatasetToGraphV2", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Restores tensors from a V2 checkpoint. +// +// For backward compatibility with the V1 format, this Op currently allows +// restoring from a V1 checkpoint as well: +// - This Op first attempts to find the V2 index file pointed to by "prefix", and +// if found proceed to read it as a V2 checkpoint; +// - Otherwise the V1 read path is invoked. +// Relying on this behavior is not recommended, as the ability to fall back to read +// V1 might be deprecated and eventually removed. +// +// By default, restores the named tensors in full. If the caller wishes to restore +// specific slices of stored tensors, "shape_and_slices" should be non-empty +// strings and correspondingly well-formed. +// +// Callers must ensure all the named tensors are indeed stored in the checkpoint. +// +// Arguments: +// prefix: Must have a single element. The prefix of a V2 checkpoint. +// tensor_names: shape {N}. The names of the tensors to be restored. +// shape_and_slices: shape {N}. The slice specs of the tensors to be restored. +// Empty strings indicate that they are non-partitioned tensors. +// dtypes: shape {N}. The list of expected dtype for the tensors. Must match +// those stored in the checkpoint. +// +// Returns shape {N}. The restored tensors, whose shapes are read from the +// checkpoint directly. +func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + opspec := tf.OpSpec{ + Type: "RestoreV2", + Input: []tf.Input{ + prefix, tensor_names, shape_and_slices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil { + scope.UpdateErr("RestoreV2", err) + return + } + return tensors +} + +// Delete the TensorArray from its resource container. +// +// This enables the user to close and release the resource in the middle +// of a step/run. +// +// Arguments: +// handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). +// +// Returns the created operation. +func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayCloseV3", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// Saves tensors in V2 checkpoint format. +// +// By default, saves the named tensors in full. If the caller wishes to save +// specific slices of full tensors, "shape_and_slices" should be non-empty strings +// and correspondingly well-formed. +// +// Arguments: +// prefix: Must have a single element. The prefix of the V2 checkpoint to which we +// write the tensors. +// tensor_names: shape {N}. The names of the tensors to be saved. +// shape_and_slices: shape {N}. The slice specs of the tensors to be saved. +// Empty strings indicate that they are non-partitioned tensors. +// tensors: `N` tensors to save. +// +// Returns the created operation. +func SaveV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, tensors []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SaveV2", + Input: []tf.Input{ + prefix, tensor_names, shape_and_slices, tf.OutputList(tensors), + }, + } + return scope.AddOperation(opspec) +} + +// SparseCountSparseOutputAttr is an optional argument to SparseCountSparseOutput. +type SparseCountSparseOutputAttr func(optionalAttr) + +// SparseCountSparseOutputMinlength sets the optional minlength attribute to value. +// +// value: Minimum value to count. Can be set to -1 for no minimum. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func SparseCountSparseOutputMinlength(value int64) SparseCountSparseOutputAttr { + return func(m optionalAttr) { + m["minlength"] = value + } +} + +// SparseCountSparseOutputMaxlength sets the optional maxlength attribute to value. +// +// value: Maximum value to count. Can be set to -1 for no maximum. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func SparseCountSparseOutputMaxlength(value int64) SparseCountSparseOutputAttr { + return func(m optionalAttr) { + m["maxlength"] = value + } +} + +// Performs sparse-output bin counting for a sparse tensor input. +// +// Counts the number of times each value occurs in the input. +// +// Arguments: +// indices: Tensor containing the indices of the sparse tensor to count. +// values: Tensor containing values of the sparse tensor to count. +// dense_shape: Tensor containing the dense shape of the sparse tensor to count. +// weights: A Tensor of the same shape as indices containing per-index weight values. +// May also be the empty tensor if no weights are used. +// binary_output: Whether to output the number of occurrences of each value or 1. +// +// Returns: +// output_indices: Indices tensor for the resulting sparse tensor object. +// output_values: Values tensor for the resulting sparse tensor object. +// output_dense_shape: Shape tensor for the resulting sparse tensor object. +func SparseCountSparseOutput(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, weights tf.Output, binary_output bool, optional ...SparseCountSparseOutputAttr) (output_indices tf.Output, output_values tf.Output, output_dense_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"binary_output": binary_output} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseCountSparseOutput", + Input: []tf.Input{ + indices, values, dense_shape, weights, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// DebugNumericSummaryV2Attr is an optional argument to DebugNumericSummaryV2. +type DebugNumericSummaryV2Attr func(optionalAttr) + +// DebugNumericSummaryV2OutputDtype sets the optional output_dtype attribute to value. +// +// value: Optional. The type of the output. Can be float32 or float64 (default: float32). +// If not specified, defaults to DT_FLOAT +func DebugNumericSummaryV2OutputDtype(value tf.DataType) DebugNumericSummaryV2Attr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// DebugNumericSummaryV2TensorDebugMode sets the optional tensor_debug_mode attribute to value. +// +// value: Tensor debug mode: the mode in which the input tensor is summarized +// by the op. See the TensorDebugMode enum in +// tensorflow/core/protobuf/debug_event.proto for details. +// +// Supported values: +// 2 (CURT_HEALTH): Output a float32/64 tensor of shape [2]. The 1st +// element is the tensor_id, if provided, and -1 otherwise. The 2nd +// element is a bit which is set to 1 if the input tensor has an +// infinity or nan value, or zero otherwise. +// +// 3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st +// element is the tensor_id, if provided, and -1 otherwise. The +// remaining four slots are the total number of elements, -infs, +// +infs, and nans in the input tensor respectively. +// +// 4 (FULL_HEALTH): Output a float32/64 tensor of shape [11]. The 1st +// element is the tensor_id, if provided, and -1 otherwise. The 2nd +// element is the device_id, if provided, and -1 otherwise. The 3rd +// element holds the datatype value of the input tensor as according +// to the enumerated type in tensorflow/core/framework/types.proto. +// The remaining elements hold the total number of elements, -infs, +// +infs, nans, negative finite numbers, zeros, and positive finite +// numbers in the input tensor respectively. +// +// 5 (SHAPE): Output a float32/64 tensor of shape [10]. The 1st +// element is the tensor_id, if provided, and -1 otherwise. The 2nd +// element holds the datatype value of the input tensor as according +// to the enumerated type in tensorflow/core/framework/types.proto. +// The 3rd element holds the rank of the tensor. The 4th element holds +// the number of elements within the tensor. Finally the remaining 6 +// elements hold the shape of the tensor. If the rank of the tensor +// is lower than 6, the shape is right padded with zeros. If the rank +// is greater than 6, the head of the shape is truncated. +// +// 6 (FULL_NUMERICS): Output a float32/64 tensor of shape [22]. The 1st +// element is the tensor_id, if provided, and -1 otherwise. The 2nd +// element is the device_id, if provided, and -1 otherwise. The 3rd +// element holds the datatype value of the input tensor as according +// to the enumerated type in tensorflow/core/framework/types.proto. +// The 4th element holds the rank of the tensor. The 5th to 11th +// elements hold the shape of the tensor. If the rank of the tensor +// is lower than 6, the shape is right padded with zeros. If the rank +// is greater than 6, the head of the shape is truncated. The 12th to +// 18th elements hold the number of elements, -infs, +infs, nans, +// denormal floats, negative finite numbers, zeros, and positive +// finite numbers in the input tensor respectively. The final four +// elements hold the min value, max value, mean, and variance of the +// input tensor. +// +// 8 (REDUCE_INF_NAN_THREE_SLOTS): Output a float32/64 tensor of shape +// [3]. The 1st element is -inf if any elements of the input tensor +// is -inf, or zero otherwise. The 2nd element is +inf if any elements +// of the input tensor is +inf, or zero otherwise. The 3rd element is +// nan if any element of the input tensor is nan, or zero otherwise. +// If not specified, defaults to -1 +func DebugNumericSummaryV2TensorDebugMode(value int64) DebugNumericSummaryV2Attr { + return func(m optionalAttr) { + m["tensor_debug_mode"] = value + } +} + +// DebugNumericSummaryV2TensorId sets the optional tensor_id attribute to value. +// +// value: Optional. An integer identifier for the tensor being summarized by this op. +// If not specified, defaults to -1 +func DebugNumericSummaryV2TensorId(value int64) DebugNumericSummaryV2Attr { + return func(m optionalAttr) { + m["tensor_id"] = value + } +} + +// Debug Numeric Summary V2 Op. +// +// Computes a numeric summary of the input tensor. The shape of the output +// depends on the tensor_debug_mode attribute. +// This op is used internally by TensorFlow Debugger (tfdbg) v2. +// +// Arguments: +// input: Input tensor, to be summarized by the op. +func DebugNumericSummaryV2(scope *Scope, input tf.Output, optional ...DebugNumericSummaryV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DebugNumericSummaryV2", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DebugNumericSummaryAttr is an optional argument to DebugNumericSummary. +type DebugNumericSummaryAttr func(optionalAttr) + +// DebugNumericSummaryDeviceName sets the optional device_name attribute to value. +// If not specified, defaults to "" +func DebugNumericSummaryDeviceName(value string) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["device_name"] = value + } +} + +// DebugNumericSummaryTensorName sets the optional tensor_name attribute to value. +// +// value: Name of the input tensor. +// If not specified, defaults to "" +func DebugNumericSummaryTensorName(value string) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["tensor_name"] = value + } +} + +// DebugNumericSummaryDebugUrls sets the optional debug_urls attribute to value. +// +// value: List of URLs to debug targets, e.g., +// file:///foo/tfdbg_dump, grpc:://localhost:11011. +// If not specified, defaults to <> +func DebugNumericSummaryDebugUrls(value []string) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["debug_urls"] = value + } +} + +// DebugNumericSummaryLowerBound sets the optional lower_bound attribute to value. +// +// value: (float) The lower bound <= which values will be included in the +// generalized -inf count. Default: -inf. +// If not specified, defaults to -inf +func DebugNumericSummaryLowerBound(value float32) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["lower_bound"] = value + } +} + +// DebugNumericSummaryUpperBound sets the optional upper_bound attribute to value. +// +// value: (float) The upper bound >= which values will be included in the +// generalized +inf count. Default: +inf. +// If not specified, defaults to inf +func DebugNumericSummaryUpperBound(value float32) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["upper_bound"] = value + } +} + +// DebugNumericSummaryMuteIfHealthy sets the optional mute_if_healthy attribute to value. +// +// value: (bool) Do not send data to the debug URLs unless at least one +// of elements [2], [3] and [7] (i.e., the nan count and the generalized -inf and +// inf counts) is non-zero. +// If not specified, defaults to false +func DebugNumericSummaryMuteIfHealthy(value bool) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["mute_if_healthy"] = value + } +} + +// DebugNumericSummaryGatedGrpc sets the optional gated_grpc attribute to value. +// +// value: Whether this op will be gated. If any of the debug_urls of this +// debug node is of the grpc:// scheme, when the value of this attribute is set +// to True, the data will not actually be sent via the grpc stream unless this +// debug op has been enabled at the debug_url. If all of the debug_urls of this +// debug node are of the grpc:// scheme and the debug op is enabled at none of +// them, the output will be an empty Tensor. +// If not specified, defaults to false +func DebugNumericSummaryGatedGrpc(value bool) DebugNumericSummaryAttr { + return func(m optionalAttr) { + m["gated_grpc"] = value + } +} + +// Debug Numeric Summary Op. +// +// Provide a basic summary of numeric value types, range and distribution. +// +// output: A double tensor of shape [14 + nDimensions], where nDimensions is the +// number of dimensions of the tensor's shape. The elements of output are: +// [0]: is initialized (1.0) or not (0.0). +// [1]: total number of elements +// [2]: NaN element count +// [3]: generalized -inf count: elements <= lower_bound. lower_bound is -inf by +// default. +// [4]: negative element count (excluding -inf), if lower_bound is the default +// -inf. Otherwise, this is the count of elements > lower_bound and < 0. +// [5]: zero element count +// [6]: positive element count (excluding +inf), if upper_bound is the default +// +inf. Otherwise, this is the count of elements < upper_bound and > 0. +// [7]: generalized +inf count, elements >= upper_bound. upper_bound is +inf by +// default. +// Output elements [1:8] are all zero, if the tensor is uninitialized. +// [8]: minimum of all non-inf and non-NaN elements. +// If uninitialized or no such element exists: +inf. +// [9]: maximum of all non-inf and non-NaN elements. +// If uninitialized or no such element exists: -inf. +// [10]: mean of all non-inf and non-NaN elements. +// If uninitialized or no such element exists: NaN. +// [11]: variance of all non-inf and non-NaN elements. +// If uninitialized or no such element exists: NaN. +// [12]: Data type of the tensor encoded as an enum integer. See the DataType +// proto for more details. +// [13]: Number of dimensions of the tensor (ndims). +// [14+]: Sizes of the dimensions. +// +// +// Arguments: +// input: Input tensor, non-Reference type. +func DebugNumericSummary(scope *Scope, input tf.Output, optional ...DebugNumericSummaryAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DebugNumericSummary", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs random integers from a uniform distribution. +// +// The generated values are uniform integers in the range `[minval, maxval)`. +// The lower bound `minval` is included in the range, while the upper bound +// `maxval` is excluded. +// +// The random integers are slightly biased unless `maxval - minval` is an exact +// power of two. The bias is small for values of `maxval - minval` significantly +// smaller than the range of the output (either `2^32` or `2^64`). +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// minval: Minimum value (inclusive, scalar). +// maxval: Maximum value (exclusive, scalar). +// +// Returns Random values with specified shape. +func StatefulUniformInt(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, minval tf.Output, maxval tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatefulUniformInt", + Input: []tf.Input{ + resource, algorithm, shape, minval, maxval, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An Op to exchange data across TPU replicas. +// +// On each replica, the input is split into `split_count` blocks along +// `split_dimension` and send to the other replicas given group_assignment. After +// receiving `split_count` - 1 blocks from other replicas, we concatenate the +// blocks along `concat_dimension` as the output. +// +// For example, suppose there are 2 TPU replicas: +// replica 0 receives input: `[[A, B]]` +// replica 1 receives input: `[[C, D]]` +// +// group_assignment=`[[0, 1]]` +// concat_dimension=0 +// split_dimension=1 +// split_count=2 +// +// replica 0's output: `[[A], [C]]` +// replica 1's output: `[[B], [D]]` +// +// Arguments: +// input: The local input to the sum. +// group_assignment: An int32 tensor with shape +// [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the +// replica ids in the ith subgroup. +// concat_dimension: The dimension number to concatenate. +// split_dimension: The dimension number to split. +// split_count: The number of splits, this number must equal to the sub-group +// size(group_assignment.get_shape()[1]) +// +// Returns The exchanged result. +func AllToAll(scope *Scope, input tf.Output, group_assignment tf.Output, concat_dimension int64, split_dimension int64, split_count int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"concat_dimension": concat_dimension, "split_dimension": split_dimension, "split_count": split_count} + opspec := tf.OpSpec{ + Type: "AllToAll", + Input: []tf.Input{ + input, group_assignment, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TridiagonalSolveAttr is an optional argument to TridiagonalSolve. +type TridiagonalSolveAttr func(optionalAttr) + +// TridiagonalSolvePartialPivoting sets the optional partial_pivoting attribute to value. +// +// value: Whether to apply partial pivoting. Partial pivoting makes the procedure more +// stable, but slower. +// If not specified, defaults to true +func TridiagonalSolvePartialPivoting(value bool) TridiagonalSolveAttr { + return func(m optionalAttr) { + m["partial_pivoting"] = value + } +} + +// Solves tridiagonal systems of equations. +// +// Solves tridiagonal systems of equations. +// Supports batch dimensions and multiple right-hand sides per each left-hand +// side. +// On CPU, solution is computed via Gaussian elimination with or without partial +// pivoting, depending on `partial_pivoting` attribute. On GPU, Nvidia's cuSPARSE +// library is used: https://docs.nvidia.com/cuda/cusparse/index.html#gtsv +// Partial pivoting is not yet supported by XLA backends. +// +// Arguments: +// diagonals: Tensor of shape `[..., 3, M]` whose innermost 2 dimensions represent the +// tridiagonal matrices with three rows being the superdiagonal, diagonals, and +// subdiagonals, in order. The last element of the superdiagonal and the first +// element of the subdiagonal is ignored. +// rhs: Tensor of shape `[..., M, K]`, representing K right-hand sides per each +// left-hand side. +// +// Returns Tensor of shape `[..., M, K]` containing the solutions +func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional ...TridiagonalSolveAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TridiagonalSolve", + Input: []tf.Input{ + diagonals, rhs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for SparseSegmentMean. +// +// Returns tensor "output" with same shape as grad, except for dimension 0 whose +// value is output_dim0. +// +// Arguments: +// grad: gradient propagated to the SparseSegmentMean op. +// indices: indices passed to the corresponding SparseSegmentMean op. +// segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. +// output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. +func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMeanGrad", + Input: []tf.Input{ + grad, indices, segment_ids, output_dim0, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SvdAttr is an optional argument to Svd. +type SvdAttr func(optionalAttr) + +// SvdComputeUv sets the optional compute_uv attribute to value. +// +// value: If true, left and right singular vectors will be +// computed and returned in `u` and `v`, respectively. +// If false, `u` and `v` are not set and should never referenced. +// If not specified, defaults to true +func SvdComputeUv(value bool) SvdAttr { + return func(m optionalAttr) { + m["compute_uv"] = value + } +} + +// SvdFullMatrices sets the optional full_matrices attribute to value. +// +// value: If true, compute full-sized `u` and `v`. If false +// (the default), compute only the leading `P` singular vectors. +// Ignored if `compute_uv` is `False`. +// If not specified, defaults to false +func SvdFullMatrices(value bool) SvdAttr { + return func(m optionalAttr) { + m["full_matrices"] = value + } +} + +// Computes the singular value decompositions of one or more matrices. +// +// Computes the SVD of each inner matrix in `input` such that +// `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])` +// +// ```python +// # a is a tensor containing a batch of matrices. +// # s is a tensor of singular values for each matrix. +// # u is the tensor containing the left singular vectors for each matrix. +// # v is the tensor containing the right singular vectors for each matrix. +// s, u, v = svd(a) +// s, _, _ = svd(a, compute_uv=False) +// ``` +// +// Arguments: +// input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions +// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`. +// +// Returns: +// s: Singular values. Shape is `[..., P]`. +// u: Left singular vectors. If `full_matrices` is `False` then shape is +// `[..., M, P]`; if `full_matrices` is `True` then shape is +// `[..., M, M]`. Undefined if `compute_uv` is `False`. +// v: Left singular vectors. If `full_matrices` is `False` then shape is +// `[..., N, P]`. If `full_matrices` is `True` then shape is `[..., N, N]`. +// Undefined if `compute_uv` is false. +func Svd(scope *Scope, input tf.Output, optional ...SvdAttr) (s tf.Output, u tf.Output, v tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Svd", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QrAttr is an optional argument to Qr. +type QrAttr func(optionalAttr) + +// QrFullMatrices sets the optional full_matrices attribute to value. +// +// value: If true, compute full-sized `q` and `r`. If false +// (the default), compute only the leading `P` columns of `q`. +// If not specified, defaults to false +func QrFullMatrices(value bool) QrAttr { + return func(m optionalAttr) { + m["full_matrices"] = value + } +} + +// Computes the QR decompositions of one or more matrices. +// +// Computes the QR decomposition of each inner matrix in `tensor` such that +// `tensor[..., :, :] = q[..., :, :] * r[..., :,:])` +// +// ```python +// # a is a tensor. +// # q is a tensor of orthonormal matrices. +// # r is a tensor of upper triangular matrices. +// q, r = qr(a) +// q_full, r_full = qr(a, full_matrices=True) +// ``` +// +// Arguments: +// input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions +// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`. +// +// Returns: +// q: Orthonormal basis for range of `a`. If `full_matrices` is `False` then +// shape is `[..., M, P]`; if `full_matrices` is `True` then shape is +// `[..., M, M]`. +// r: Triangular factor. If `full_matrices` is `False` then shape is +// `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`. +func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Qr", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MatrixTriangularSolveAttr is an optional argument to MatrixTriangularSolve. +type MatrixTriangularSolveAttr func(optionalAttr) + +// MatrixTriangularSolveLower sets the optional lower attribute to value. +// +// value: Boolean indicating whether the innermost matrices in `matrix` are +// lower or upper triangular. +// If not specified, defaults to true +func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr { + return func(m optionalAttr) { + m["lower"] = value + } +} + +// MatrixTriangularSolveAdjoint sets the optional adjoint attribute to value. +// +// value: Boolean indicating whether to solve with `matrix` or its (block-wise) +// adjoint. +// +// @compatibility(numpy) +// Equivalent to scipy.linalg.solve_triangular +// @end_compatibility +// If not specified, defaults to false +func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr { + return func(m optionalAttr) { + m["adjoint"] = value + } +} + +// Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. +// +// +// `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form +// square matrices. If `lower` is `True` then the strictly upper triangular part +// of each inner-most matrix is assumed to be zero and not accessed. +// If `lower` is False then the strictly lower triangular part of each inner-most +// matrix is assumed to be zero and not accessed. +// `rhs` is a tensor of shape `[..., M, N]`. +// +// The output is a tensor of shape `[..., M, N]`. If `adjoint` is +// `True` then the innermost matrices in `output` satisfy matrix equations +// `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. +// If `adjoint` is `False` then the strictly then the innermost matrices in +// `output` satisfy matrix equations +// `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`. +// +// Note, the batch shapes for the inputs only need to broadcast. +// +// Example: +// ```python +// +// a = tf.constant([[3, 0, 0, 0], +// [2, 1, 0, 0], +// [1, 0, 1, 0], +// [1, 1, 1, 1]], dtype=tf.float32) +// +// b = tf.constant([[4], +// [2], +// [4], +// [2]], dtype=tf.float32) +// +// x = tf.linalg.triangular_solve(a, b, lower=True) +// x +// # +// +// # in python3 one can use `a@x` +// tf.matmul(a, x) +// # +// ``` +// +// Arguments: +// matrix: Shape is `[..., M, M]`. +// rhs: Shape is `[..., M, K]`. +// +// Returns Shape is `[..., M, K]`. +func MatrixTriangularSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixTriangularSolveAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixTriangularSolve", + Input: []tf.Input{ + matrix, rhs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2. +type SelfAdjointEigV2Attr func(optionalAttr) + +// SelfAdjointEigV2ComputeV sets the optional compute_v attribute to value. +// +// value: If `True` then eigenvectors will be computed and returned in `v`. +// Otherwise, only the eigenvalues will be computed. +// If not specified, defaults to true +func SelfAdjointEigV2ComputeV(value bool) SelfAdjointEigV2Attr { + return func(m optionalAttr) { + m["compute_v"] = value + } +} + +// Computes the eigen decomposition of one or more square self-adjoint matrices. +// +// Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in +// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues +// are sorted in non-decreasing order. +// +// ```python +// # a is a tensor. +// # e is a tensor of eigenvalues. +// # v is a tensor of eigenvectors. +// e, v = self_adjoint_eig(a) +// e = self_adjoint_eig(a, compute_v=False) +// ``` +// +// Arguments: +// input: `Tensor` input of shape `[N, N]`. +// +// Returns: +// e: Eigenvalues. Shape is `[N]`. +// v: Eigenvectors. Shape is `[N, N]`. +func SelfAdjointEigV2(scope *Scope, input tf.Output, optional ...SelfAdjointEigV2Attr) (e tf.Output, v tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SelfAdjointEigV2", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes the Eigen Decomposition of a batch of square self-adjoint matrices. +// +// DEPRECATED at GraphDef version 11: Use SelfAdjointEigV2 instead. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices, with the same constraints as the single matrix +// SelfAdjointEig. +// +// The result is a [..., M+1, M] matrix with [..., 0,:] containing the +// eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. The eigenvalues +// are sorted in non-decreasing order. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M+1, M]`. +func SelfAdjointEig(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SelfAdjointEig", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the key-value pairs in one or more LMDB files. +// +// The Lightning Memory-Mapped Database Manager, or LMDB, is an embedded binary +// key-value database. This dataset can read the contents of LMDB database files, +// the names of which generally have the `.mdb` suffix. +// +// Each output element consists of a key-value pair represented as a pair of +// scalar string `Tensor`s, where the first `Tensor` contains the key and the +// second `Tensor` contains the value. +// +// LMDB uses different file formats on big- and little-endian machines. +// `LMDBDataset` can only read files in the format of the host machine. +// +// Arguments: +// filenames: A scalar or a vector containing the name(s) of the binary file(s) to be +// read. +// +// +func LMDBDataset(scope *Scope, filenames tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "LMDBDataset", + Input: []tf.Input{ + filenames, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MatrixInverseAttr is an optional argument to MatrixInverse. +type MatrixInverseAttr func(optionalAttr) + +// MatrixInverseAdjoint sets the optional adjoint attribute to value. +// If not specified, defaults to false +func MatrixInverseAdjoint(value bool) MatrixInverseAttr { + return func(m optionalAttr) { + m["adjoint"] = value + } +} + +// Computes the inverse of one or more square invertible matrices or their +// +// adjoints (conjugate transposes). +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor of the same shape as the input +// containing the inverse for all input submatrices `[..., :, :]`. +// +// The op uses LU decomposition with partial pivoting to compute the inverses. +// +// If a matrix is not invertible there is no guarantee what the op does. It +// may detect the condition and raise an exception or it may simply return a +// garbage result. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M, M]`. +// +// @compatibility(numpy) +// Equivalent to np.linalg.inv +// @end_compatibility +func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixInverse", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sign and the log of the absolute value of the determinant of +// +// one or more square matrices. +// +// The input is a tensor of shape `[N, M, M]` whose inner-most 2 dimensions +// form square matrices. The outputs are two tensors containing the signs and +// absolute values of the log determinants for all N input submatrices +// `[..., :, :]` such that the determinant = sign*exp(log_abs_determinant). +// The log_abs_determinant is computed as det(P)*sum(log(diag(LU))) where LU +// is the LU decomposition of the input and P is the corresponding +// permutation matrix. +// +// Arguments: +// input: Shape is `[N, M, M]`. +// +// Returns: +// sign: The signs of the log determinants of the inputs. Shape is `[N]`. +// log_abs_determinant: The logs of the absolute values of the determinants +// of the N input matrices. Shape is `[N]`. +func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_abs_determinant tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogMatrixDeterminant", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes the determinant of one or more square matrices. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor containing the determinants +// for all input submatrices `[..., :, :]`. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[...]`. +func MatrixDeterminant(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDeterminant", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeBilinearAttr is an optional argument to ResizeBilinear. +type ResizeBilinearAttr func(optionalAttr) + +// ResizeBilinearAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeBilinearAlignCorners(value bool) ResizeBilinearAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeBilinearHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBilinearHalfPixelCenters(value bool) ResizeBilinearAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize `images` to `size` using bilinear interpolation. +// +// Input images can be of different types but output images are always float. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeBilinear(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBilinearAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeBilinear", + Input: []tf.Input{ + images, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a TensorList by indexing into a Tensor. +// +// Each member of the TensorList corresponds to one row of the input tensor, +// specified by the given index (see `tf.gather`). +// +// tensor: The input tensor. +// indices: The indices used to index into the list. +// element_shape: The shape of the elements in the list (can be less specified than +// the shape of the tensor). +// num_elements: The size of the output list. Must be large enough to accommodate +// the largest index in indices. If -1, the list is just large enough to include +// the largest index in indices. +// output_handle: The TensorList. +func TensorListScatterV2(scope *Scope, tensor tf.Output, indices tf.Output, element_shape tf.Output, num_elements tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListScatterV2", + Input: []tf.Input{ + tensor, indices, element_shape, num_elements, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a TensorList by indexing into a Tensor. +// +// Each member of the TensorList corresponds to one row of the input tensor, +// specified by the given index (see `tf.gather`). +// +// tensor: The input tensor. +// indices: The indices used to index into the list. +// element_shape: The shape of the elements in the list (can be less specified than +// the shape of the tensor). +// output_handle: The TensorList. +func TensorListScatter(scope *Scope, tensor tf.Output, indices tf.Output, element_shape tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListScatter", + Input: []tf.Input{ + tensor, indices, element_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient of the sigmoid of `x` wrt its input. +// +// Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and +// `dy` is the corresponding input gradient. +func SigmoidGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SigmoidGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a Tensor by indexing into the TensorList. +// +// Each row in the produced Tensor corresponds to the element in the TensorList +// specified by the given index (see `tf.gather`). +// +// input_handle: The input tensor list. +// indices: The indices used to index into the list. +// values: The tensor. +func TensorListGather(scope *Scope, input_handle tf.Output, indices tf.Output, element_shape tf.Output, element_dtype tf.DataType) (values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "TensorListGather", + Input: []tf.Input{ + input_handle, indices, element_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// The shape of the elements of the given list, as a tensor. +// +// input_handle: the list +// element_shape: the shape of elements of the list +func TensorListElementShape(scope *Scope, input_handle tf.Output, shape_type tf.DataType) (element_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape_type": shape_type} + opspec := tf.OpSpec{ + Type: "TensorListElementShape", + Input: []tf.Input{ + input_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExperimentalThreadPoolHandleAttr is an optional argument to ExperimentalThreadPoolHandle. +type ExperimentalThreadPoolHandleAttr func(optionalAttr) + +// ExperimentalThreadPoolHandleMaxIntraOpParallelism sets the optional max_intra_op_parallelism attribute to value. +// +// value: The maximum degree of parallelism to use within operations that execute on this +// threadpool. +// If not specified, defaults to 1 +func ExperimentalThreadPoolHandleMaxIntraOpParallelism(value int64) ExperimentalThreadPoolHandleAttr { + return func(m optionalAttr) { + m["max_intra_op_parallelism"] = value + } +} + +// ExperimentalThreadPoolHandleContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func ExperimentalThreadPoolHandleContainer(value string) ExperimentalThreadPoolHandleAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// ExperimentalThreadPoolHandleSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func ExperimentalThreadPoolHandleSharedName(value string) ExperimentalThreadPoolHandleAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// num_threads: The number of threads in the thread pool. +// display_name: A human-readable name for the threads that may be visible in some +// visualizations. +// threadpool. +// +// Returns A resource that can be consumed by one or more ExperimentalThreadPoolDataset +// ops. +func ExperimentalThreadPoolHandle(scope *Scope, num_threads int64, display_name string, optional ...ExperimentalThreadPoolHandleAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_threads": num_threads, "display_name": display_name} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalThreadPoolHandle", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a TensorList which, when stacked, has the value of `tensor`. +// +// Each tensor in the result list corresponds to one row of the input tensor. +// +// tensor: The input tensor. +// output_handle: The list. +func TensorListFromTensor(scope *Scope, tensor tf.Output, element_shape tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListFromTensor", + Input: []tf.Input{ + tensor, element_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorListStackAttr is an optional argument to TensorListStack. +type TensorListStackAttr func(optionalAttr) + +// TensorListStackNumElements sets the optional num_elements attribute to value. +// If not specified, defaults to -1 +func TensorListStackNumElements(value int64) TensorListStackAttr { + return func(m optionalAttr) { + m["num_elements"] = value + } +} + +// Stacks all tensors in the list. +// +// Requires that all tensors have the same shape. +// +// input_handle: the input list +// tensor: the gathered result +// num_elements: optional. If not -1, the number of elements in the list. +// +func TensorListStack(scope *Scope, input_handle tf.Output, element_shape tf.Output, element_dtype tf.DataType, optional ...TensorListStackAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorListStack", + Input: []tf.Input{ + input_handle, element_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the number of tensors in the input tensor list. +// +// input_handle: the input list +// length: the number of tensors in the list +func TensorListLength(scope *Scope, input_handle tf.Output) (length tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListLength", + Input: []tf.Input{ + input_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Merges summaries. +// +// This op creates a +// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) +// protocol buffer that contains the union of all the values in the input +// summaries. +// +// When the Op is run, it reports an `InvalidArgument` error if multiple values +// in the summaries to merge use the same tag. +// +// Arguments: +// inputs: Can be of any shape. Each must contain serialized `Summary` protocol +// buffers. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func MergeSummary(scope *Scope, inputs []tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MergeSummary", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AvgPoolAttr is an optional argument to AvgPool. +type AvgPoolAttr func(optionalAttr) + +// AvgPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func AvgPoolDataFormat(value string) AvgPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs average pooling on the input. +// +// Each entry in `output` is the mean of the corresponding size `ksize` +// window in `value`. +// +// Arguments: +// value: 4-D with shape `[batch, height, width, channels]`. +// ksize: The size of the sliding window for each dimension of `value`. +// strides: The stride of the sliding window for each dimension of `value`. +// padding: The type of padding algorithm to use. +// +// Returns The average pooled output tensor. +func AvgPool(scope *Scope, value tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPool", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AudioSummaryV2Attr is an optional argument to AudioSummaryV2. +type AudioSummaryV2Attr func(optionalAttr) + +// AudioSummaryV2MaxOutputs sets the optional max_outputs attribute to value. +// +// value: Max number of batch elements to generate audio for. +// If not specified, defaults to 3 +// +// REQUIRES: value >= 1 +func AudioSummaryV2MaxOutputs(value int64) AudioSummaryV2Attr { + return func(m optionalAttr) { + m["max_outputs"] = value + } +} + +// Outputs a `Summary` protocol buffer with audio. +// +// The summary has up to `max_outputs` summary values containing audio. The +// audio is built from `tensor` which must be 3-D with shape `[batch_size, +// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are +// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. +// +// The `tag` argument is a scalar `Tensor` of type `string`. It is used to +// build the `tag` of the summary values: +// +// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. +// * If `max_outputs` is greater than 1, the summary value tags are +// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. +// +// Arguments: +// tag: Scalar. Used to build the `tag` attribute of the summary values. +// tensor: 2-D of shape `[batch_size, frames]`. +// sample_rate: The sample rate of the signal in hertz. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate tf.Output, optional ...AudioSummaryV2Attr) (summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSummaryV2", + Input: []tf.Input{ + tag, tensor, sample_rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs a `Summary` protocol buffer with a histogram. +// +// The generated +// [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) +// has one summary value containing a histogram for `values`. +// +// This op reports an `InvalidArgument` error if any value is not finite. +// +// Arguments: +// tag: Scalar. Tag to use for the `Summary.Value`. +// values: Any shape. Values to use to build the histogram. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func HistogramSummary(scope *Scope, tag tf.Output, values tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "HistogramSummary", + Input: []tf.Input{ + tag, values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringLengthAttr is an optional argument to StringLength. +type StringLengthAttr func(optionalAttr) + +// StringLengthUnit sets the optional unit attribute to value. +// +// value: The unit that is counted to compute string length. One of: `"BYTE"` (for +// the number of bytes in each string) or `"UTF8_CHAR"` (for the number of UTF-8 +// encoded Unicode code points in each string). Results are undefined +// if `unit=UTF8_CHAR` and the `input` strings do not contain structurally +// valid UTF-8. +// If not specified, defaults to "BYTE" +func StringLengthUnit(value string) StringLengthAttr { + return func(m optionalAttr) { + m["unit"] = value + } +} + +// String lengths of `input`. +// +// Computes the length of each string given in the input tensor. +// +// >>> strings = tf.constant(['Hello','TensorFlow', '\U0001F642']) +// >>> tf.strings.length(strings).numpy() # default counts bytes +// array([ 5, 10, 4], dtype=int32) +// >>> tf.strings.length(strings, unit="UTF8_CHAR").numpy() +// array([ 5, 10, 1], dtype=int32) +// +// +// Arguments: +// input: The strings for which to compute the length for each element. +// +// Returns Integer tensor that has the same shape as `input`. The output contains the +// element-wise string lengths of `input`. +func StringLength(scope *Scope, input tf.Output, optional ...StringLengthAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringLength", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorSummaryAttr is an optional argument to TensorSummary. +type TensorSummaryAttr func(optionalAttr) + +// TensorSummaryDescription sets the optional description attribute to value. +// +// value: A json-encoded SummaryDescription proto. +// If not specified, defaults to "" +func TensorSummaryDescription(value string) TensorSummaryAttr { + return func(m optionalAttr) { + m["description"] = value + } +} + +// TensorSummaryLabels sets the optional labels attribute to value. +// +// value: An unused list of strings. +// If not specified, defaults to <> +func TensorSummaryLabels(value []string) TensorSummaryAttr { + return func(m optionalAttr) { + m["labels"] = value + } +} + +// TensorSummaryDisplayName sets the optional display_name attribute to value. +// +// value: An unused string. +// If not specified, defaults to "" +func TensorSummaryDisplayName(value string) TensorSummaryAttr { + return func(m optionalAttr) { + m["display_name"] = value + } +} + +// Outputs a `Summary` protocol buffer with a tensor. +// +// This op is being phased out in favor of TensorSummaryV2, which lets callers pass +// a tag as well as a serialized SummaryMetadata proto string that contains +// plugin-specific data. We will keep this op to maintain backwards compatibility. +// +// Arguments: +// tensor: A tensor to serialize. +func TensorSummary(scope *Scope, tensor tf.Output, optional ...TensorSummaryAttr) (summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorSummary", + Input: []tf.Input{ + tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Scatters tensor at indices in an input list. +// +// Each member of the TensorList corresponds to one row of the input tensor, +// specified by the given index (see `tf.gather`). +// +// input_handle: The list to scatter into. +// tensor: The input tensor. +// indices: The indices used to index into the list. +// output_handle: The TensorList. +func TensorListScatterIntoExistingList(scope *Scope, input_handle tf.Output, tensor tf.Output, indices tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListScatterIntoExistingList", + Input: []tf.Input{ + input_handle, tensor, indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs a `Summary` protocol buffer with a tensor and per-plugin data. +// +// Arguments: +// tag: A string attached to this summary. Used for organization in TensorBoard. +// tensor: A tensor to serialize. +// serialized_summary_metadata: A serialized SummaryMetadata proto. Contains plugin +// data. +func TensorSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, serialized_summary_metadata tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorSummaryV2", + Input: []tf.Input{ + tag, tensor, serialized_summary_metadata, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient for the sqrt of `x` wrt its input. +// +// Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy` +// is the corresponding input gradient. +func SqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SqrtGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Applies sparse addition to `input` using individual values or slices +// +// from `updates` according to indices `indices`. The updates are non-aliasing: +// `input` is only modified in-place if no other operations will use it. +// Otherwise, a copy of `input` is made. This operation has a gradient with +// respect to both `input` and `updates`. +// +// `input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `input`. +// It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or `(P-K)`-dimensional slices +// (if `K < P`) along the `K`th dimension of `input`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$ +// +// For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 +// elements. In Python, that addition would look like this: +// +// input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// output = tf.scatter_nd_non_aliasing_add(input, indices, updates) +// with tf.Session() as sess: +// print(sess.run(output)) +// +// The resulting value `output` would look like this: +// +// [1, 13, 3, 14, 14, 6, 7, 20] +// +// See `tf.scatter_nd` for more details about how to make updates to slices. +// +// Arguments: +// input: A Tensor. +// indices: A Tensor. Must be one of the following types: `int32`, `int64`. +// A tensor of indices into `input`. +// updates: A Tensor. Must have the same type as ref. A tensor of updated values +// to add to `input`. +// +// Returns A `Tensor` with the same shape as `input`, containing values of `input` +// updated with `updates`. +func ScatterNdNonAliasingAdd(scope *Scope, input tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ScatterNdNonAliasingAdd", + Input: []tf.Input{ + input, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2. +type MutableHashTableOfTensorsV2Attr func(optionalAttr) + +// MutableHashTableOfTensorsV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// If not specified, defaults to false +func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value. +// If not specified, defaults to <> +func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr { + return func(m optionalAttr) { + m["value_shape"] = value + } +} + +// Creates an empty hash table. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a vector. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableHashTableOfTensorsV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. +// +// The `input` tensor has shape `[batch, in_height, in_width, depth]` and the +// `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each +// input channel is processed independently of the others with its own structuring +// function. The `output` tensor has shape +// `[batch, out_height, out_width, depth]`. The spatial dimensions of the output +// tensor depend on the `padding` algorithm. We currently only support the default +// "NHWC" `data_format`. +// +// In detail, the grayscale morphological 2-D dilation is the max-sum correlation +// (for consistency with `conv2d`, we use unmirrored filters): +// +// output[b, y, x, c] = +// max_{dy, dx} input[b, +// strides[1] * y + rates[1] * dy, +// strides[2] * x + rates[2] * dx, +// c] + +// filter[dy, dx, c] +// +// Max-pooling is a special case when the filter has size equal to the pooling +// kernel size and contains all zeros. +// +// Note on duality: The dilation of `input` by the `filter` is equal to the +// negation of the erosion of `-input` by the reflected `filter`. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, depth]`. +// filter: 3-D with shape `[filter_height, filter_width, depth]`. +// strides: The stride of the sliding window for each dimension of the input +// tensor. Must be: `[1, stride_height, stride_width, 1]`. +// rates: The input stride for atrous morphological dilation. Must be: +// `[1, rate_height, rate_width, 1]`. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape `[batch, out_height, out_width, depth]`. +func Dilation2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, rates []int64, padding string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "Dilation2D", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softplus: `log(exp(features) + 1)`. +func Softplus(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Softplus", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MutableHashTableV2Attr is an optional argument to MutableHashTableV2. +type MutableHashTableV2Attr func(optionalAttr) + +// MutableHashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableHashTableV2Container(value string) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableHashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableHashTableV2SharedName(value string) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func MutableHashTableV2UseNodeNameSharing(value bool) MutableHashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// Creates an empty hash table. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableHashTableV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble. +// mean_gradients: A tensor with shape=[logits_dimension] with mean of gradients for a first node. +// mean_hessians: A tensor with shape=[logits_dimension] mean of hessians for a first node. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// +// Returns Bool, whether to continue bias centering. +func BoostedTreesCenterBias(scope *Scope, tree_ensemble_handle tf.Output, mean_gradients tf.Output, mean_hessians tf.Output, l1 tf.Output, l2 tf.Output) (continue_centering tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCenterBias", + Input: []tf.Input{ + tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// HashTableV2Attr is an optional argument to HashTableV2. +type HashTableV2Attr func(optionalAttr) + +// HashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func HashTableV2Container(value string) HashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// HashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func HashTableV2SharedName(value string) HashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// +// value: If true and shared_name is empty, the table is shared +// using the node name. +// If not specified, defaults to false +func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// Creates a non-initialized hash table. +// +// This op creates a hash table, specifying the type of its keys and values. +// Before using the table you will have to initialize it. After initialization the +// table will be immutable. +// +// Arguments: +// key_dtype: Type of the table keys. +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "HashTableV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Check if the input matches the regex pattern. +// +// The input is a string tensor of any shape. The pattern is a scalar +// string tensor which is applied to every element of the input tensor. +// The boolean values (True or False) of the output tensor indicate +// if the input matches the regex pattern provided. +// +// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Examples: +// +// >>> tf.strings.regex_full_match(["TF lib", "lib TF"], ".*lib$") +// +// >>> tf.strings.regex_full_match(["TF lib", "lib TF"], ".*TF$") +// +// +// Arguments: +// input: A string tensor of the text to be processed. +// pattern: A scalar string tensor containing the regular expression to match the input. +// +// Returns A bool tensor with the same shape as `input`. +func RegexFullMatch(scope *Scope, input tf.Output, pattern tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RegexFullMatch", + Input: []tf.Input{ + input, pattern, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MatrixDiagV3Attr is an optional argument to MatrixDiagV3. +type MatrixDiagV3Attr func(optionalAttr) + +// MatrixDiagV3Align sets the optional align attribute to value. +// +// value: Some diagonals are shorter than `max_diag_len` and need to be padded. `align` is +// a string specifying how superdiagonals and subdiagonals should be aligned, +// respectively. There are four possible alignments: "RIGHT_LEFT" (default), +// "LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals +// to the right (left-pads the row) and subdiagonals to the left (right-pads the +// row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is +// the opposite alignment. +// If not specified, defaults to "RIGHT_LEFT" +func MatrixDiagV3Align(value string) MatrixDiagV3Attr { + return func(m optionalAttr) { + m["align"] = value + } +} + +// Returns a batched diagonal tensor with given batched diagonal values. +// +// Returns a tensor with the contents in `diagonal` as `k[0]`-th to `k[1]`-th +// diagonals of a matrix, with everything else padded with `padding`. `num_rows` +// and `num_cols` specify the dimension of the innermost matrix of the output. If +// both are not specified, the op assumes the innermost matrix is square and infers +// its size from `k` and the innermost dimension of `diagonal`. If only one of them +// is specified, the op assumes the unspecified value is the smallest possible +// based on other criteria. +// +// Let `diagonal` have `r` dimensions `[I, J, ..., L, M, N]`. The output tensor has +// rank `r+1` with shape `[I, J, ..., L, M, num_rows, num_cols]` when only one +// diagonal is given (`k` is an integer or `k[0] == k[1]`). Otherwise, it has rank +// `r` with shape `[I, J, ..., L, num_rows, num_cols]`. +// +// The second innermost dimension of `diagonal` has double meaning. +// When `k` is scalar or `k[0] == k[1]`, `M` is part of the batch size +// [I, J, ..., M], and the output tensor is: +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, n-max(d_upper, 0)] ; if n - m == d_upper +// padding_value ; otherwise +// ``` +// +// Otherwise, `M` is treated as the number of diagonals for the matrix in the +// same batch (`M = k[1]-k[0]+1`), and the output tensor is: +// +// ``` +// output[i, j, ..., l, m, n] +// = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1] +// padding_value ; otherwise +// ``` +// where `d = n - m`, `diag_index = [k] - d`, and +// `index_in_diag = n - max(d, 0) + offset`. +// +// `offset` is zero except when the alignment of the diagonal is to the right. +// ``` +// offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT} +// and `d >= 0`) or +// (`align` in {LEFT_RIGHT, RIGHT_RIGHT} +// and `d <= 0`) +// 0 ; otherwise +// ``` +// where `diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))`. +// +// For example: +// +// ``` +// # The main diagonal. +// diagonal = np.array([[1, 2, 3, 4], # Input shape: (2, 4) +// [5, 6, 7, 8]]) +// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0], # Output shape: (2, 4, 4) +// [0, 2, 0, 0], +// [0, 0, 3, 0], +// [0, 0, 0, 4]], +// [[5, 0, 0, 0], +// [0, 6, 0, 0], +// [0, 0, 7, 0], +// [0, 0, 0, 8]]] +// +// # A superdiagonal (per batch). +// diagonal = np.array([[1, 2, 3], # Input shape: (2, 3) +// [4, 5, 6]]) +// tf.matrix_diag(diagonal, k = 1) +// ==> [[[0, 1, 0, 0], # Output shape: (2, 4, 4) +// [0, 0, 2, 0], +// [0, 0, 0, 3], +// [0, 0, 0, 0]], +// [[0, 4, 0, 0], +// [0, 0, 5, 0], +// [0, 0, 0, 6], +// [0, 0, 0, 0]]] +// +// # A tridiagonal band (per batch). +// diagonals = np.array([[[0, 8, 9], # Input shape: (2, 2, 3) +// [1, 2, 3], +// [4, 5, 0]], +// [[0, 2, 3], +// [6, 7, 9], +// [9, 1, 0]]]) +// tf.matrix_diag(diagonals, k = (-1, 1)) +// ==> [[[1, 8, 0], # Output shape: (2, 3, 3) +// [4, 2, 9], +// [0, 5, 3]], +// [[6, 2, 0], +// [9, 7, 3], +// [0, 1, 9]]] +// +// # LEFT_RIGHT alignment. +// diagonals = np.array([[[8, 9, 0], # Input shape: (2, 2, 3) +// [1, 2, 3], +// [0, 4, 5]], +// [[2, 3, 0], +// [6, 7, 9], +// [0, 9, 1]]]) +// tf.matrix_diag(diagonals, k = (-1, 1), align="LEFT_RIGHT") +// ==> [[[1, 8, 0], # Output shape: (2, 3, 3) +// [4, 2, 9], +// [0, 5, 3]], +// [[6, 2, 0], +// [9, 7, 3], +// [0, 1, 9]]] +// +// # Rectangular matrix. +// diagonal = np.array([1, 2]) # Input shape: (2) +// tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4) +// ==> [[0, 0, 0, 0], # Output shape: (3, 4) +// [1, 0, 0, 0], +// [0, 2, 0, 0]] +// +// # Rectangular matrix with inferred num_cols and padding_value = 9. +// tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9) +// ==> [[9, 9], # Output shape: (3, 2) +// [1, 9], +// [9, 2]] +// +// ``` +// +// Arguments: +// diagonal: Rank `r`, where `r >= 1` +// k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main +// diagonal, and negative value means subdiagonals. `k` can be a single integer +// (for a single diagonal) or a pair of integers specifying the low and high ends +// of a matrix band. `k[0]` must not be larger than `k[1]`. +// num_rows: The number of rows of the output matrix. If it is not provided, the op assumes +// the output matrix is a square matrix and infers the matrix size from k and the +// innermost dimension of `diagonal`. +// num_cols: The number of columns of the output matrix. If it is not provided, the op +// assumes the output matrix is a square matrix and infers the matrix size from +// k and the innermost dimension of `diagonal`. +// padding_value: The number to fill the area outside the specified diagonal band with. +// Default is 0. +// +// Returns Has rank `r+1` when `k` is an integer or `k[0] == k[1]`, rank `r` otherwise. +func MatrixDiagV3(scope *Scope, diagonal tf.Output, k tf.Output, num_rows tf.Output, num_cols tf.Output, padding_value tf.Output, optional ...MatrixDiagV3Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixDiagV3", + Input: []tf.Input{ + diagonal, k, num_rows, num_cols, padding_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high overlaps +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. N-by-n overlap values are supplied as square matrix, +// which allows for defining a custom overlap criterium (eg. intersection over union, +// intersection over area, etc.). +// +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// +// selected_indices = tf.image.non_max_suppression_with_overlaps( +// overlaps, scores, max_output_size, overlap_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// overlaps: A 2-D float tensor of shape `[num_boxes, num_boxes]` representing +// the n-by-n box overlap values. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// overlap_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionWithOverlaps(scope *Scope, overlaps tf.Output, scores tf.Output, max_output_size tf.Output, overlap_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionWithOverlaps", + Input: []tf.Input{ + overlaps, scores, max_output_size, overlap_threshold, score_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs all keys and values in the table. +// +// Arguments: +// table_handle: Handle to the table. +// +// +// +// Returns: +// keys: Vector of all keys present in the table. +// values: Tensor of all values in the table. Indexed in parallel with `keys`. +func LookupTableExportV2(scope *Scope, table_handle tf.Output, Tkeys tf.DataType, Tvalues tf.DataType) (keys tf.Output, values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"Tkeys": Tkeys, "Tvalues": Tvalues} + opspec := tf.OpSpec{ + Type: "LookupTableExportV2", + Input: []tf.Input{ + table_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// RetrieveTPUEmbeddingAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingAdagradParameters. +type RetrieveTPUEmbeddingAdagradParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdagradParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingAdagradParametersTableId(value int64) RetrieveTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdagradParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdagradParametersTableName(value string) RetrieveTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingAdagradParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdagradParametersConfig(value string) RetrieveTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve Adagrad embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the Adagrad optimization algorithm. +// accumulators: Parameter accumulators updated by the Adagrad optimization algorithm. +func RetrieveTPUEmbeddingAdagradParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradParametersAttr) (parameters tf.Output, accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdagradParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes the sum along sparse segments of a tensor divided by the sqrt of N. +// +// N is the size of the segment being reduced. +// +// Like `SparseSegmentSqrtN`, but allows missing ids in `segment_ids`. If an id is +// missing, the `output` tensor at that position will be zeroed. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// num_segments: Should equal the number of distinct segment IDs. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSqrtNWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSqrtNWithNumSegments", + Input: []tf.Input{ + data, indices, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the Cholesky decomposition of one or more square matrices. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. +// +// The input has to be symmetric and positive definite. Only the lower-triangular +// part of the input will be used for this operation. The upper-triangular part +// will not be read. +// +// The output is a tensor of the same shape as the input +// containing the Cholesky decompositions for all input submatrices `[..., :, :]`. +// +// **Note**: The gradient computation on GPU is faster for large matrices but +// not for large batch dimensions when the submatrices are small. In this +// case it might be faster to use the CPU. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M, M]`. +func Cholesky(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Cholesky", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Splits a tensor into a list. +// +// list[i] corresponds to lengths[i] tensors from the input tensor. +// The tensor must have rank at least 1 and contain exactly sum(lengths) elements. +// +// tensor: The input tensor. +// element_shape: A shape compatible with that of elements in the tensor. +// lengths: Vector of sizes of the 0th dimension of tensors in the list. +// output_handle: The list. +func TensorListSplit(scope *Scope, tensor tf.Output, element_shape tf.Output, lengths tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListSplit", + Input: []tf.Input{ + tensor, element_shape, lengths, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Check if the input matches the regex pattern. +// +// The input is a string tensor of any shape. The pattern is the +// regular expression to be matched with every element of the input tensor. +// The boolean values (True or False) of the output tensor indicate +// if the input matches the regex pattern provided. +// +// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: A string tensor of the text to be processed. +// pattern: The regular expression to match the input. +// +// Returns A bool tensor with the same shape as `input`. +func StaticRegexFullMatch(scope *Scope, input tf.Output, pattern string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pattern": pattern} + opspec := tf.OpSpec{ + Type: "StaticRegexFullMatch", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParseSingleSequenceExampleAttr is an optional argument to ParseSingleSequenceExample. +type ParseSingleSequenceExampleAttr func(optionalAttr) + +// ParseSingleSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value. +// +// value: A list of Ncontext_sparse types; the data types of data in +// each context Feature given in context_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleContextSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["context_sparse_types"] = value + } +} + +// ParseSingleSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_dense_types"] = value + } +} + +// ParseSingleSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value. +// +// value: A list of Ncontext_dense shapes; the shapes of data in +// each context Feature given in context_dense_keys. +// The number of elements in the Feature corresponding to context_dense_key[j] +// must always equal context_dense_shapes[j].NumEntries(). +// The shape of context_dense_values[j] will match context_dense_shapes[j]. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleContextDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["context_dense_shapes"] = value + } +} + +// ParseSingleSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value. +// +// value: A list of Nfeature_list_sparse types; the data types +// of data in each FeatureList given in feature_list_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_sparse_types"] = value + } +} + +// ParseSingleSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value. +// +// value: A list of Nfeature_list_dense shapes; the shapes of +// data in each FeatureList given in feature_list_dense_keys. +// The shape of each Feature in the FeatureList corresponding to +// feature_list_dense_key[j] must always equal +// feature_list_dense_shapes[j].NumEntries(). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSingleSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSingleSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_dense_shapes"] = value + } +} + +// Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors. +// +// Arguments: +// serialized: A scalar containing a binary serialized SequenceExample proto. +// feature_list_dense_missing_assumed_empty: A vector listing the +// FeatureList keys which may be missing from the SequenceExample. If the +// associated FeatureList is missing, it is treated as empty. By default, +// any FeatureList not listed in this vector must exist in the SequenceExample. +// context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars). +// The keys expected in the Examples' features associated with context_sparse +// values. +// context_dense_keys: A list of Ncontext_dense string Tensors (scalars). +// The keys expected in the SequenceExamples' context features associated with +// dense values. +// feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors +// (scalars). The keys expected in the FeatureLists associated with sparse +// values. +// feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars). +// The keys expected in the SequenceExamples' feature_lists associated +// with lists of dense values. +// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty). +// context_dense_defaults[j] provides default values +// when the SequenceExample's context map lacks context_dense_key[j]. +// If an empty Tensor is provided for context_dense_defaults[j], +// then the Feature context_dense_keys[j] is required. +// The input type is inferred from context_dense_defaults[j], even when it's +// empty. If context_dense_defaults[j] is not empty, its shape must match +// context_dense_shapes[j]. +// debug_name: A scalar containing the name of the serialized proto. +// May contain, for example, table key (descriptive) name for the +// corresponding serialized proto. This is purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty scalar if no name is available. +func ParseSingleSequenceExample(scope *Scope, serialized tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_sparse_keys []tf.Output, context_dense_keys []tf.Output, feature_list_sparse_keys []tf.Output, feature_list_dense_keys []tf.Output, context_dense_defaults []tf.Output, debug_name tf.Output, optional ...ParseSingleSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParseSingleSequenceExample", + Input: []tf.Input{ + serialized, feature_list_dense_missing_assumed_empty, tf.OutputList(context_sparse_keys), tf.OutputList(context_dense_keys), tf.OutputList(feature_list_sparse_keys), tf.OutputList(feature_list_dense_keys), tf.OutputList(context_dense_defaults), debug_name, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil { + scope.UpdateErr("ParseSingleSequenceExample", err) + return + } + return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values +} + +// Computes the number of elements in the given table. +// +// Arguments: +// table_handle: Handle to the table. +// +// Returns Scalar that contains number of elements in the table. +func LookupTableSizeV2(scope *Scope, table_handle tf.Output) (size tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableSizeV2", + Input: []tf.Input{ + table_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes inverse hyperbolic sine of x element-wise. +// +// Given an input tensor, this function computes inverse hyperbolic sine +// for every element in the tensor. Both input and output has a range of +// `[-inf, inf]`. +// +// ```python +// x = tf.constant([-float("inf"), -2, -0.5, 1, 1.2, 200, 10000, float("inf")]) +// tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 inf] +// ``` +func Asinh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Asinh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Looks up keys in a table, outputs the corresponding values. +// +// The tensor `keys` must of the same type as the keys of the table. +// The output `values` is of the type of the table values. +// +// The scalar `default_value` is the value output for keys not present in the +// table. It must also be of the same type as the table values. +// +// Arguments: +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. +// +// +// Returns Same shape as `keys`. Values found in the table, or `default_values` +// for missing keys. +func LookupTableFindV2(scope *Scope, table_handle tf.Output, keys tf.Output, default_value tf.Output) (values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableFindV2", + Input: []tf.Input{ + table_handle, keys, default_value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolGradAttr is an optional argument to MaxPoolGrad. +type MaxPoolGradAttr func(optionalAttr) + +// MaxPoolGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradDataFormat(value string) MaxPoolGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients w.r.t. the output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients w.r.t. the input to `max_pool`. +func MaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Rolls the elements of a tensor along an axis. +// +// The elements are shifted positively (towards larger indices) by the offset of +// `shift` along the dimension of `axis`. Negative `shift` values will shift +// elements in the opposite direction. Elements that roll passed the last position +// will wrap around to the first and vice versa. Multiple shifts along multiple +// axes may be specified. +// +// For example: +// +// ``` +// # 't' is [0, 1, 2, 3, 4] +// roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2] +// +// # shifting along multiple dimensions +// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +// roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]] +// +// # shifting along the same axis multiple times +// # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] +// roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] +// ``` +// +// Arguments: +// +// shift: Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which +// elements are shifted positively (towards larger indices) along the dimension +// specified by `axis[i]`. Negative shifts will roll the elements in the opposite +// direction. +// axis: Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift +// `shift[i]` should occur. If the same axis is referenced more than once, the +// total shift for that axis will be the sum of all the shifts that belong to that +// axis. +// +// Returns Has the same shape and size as the input. The elements are shifted +// positively (towards larger indices) by the offsets of `shift` along the +// dimensions of `axis`. +func Roll(scope *Scope, input tf.Output, shift tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Roll", + Input: []tf.Input{ + input, shift, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RestoreSliceAttr is an optional argument to RestoreSlice. +type RestoreSliceAttr func(optionalAttr) + +// RestoreSlicePreferredShard sets the optional preferred_shard attribute to value. +// +// value: Index of file to open first if multiple files match +// `file_pattern`. See the documentation for `Restore`. +// If not specified, defaults to -1 +func RestoreSlicePreferredShard(value int64) RestoreSliceAttr { + return func(m optionalAttr) { + m["preferred_shard"] = value + } +} + +// Restores a tensor from checkpoint files. +// +// This is like `Restore` except that restored tensor can be listed as filling +// only a slice of a larger tensor. `shape_and_slice` specifies the shape of the +// larger tensor and the slice that the restored tensor covers. +// +// The `shape_and_slice` input has the same format as the +// elements of the `shapes_and_slices` input of the `SaveSlices` op. +// +// Arguments: +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// shape_and_slice: Scalar. The shapes and slice specifications to use when +// restoring a tensors. +// dt: The type of the tensor to be restored. +// +// Returns The restored tensor. +func RestoreSlice(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, shape_and_slice tf.Output, dt tf.DataType, optional ...RestoreSliceAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dt": dt} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RestoreSlice", + Input: []tf.Input{ + file_pattern, tensor_name, shape_and_slice, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapUnstageAttr is an optional argument to OrderedMapUnstage. +type OrderedMapUnstageAttr func(optionalAttr) + +// OrderedMapUnstageCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapUnstageCapacity(value int64) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapUnstageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapUnstageMemoryLimit(value int64) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapUnstageContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapUnstageContainer(value string) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapUnstageSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapUnstageSharedName(value string) OrderedMapUnstageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes and returns the values associated with the key +// +// from the underlying container. If the underlying container +// does not contain this key, the op will block until it does. +func OrderedMapUnstage(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...OrderedMapUnstageAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapUnstage", + Input: []tf.Input{ + key, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("OrderedMapUnstage", err) + return + } + return values +} + +// SobolSampleAttr is an optional argument to SobolSample. +type SobolSampleAttr func(optionalAttr) + +// SobolSampleDtype sets the optional dtype attribute to value. +// +// value: The type of the sample. One of: `float32` or `float64`. +// If not specified, defaults to DT_FLOAT +func SobolSampleDtype(value tf.DataType) SobolSampleAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Generates points from the Sobol sequence. +// +// Creates a Sobol sequence with `num_results` samples. Each sample has dimension +// `dim`. Skips the first `skip` samples. +// +// Arguments: +// dim: Positive scalar `Tensor` representing each sample's dimension. +// num_results: Positive scalar `Tensor` of dtype int32. The number of Sobol points to return +// in the output. +// skip: Positive scalar `Tensor` of dtype int32. The number of initial points of the +// Sobol sequence to skip. +// +// Returns `Tensor` of samples from Sobol sequence with `shape` [num_results, dim]. +func SobolSample(scope *Scope, dim tf.Output, num_results tf.Output, skip tf.Output, optional ...SobolSampleAttr) (samples tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SobolSample", + Input: []tf.Input{ + dim, num_results, skip, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedReluAttr is an optional argument to QuantizedRelu. +type QuantizedReluAttr func(optionalAttr) + +// QuantizedReluOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedReluOutType(value tf.DataType) QuantizedReluAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Computes Quantized Rectified Linear: `max(features, 0)` +// +// Arguments: +// +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns: +// activations: Has the same output shape as "features". +// min_activations: The float value that the lowest quantized value represents. +// max_activations: The float value that the highest quantized value represents. +func QuantizedRelu(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedRelu", + Input: []tf.Input{ + features, min_features, max_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the next representable value of `x1` in the direction of `x2`, element-wise. +// +// This operation returns the same result as the C++ std::nextafter function. +// +// It can also return a subnormal number. +// +// @compatibility(cpp) +// Equivalent to C++ std::nextafter function. +// @end_compatibility +func NextAfter(scope *Scope, x1 tf.Output, x2 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NextAfter", + Input: []tf.Input{ + x1, x2, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Bucketizes 'input' based on 'boundaries'. +// +// For example, if the inputs are +// boundaries = [0, 10, 100] +// input = [[-5, 10000] +// [150, 10] +// [5, 100]] +// +// then the output will be +// output = [[0, 3] +// [3, 2] +// [1, 3]] +// +// Arguments: +// input: Any shape of Tensor contains with int or float type. +// boundaries: A sorted list of floats gives the boundary of the buckets. +// +// Returns Same shape with 'input', each value of input replaced with bucket index. +// +// @compatibility(numpy) +// Equivalent to np.digitize. +// @end_compatibility +func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"boundaries": boundaries} + opspec := tf.OpSpec{ + Type: "Bucketize", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the log of the absolute value of `Gamma(x)` element-wise. +// +// For positive numbers, this function computes log((input - 1)!) for every element in the tensor. +// `lgamma(5) = log((5-1)!) = log(4!) = log(24) = 3.1780539` +// +// Example: +// +// ```python +// x = tf.constant([0, 0.5, 1, 4.5, -4, -5.6]) +// tf.math.lgamma(x) ==> [inf, 0.5723649, 0., 2.4537368, inf, -4.6477685] +// ``` +func Lgamma(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Lgamma", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reads the value of a variable. +// +// The tensor returned by this operation is immutable. +// +// The value returned by this operation is guaranteed to be influenced by all the +// writes on which this operation depends directly or indirectly, and to not be +// influenced by any of the writes which depend directly or indirectly on this +// operation. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// dtype: the dtype of the value. +func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "ReadVariableOp", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes a range that covers the actual values present in a quantized tensor. +// +// Given a quantized tensor described by `(input, input_min, input_max)`, outputs a +// range that covers the actual values present in that tensor. This op is typically +// used to produce the `requested_output_min` and `requested_output_max` for +// `Requantize`. +// +// Arguments: +// +// input_min: The float value that the minimum quantized input value represents. +// input_max: The float value that the maximum quantized input value represents. +// +// Returns: +// output_min: The computed min output. +// output_max: the computed max output. +func RequantizationRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output) (output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RequantizationRange", + Input: []tf.Input{ + input, input_min, input_max, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Compare values of `input` to `threshold` and pack resulting bits into a `uint8`. +// +// Each comparison returns a boolean `true` (if `input_value > threshold`) +// or and `false` otherwise. +// +// This operation is useful for Locality-Sensitive-Hashing (LSH) and other +// algorithms that use hashing approximations of cosine and `L2` distances; +// codes can be generated from an input via: +// +// ```python +// codebook_size = 50 +// codebook_bits = codebook_size * 32 +// codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits], +// dtype=x.dtype, +// initializer=tf.orthogonal_initializer()) +// codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.) +// codes = tf.bitcast(codes, tf.int32) # go from uint8 to int32 +// # now codes has shape x.shape[:-1] + [codebook_size] +// ``` +// +// **NOTE**: Currently, the innermost dimension of the tensor must be divisible +// by 8. +// +// Given an `input` shaped `[s0, s1, ..., s_n]`, the output is +// a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`. +// +// Arguments: +// input: Values to compare against `threshold` and bitpack. +// threshold: Threshold to compare against. +// +// Returns The bitpacked comparisons. +func CompareAndBitpack(scope *Scope, input tf.Output, threshold tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CompareAndBitpack", + Input: []tf.Input{ + input, threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Tensor contraction according to Einstein summation convention. +// +// Implements generalized Tensor contraction and reduction. Each input Tensor must +// have a corresponding input subscript appearing in the comma-separated left-hand +// side of the equation. The right-hand side of the equation consists of the +// output subscript. The input subscripts and the output subscript should consist +// of zero or more named axis labels and at most one ellipsis (`...`). +// +// The named axis labels may be any single character other than those having +// special meaning, namely `,.->`. The behavior of this Op is undefined if it +// receives an ill-formatted equation; since the validation is done at +// graph-building time, we omit format validation checks at runtime. +// +// Note: This Op is *not* intended to be called by the user; instead users should +// call `tf.einsum` directly. It is a hidden Op used by `tf.einsum`. +// +// Operations are applied to the input(s) according to the following rules: +// +// (a) Generalized Diagonals: For input dimensions corresponding to axis labels +// appearing more than once in the same input subscript, we take the +// generalized (`k`-dimensional) diagonal. +// For example, in the equation `iii->i` with input shape `[3, 3, 3]`, the +// generalized diagonal would consist of `3` elements at indices `(0, 0, 0)`, +// `(1, 1, 1)` and `(2, 2, 2)` to create a Tensor of shape `[3]`. +// +// (b) Reduction: Axes corresponding to labels appearing only in one input +// subscript but not in the output subscript are summed over prior to Tensor +// contraction. +// For example, in the equation `ab,bc->b`, the axis labels `a` and `c` are +// the reduction axis labels. +// +// (c) Batch Dimensions: Axes corresponding to labels appearing in each of the +// input subscripts and also in the output subscript make up the batch +// dimensions in Tensor contraction. Unnamed axis labels corresponding to +// ellipsis (`...`) also correspond to batch dimensions. +// For example, for the equation denoting batch matrix multiplication, +// `bij,bjk->bik`, the axis label `b` corresponds to a batch dimension. +// +// (d) Contraction: In case of binary einsum, axes corresponding to labels +// appearing in two different inputs (and not in the output) are contracted +// against each other. +// Considering the batch matrix multiplication equation again +// (`bij,bjk->bik`), the contracted axis label is `j`. +// +// (e) Expand Diagonal: If the output subscripts contain repeated (explicit) axis +// labels, the opposite operation of (a) is applied. For example, in the +// equation `i->iii`, and input shape `[3]`, the output of shape `[3, 3, 3]` +// are all zeros, except for the (generalized) diagonal which is populated +// with values from the input. +// Note: This operation is not supported by `np.einsum` or `tf.einsum`; it is +// provided to enable computing the symbolic gradient of `tf.einsum`. +// +// The output subscripts must contain only labels appearing in at least one of the +// input subscripts. Furthermore, all dimensions mapping to the same axis label +// must be equal. +// +// Any of the input and output subscripts may contain at most a single ellipsis +// (`...`). These ellipsis are mapped against dimensions not corresponding to any +// named axis label. If two inputs contain ellipsis, then they are broadcasted +// according to standard NumPy broadcasting +// [rules](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). +// +// The broadcasted dimensions are placed in the corresponding location of the +// ellipsis in the output subscript. If the broadcasted dimensions are non-empty +// and the output subscripts do not contain ellipsis, then an InvalidArgument error +// is raised. +// +// @compatibility(numpy) +// Similar to [`numpy.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html). +// +// Comparison with `numpy.einsum`: +// +// * This Op only supports unary and binary forms of `numpy.einsum`. +// * This Op does not support implicit form. (i.e. equations without `->`). +// * This Op also supports repeated indices in the output subscript, which is not +// supported by `numpy.einsum`. +// @end_compatibility +// +// +// Arguments: +// inputs: List of 1 or 2 Tensors. +// equation: String describing the Einstein Summation operation; in the format of np.einsum. +// +// Returns Output Tensor with shape depending upon `equation`. +func Einsum(scope *Scope, inputs []tf.Output, equation string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"equation": equation} + opspec := tf.OpSpec{ + Type: "Einsum", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Convert the quantized 'input' tensor into a lower-precision 'output', using the +// +// actual distribution of the values to maximize the usage of the lower bit depth +// and adjusting the output min and max ranges accordingly. +// +// [input_min, input_max] are scalar floats that specify the range for the float +// interpretation of the 'input' data. For example, if input_min is -1.0f and +// input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 +// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. +// +// This operator tries to squeeze as much precision as possible into an output with +// a lower bit depth by calculating the actual min and max values found in the +// data. For example, maybe that quint16 input has no values lower than 16,384 and +// none higher than 49,152. That means only half the range is actually needed, all +// the float interpretations are between -0.5f and 0.5f, so if we want to compress +// the data into a quint8 output, we can use that range rather than the theoretical +// -1.0f to 1.0f that is suggested by the input min and max. +// +// In practice, this is most useful for taking output from operations like +// QuantizedMatMul that can produce higher bit-depth outputs than their inputs and +// may have large potential output ranges, but in practice have a distribution of +// input values that only uses a small fraction of the possible range. By feeding +// that output into this operator, we can reduce it from 32 bits down to 8 with +// minimal loss of accuracy. +// +// Arguments: +// +// input_min: The float value that the minimum quantized input value represents. +// input_max: The float value that the maximum quantized input value represents. +// out_type: The type of the output. Should be a lower bit depth than Tinput. +// +// Returns: +// output +// output_min: The float value that the minimum quantized output value represents. +// output_max: The float value that the maximum quantized output value represents. +func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "QuantizeDownAndShrinkRange", + Input: []tf.Input{ + input, input_min, input_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process. +// +// Note that the hash function may change from time to time. +// This functionality will be deprecated and it's recommended to use +// `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. +// +// Arguments: +// +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucket(scope *Scope, string_tensor tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucket", + Input: []tf.Input{ + string_tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softsign: `features / (abs(features) + 1)`. +func Softsign(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Softsign", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedAddAttr is an optional argument to QuantizedAdd. +type QuantizedAddAttr func(optionalAttr) + +// QuantizedAddToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedAddToutput(value tf.DataType) QuantizedAddAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// Returns x + y element-wise, working on quantized buffers. +// +// Arguments: +// +// +// min_x: The float value that the lowest quantized `x` value represents. +// max_x: The float value that the highest quantized `x` value represents. +// min_y: The float value that the lowest quantized `y` value represents. +// max_y: The float value that the highest quantized `y` value represents. +// +// Returns: +// z +// min_z: The float value that the lowest quantized output value represents. +// max_z: The float value that the highest quantized output value represents. +// +// *NOTE*: `QuantizedAdd` supports limited forms of broadcasting. More about +// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedAddAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedAdd", + Input: []tf.Input{ + x, y, min_x, max_x, min_y, max_y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ShuffleAndRepeatDatasetAttr is an optional argument to ShuffleAndRepeatDataset. +type ShuffleAndRepeatDatasetAttr func(optionalAttr) + +// ShuffleAndRepeatDatasetReshuffleEachIteration sets the optional reshuffle_each_iteration attribute to value. +// If not specified, defaults to true +func ShuffleAndRepeatDatasetReshuffleEachIteration(value bool) ShuffleAndRepeatDatasetAttr { + return func(m optionalAttr) { + m["reshuffle_each_iteration"] = value + } +} + +// Creates a dataset that shuffles and repeats elements from `input_dataset` +// +// pseudorandomly. +// +// Arguments: +// +// buffer_size: The number of output elements to buffer in an iterator over +// this dataset. Compare with the `min_after_dequeue` attr when creating a +// `RandomShuffleQueue`. +// seed: A scalar seed for the random number generator. If either `seed` or +// `seed2` is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// count: A scalar representing the number of times the underlying dataset +// should be repeated. The default is `-1`, which results in infinite repetition. +// +// +func ShuffleAndRepeatDataset(scope *Scope, input_dataset tf.Output, buffer_size tf.Output, seed tf.Output, seed2 tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ShuffleAndRepeatDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShuffleAndRepeatDataset", + Input: []tf.Input{ + input_dataset, buffer_size, seed, seed2, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedMulAttr is an optional argument to QuantizedMul. +type QuantizedMulAttr func(optionalAttr) + +// QuantizedMulToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedMulToutput(value tf.DataType) QuantizedMulAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// Returns x * y element-wise, working on quantized buffers. +// +// Arguments: +// +// +// min_x: The float value that the lowest quantized `x` value represents. +// max_x: The float value that the highest quantized `x` value represents. +// min_y: The float value that the lowest quantized `y` value represents. +// max_y: The float value that the highest quantized `y` value represents. +// +// Returns: +// z +// min_z: The float value that the lowest quantized output value represents. +// max_z: The float value that the highest quantized output value represents. +// +// *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about +// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedMulAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMul", + Input: []tf.Input{ + x, y, min_x, max_x, min_y, max_y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// CumulativeLogsumexpAttr is an optional argument to CumulativeLogsumexp. +type CumulativeLogsumexpAttr func(optionalAttr) + +// CumulativeLogsumexpExclusive sets the optional exclusive attribute to value. +// +// value: If `True`, perform exclusive cumulative log-sum-exp. +// If not specified, defaults to false +func CumulativeLogsumexpExclusive(value bool) CumulativeLogsumexpAttr { + return func(m optionalAttr) { + m["exclusive"] = value + } +} + +// CumulativeLogsumexpReverse sets the optional reverse attribute to value. +// +// value: A `bool` (default: False). +// If not specified, defaults to false +func CumulativeLogsumexpReverse(value bool) CumulativeLogsumexpAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Compute the cumulative product of the tensor `x` along `axis`. +// +// By default, this op performs an inclusive cumulative log-sum-exp, +// which means that the first +// element of the input is identical to the first element of the output: +// ```python +// tf.math.cumulative_logsumexp([a, b, c]) # => [a, log(exp(a) + exp(b)), log(exp(a) + exp(b) + exp(c))] +// ``` +// +// By setting the `exclusive` kwarg to `True`, an exclusive cumulative log-sum-exp is +// performed instead: +// ```python +// tf.cumulative_logsumexp([a, b, c], exclusive=True) # => [-inf, a, log(exp(a) * exp(b))] +// ``` +// Note that the neutral element of the log-sum-exp operation is `-inf`, +// however, for performance reasons, the minimal value representable by the +// floating point type is used instead. +// +// By setting the `reverse` kwarg to `True`, the cumulative log-sum-exp is performed in the +// opposite direction. +// +// Arguments: +// x: A `Tensor`. Must be one of the following types: `float16`, `float32`, `float64`. +// axis: A `Tensor` of type `int32` (default: 0). Must be in the range +// `[-rank(x), rank(x))`. +func CumulativeLogsumexp(scope *Scope, x tf.Output, axis tf.Output, optional ...CumulativeLogsumexpAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CumulativeLogsumexp", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyGradientDescentAttr is an optional argument to ResourceApplyGradientDescent. +type ResourceApplyGradientDescentAttr func(optionalAttr) + +// ResourceApplyGradientDescentUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, the subtraction will be protected by a lock; +// otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyGradientDescentUseLocking(value bool) ResourceApplyGradientDescentAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' by subtracting 'alpha' * 'delta' from it. +// +// Arguments: +// var_: Should be from a Variable(). +// alpha: Scaling factor. Must be a scalar. +// delta: The change. +// +// Returns the created operation. +func ResourceApplyGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, delta tf.Output, optional ...ResourceApplyGradientDescentAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyGradientDescent", + Input: []tf.Input{ + var_, alpha, delta, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the matrix logarithm of one or more square matrices: +// +// +// \\(log(exp(A)) = A\\) +// +// This op is only defined for complex matrices. If A is positive-definite and +// real, then casting to a complex matrix, taking the logarithm and casting back +// to a real matrix will give the correct result. +// +// This function computes the matrix logarithm using the Schur-Parlett algorithm. +// Details of the algorithm can be found in Section 11.6.2 of: +// Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008. +// ISBN 978-0-898716-46-7. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor of the same shape as the input +// containing the exponential for all input submatrices `[..., :, :]`. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M, M]`. +// +// @compatibility(scipy) +// Equivalent to scipy.linalg.logm +// @end_compatibility +func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixLogarithm", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseBincountAttr is an optional argument to SparseBincount. +type SparseBincountAttr func(optionalAttr) + +// SparseBincountBinaryOutput sets the optional binary_output attribute to value. +// +// value: bool; Whether the kernel should count the appearance or number of occurrences. +// If not specified, defaults to false +func SparseBincountBinaryOutput(value bool) SparseBincountAttr { + return func(m optionalAttr) { + m["binary_output"] = value + } +} + +// Counts the number of occurrences of each value in an integer array. +// +// Outputs a vector with length `size` and the same dtype as `weights`. If +// `weights` are empty, then index `i` stores the number of times the value `i` is +// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of +// the value in `weights` at each index where the corresponding value in `arr` is +// `i`. +// +// Values in `arr` outside of the range [0, size) are ignored. +// +// Arguments: +// indices: 2D int64 `Tensor`. +// values: 1D int `Tensor`. +// dense_shape: 1D int64 `Tensor`. +// size: non-negative int scalar `Tensor`. +// weights: is an int32, int64, float32, or float64 `Tensor` with the same +// shape as `input`, or a length-0 `Tensor`, in which case it acts as all weights +// equal to 1. +// +// Returns 1D `Tensor` with length equal to `size` or 2D `Tensor` with [batch_size, `size`]. +// The counts or summed weights for each value in the range [0, size). +func SparseBincount(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, size tf.Output, weights tf.Output, optional ...SparseBincountAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseBincount", + Input: []tf.Input{ + indices, values, dense_shape, size, weights, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Calculate product with tridiagonal matrix. +// +// Calculates product of two matrices, where left matrix is a tridiagonal matrix. +// +// Arguments: +// superdiag: Tensor of shape `[..., 1, M]`, representing superdiagonals of +// tri-diagonal matrices to the left of multiplication. Last element is ignored. +// maindiag: Tensor of shape `[..., 1, M]`, representing main diagonals of tri-diagonal +// matrices to the left of multiplication. +// subdiag: Tensor of shape `[..., 1, M]`, representing subdiagonals of tri-diagonal +// matrices to the left of multiplication. First element is ignored. +// rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of +// multiplication. +// +// Returns Tensor of shape `[..., M, N]` containing the product. +func TridiagonalMatMul(scope *Scope, superdiag tf.Output, maindiag tf.Output, subdiag tf.Output, rhs tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TridiagonalMatMul", + Input: []tf.Input{ + superdiag, maindiag, subdiag, rhs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` +// +// if < 0, `scale * features` otherwise. +// +// To be used together with +// `initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. +// For correct dropout, use `tf.contrib.nn.alpha_dropout`. +// +// See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) +func Selu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Selu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DenseBincountAttr is an optional argument to DenseBincount. +type DenseBincountAttr func(optionalAttr) + +// DenseBincountBinaryOutput sets the optional binary_output attribute to value. +// +// value: bool; Whether the kernel should count the appearance or number of occurrences. +// If not specified, defaults to false +func DenseBincountBinaryOutput(value bool) DenseBincountAttr { + return func(m optionalAttr) { + m["binary_output"] = value + } +} + +// Counts the number of occurrences of each value in an integer array. +// +// Outputs a vector with length `size` and the same dtype as `weights`. If +// `weights` are empty, then index `i` stores the number of times the value `i` is +// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of +// the value in `weights` at each index where the corresponding value in `arr` is +// `i`. +// +// Values in `arr` outside of the range [0, size) are ignored. +// +// Arguments: +// input: 1D or 2D int `Tensor`. +// size: non-negative int scalar `Tensor`. +// weights: is an int32, int64, float32, or float64 `Tensor` with the same +// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights +// equal to 1. +// +// Returns 1D `Tensor` with length equal to `size` or 2D `Tensor` with [batch_size, `size`]. +// The counts or summed weights for each value in the range [0, size). +func DenseBincount(scope *Scope, input tf.Output, size tf.Output, weights tf.Output, optional ...DenseBincountAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DenseBincount", + Input: []tf.Input{ + input, size, weights, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the complex conjugate of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// complex numbers that are the complex conjugate of each element in `input`. The +// complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the +// real part and *b* is the imaginary part. +// +// The complex conjugate returned by this operation is of the form \\(a - bj\\). +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] +// ``` +func Conj(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Conj", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ImagAttr is an optional argument to Imag. +type ImagAttr func(optionalAttr) + +// ImagTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func ImagTout(value tf.DataType) ImagAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the imaginary part of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the imaginary part of each element in `input`. All +// elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* +// is the real part and *b* is the imaginary part returned by this operation. +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.imag(input) ==> [4.75, 5.75] +// ``` +func Imag(scope *Scope, input tf.Output, optional ...ImagAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Imag", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RealAttr is an optional argument to Real. +type RealAttr func(optionalAttr) + +// RealTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func RealTout(value tf.DataType) RealAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the real part of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the real part of each element in `input`. All elements in +// `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real +// part returned by this operation and *b* is the imaginary part. +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.real(input) ==> [-2.25, 3.25] +// ``` +func Real(scope *Scope, input tf.Output, optional ...RealAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Real", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DequantizeAttr is an optional argument to Dequantize. +type DequantizeAttr func(optionalAttr) + +// DequantizeMode sets the optional mode attribute to value. +// If not specified, defaults to "MIN_COMBINED" +func DequantizeMode(value string) DequantizeAttr { + return func(m optionalAttr) { + m["mode"] = value + } +} + +// DequantizeNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func DequantizeNarrowRange(value bool) DequantizeAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// DequantizeAxis sets the optional axis attribute to value. +// If not specified, defaults to -1 +func DequantizeAxis(value int64) DequantizeAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// DequantizeDtype sets the optional dtype attribute to value. +// +// value: Type of the output tensor. Currently Dequantize supports float and bfloat16. +// If 'dtype' is 'bfloat16', it only supports 'MIN_COMBINED' mode. +// If not specified, defaults to DT_FLOAT +func DequantizeDtype(value tf.DataType) DequantizeAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Dequantize the 'input' tensor into a float or bfloat16 Tensor. +// +// [min_range, max_range] are scalar floats that specify the range for +// the output. The 'mode' attribute controls exactly which calculations are +// used to convert the float values to their quantized equivalents. +// +// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: +// +// ``` +// if T == qint8: in[i] += (range(T) + 1)/ 2.0 +// out[i] = min_range + (in[i]* (max_range - min_range) / range(T)) +// ``` +// here `range(T) = numeric_limits::max() - numeric_limits::min()` +// +// *MIN_COMBINED Mode Example* +// +// If the input comes from a QuantizedRelu6, the output type is +// quint8 (range of 0-255) but the possible range of QuantizedRelu6 is +// 0-6. The min_range and max_range values are therefore 0.0 and 6.0. +// Dequantize on quint8 will take each value, cast to float, and multiply +// by 6 / 255. +// Note that if quantizedtype is qint8, the operation will additionally add +// each value by 128 prior to casting. +// +// If the mode is 'MIN_FIRST', then this approach is used: +// +// ```c++ +// num_discrete_values = 1 << (# of bits in T) +// range_adjust = num_discrete_values / (num_discrete_values - 1) +// range = (range_max - range_min) * range_adjust +// range_scale = range / num_discrete_values +// const double offset_input = static_cast(input) - lowest_quantized; +// result = range_min + ((input - numeric_limits::min()) * range_scale) +// ``` +// +// If the mode is `SCALED`, dequantization is performed by multiplying each +// input value by a scaling_factor. (Thus an input of 0 always maps to 0.0). +// +// The scaling_factor is determined from `min_range`, `max_range`, and +// `narrow_range` in a way that is compatible with `QuantizeAndDequantize{V2|V3}` +// and `QuantizeV2`, using the following algorithm: +// +// ```c++ +// +// const int min_expected_T = std::numeric_limits::min() + +// (narrow_range ? 1 : 0); +// const int max_expected_T = std::numeric_limits::max(); +// const float max_expected_T = std::numeric_limits::max(); +// +// const float scale_factor = +// (std::numeric_limits::min() == 0) ? (max_range / max_expected_T) +// : std::max(min_range / min_expected_T, +// max_range / max_expected_T); +// ``` +// +// Arguments: +// +// min_range: The minimum scalar value possibly produced for the input. +// max_range: The maximum scalar value possibly produced for the input. +func Dequantize(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, optional ...DequantizeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Dequantize", + Input: []tf.Input{ + input, min_range, max_range, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ComplexAttr is an optional argument to Complex. +type ComplexAttr func(optionalAttr) + +// ComplexTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_COMPLEX64 +func ComplexTout(value tf.DataType) ComplexAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Converts two real numbers to a complex number. +// +// Given a tensor `real` representing the real part of a complex number, and a +// tensor `imag` representing the imaginary part of a complex number, this +// operation returns complex numbers elementwise of the form \\(a + bj\\), where +// *a* represents the `real` part and *b* represents the `imag` part. +// +// The input tensors `real` and `imag` must have the same shape. +// +// For example: +// +// ``` +// # tensor 'real' is [2.25, 3.25] +// # tensor `imag` is [4.75, 5.75] +// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] +// ``` +func Complex(scope *Scope, real tf.Output, imag tf.Output, optional ...ComplexAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Complex", + Input: []tf.Input{ + real, imag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNCanonicalToParamsV2Attr is an optional argument to CudnnRNNCanonicalToParamsV2. +type CudnnRNNCanonicalToParamsV2Attr func(optionalAttr) + +// CudnnRNNCanonicalToParamsV2RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNCanonicalToParamsV2RnnMode(value string) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNCanonicalToParamsV2InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNCanonicalToParamsV2InputMode(value string) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNCanonicalToParamsV2Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNCanonicalToParamsV2Direction(value string) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNCanonicalToParamsV2Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsV2Dropout(value float32) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNCanonicalToParamsV2Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsV2Seed(value int64) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNCanonicalToParamsV2Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsV2Seed2(value int64) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNCanonicalToParamsV2NumProj sets the optional num_proj attribute to value. +// If not specified, defaults to 0 +func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2Attr { + return func(m optionalAttr) { + m["num_proj"] = value + } +} + +// Converts CudnnRNN params from canonical form to usable form. It supports the projection in LSTM. +// +// Writes a set of weights into the opaque params buffer so they can be used in +// upcoming training or inferences. +// +// Note that the params buffer may not be compatible across different GPUs. So any +// save and restoration should be converted to and from the canonical weights and +// biases. +// +// num_layers: Specifies the number of layers in the RNN model. +// num_units: Specifies the size of the hidden state. +// input_size: Specifies the size of the input state. +// weights: the canonical form of weights that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// biases: the canonical form of biases that can be used for saving +// and restoration. They are more likely to be compatible across different +// generations. +// num_params_weights: number of weight parameter matrix for all layers. +// num_params_biases: number of bias parameter vector for all layers. +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// The actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. +// dir = (direction == bidirectional) ? 2 : 1 +// dropout: dropout probability. When set to 0., dropout is disabled. +// seed: the 1st part of a seed to initialize dropout. +// seed2: the 2nd part of a seed to initialize dropout. +// num_proj: The output dimensionality for the projection matrices. If None or 0, +// no projection is performed. +func CudnnRNNCanonicalToParamsV2(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, weights []tf.Output, biases []tf.Output, optional ...CudnnRNNCanonicalToParamsV2Attr) (params tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNCanonicalToParamsV2", + Input: []tf.Input{ + num_layers, num_units, input_size, tf.OutputList(weights), tf.OutputList(biases), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a sequence of numbers. +// +// This operation creates a sequence of numbers that begins at `start` and +// extends by increments of `delta` up to but not including `limit`. +// +// For example: +// +// ``` +// # 'start' is 3 +// # 'limit' is 18 +// # 'delta' is 3 +// tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] +// ``` +// +// Arguments: +// start: 0-D (scalar). First entry in the sequence. +// limit: 0-D (scalar). Upper limit of sequence, exclusive. +// delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. +// +// Returns 1-D. +func Range(scope *Scope, start tf.Output, limit tf.Output, delta tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Range", + Input: []tf.Input{ + start, limit, delta, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AnyAttr is an optional argument to Any. +type AnyAttr func(optionalAttr) + +// AnyKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func AnyKeepDims(value bool) AnyAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the "logical or" of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Any", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient of morphological 2-D dilation with respect to the input. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, depth]`. +// filter: 3-D with shape `[filter_height, filter_width, depth]`. +// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. +// strides: 1-D of length 4. The stride of the sliding window for each dimension of +// the input tensor. Must be: `[1, stride_height, stride_width, 1]`. +// rates: 1-D of length 4. The input stride for atrous morphological dilation. +// Must be: `[1, rate_height, rate_width, 1]`. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape `[batch, in_height, in_width, depth]`. +func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (in_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "Dilation2DBackpropInput", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AllAttr is an optional argument to All. +type AllAttr func(optionalAttr) + +// AllKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func AllKeepDims(value bool) AllAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the "logical and" of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func All(scope *Scope, input tf.Output, axis tf.Output, optional ...AllAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "All", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for SparseSegmentSqrtN. +// +// Returns tensor "output" with same shape as grad, except for dimension 0 whose +// value is output_dim0. +// +// Arguments: +// grad: gradient propagated to the SparseSegmentSqrtN op. +// indices: indices passed to the corresponding SparseSegmentSqrtN op. +// segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op. +// output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op. +func SparseSegmentSqrtNGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSqrtNGrad", + Input: []tf.Input{ + grad, indices, segment_ids, output_dim0, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the mean along sparse segments of a tensor. +// +// See `tf.sparse.segment_sum` for usage examples. +// +// Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first +// dimension, selecting a subset of dimension 0, specified by `indices`. +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentMean(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMean", + Input: []tf.Input{ + data, indices, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sum along sparse segments of a tensor. +// +// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is +// missing, the `output` tensor at that position will be zeroed. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/sparse#Segmentation) +// for an explanation of segments. +// +// For example: +// +// ```python +// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) +// +// tf.sparse_segment_sum_with_num_segments( +// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3) +// # => [[0 0 0 0] +// # [0 0 0 0] +// # [0 0 0 0]] +// +// tf.sparse_segment_sum_with_num_segments(c, +// tf.constant([0, 1]), +// tf.constant([0, 2], +// num_segments=4)) +// # => [[ 1 2 3 4] +// # [ 0 0 0 0] +// # [-1 -2 -3 -4] +// # [ 0 0 0 0]] +// ``` +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// num_segments: Should equal the number of distinct segment IDs. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `num_segments`. +func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSumWithNumSegments", + Input: []tf.Input{ + data, indices, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sum along sparse segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first +// dimension, selecting a subset of dimension 0, specified by `indices`. +// +// For example: +// +// ```python +// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) +// +// # Select two rows, one segment. +// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0])) +// # => [[0 0 0 0]] +// +// # Select two rows, two segment. +// tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1])) +// # => [[ 1 2 3 4] +// # [-1 -2 -3 -4]] +// +// # Select all rows, two segments. +// tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1])) +// # => [[0 0 0 0] +// # [5 6 7 8]] +// +// # Which is equivalent to: +// tf.segment_sum(c, tf.constant([0, 0, 1])) +// ``` +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSum(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSum", + Input: []tf.Input{ + data, indices, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output[i] = \sum_{j...} data[j...]\\) where the sum is over tuples `j...` such +// that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids` +// need not be sorted and need not cover all values in the full +// range of valid values. +// +// If the sum is empty for a given segment ID `i`, `output[i] = 0`. +// If the given segment ID `i` is negative, the value is dropped and will not be +// added to the sum of the segment. +// +// `num_segments` should equal the number of distinct segment IDs. +// +//
+// +//
+// +// ``` python +// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) +// tf.unsorted_segment_sum(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 5, 5, 5, 5], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A tensor whose shape is a prefix of `data.shape`. +// +// +// Returns Has same shape as data, except for the first `segment_ids.rank` +// dimensions, which are replaced with a single dimension which has size +// `num_segments`. +func UnsortedSegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnsortedSegmentSum", + Input: []tf.Input{ + data, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes arctangent of `y/x` element-wise, respecting signs of the arguments. +// +// This is the angle \( \theta \in [-\pi, \pi] \) such that +// \[ x = r \cos(\theta) \] +// and +// \[ y = r \sin(\theta) \] +// where \(r = \sqrt(x^2 + y^2) \). +func Atan2(scope *Scope, y tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Atan2", + Input: []tf.Input{ + y, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the product along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \prod_j data_j\\) where the product is over `j` such +// that `segment_ids[j] == i`. +// +// If the product is empty for a given segment ID `i`, `output[i] = 1`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_prod(c, tf.constant([0, 0, 1])) +// # ==> [[4, 6, 6, 4], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentProd", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyAdamWithAmsgradAttr is an optional argument to ResourceApplyAdamWithAmsgrad. +type ResourceApplyAdamWithAmsgradAttr func(optionalAttr) + +// ResourceApplyAdamWithAmsgradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdamWithAmsgradUseLocking(value bool) ResourceApplyAdamWithAmsgradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the Adam algorithm. +// +// $$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$ +// $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$ +// $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$ +// $$\hat{v}_t := max{\hat{v}_{t-1}, v_t}$$ +// $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$ +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// vhat: Should be from a Variable(). +// beta1_power: Must be a scalar. +// beta2_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdamWithAmsgrad(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, vhat tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamWithAmsgradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdamWithAmsgrad", + Input: []tf.Input{ + var_, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the sum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \sum_j data_j\\) where sum is over `j` such +// that `segment_ids[j] == i`. +// +// If the sum is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_sum(c, tf.constant([0, 0, 1])) +// # ==> [[5, 5, 5, 5], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentSum(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentSum", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ArgMinAttr is an optional argument to ArgMin. +type ArgMinAttr func(optionalAttr) + +// ArgMinOutputType sets the optional output_type attribute to value. +// If not specified, defaults to DT_INT64 +func ArgMinOutputType(value tf.DataType) ArgMinAttr { + return func(m optionalAttr) { + m["output_type"] = value + } +} + +// Returns the index with the smallest value across dimensions of a tensor. +// +// Note that in case of ties the identity of the return value is not guaranteed. +// +// Usage: +// ```python +// import tensorflow as tf +// a = [1, 10, 26.9, 2.8, 166.32, 62.3] +// b = tf.math.argmin(input = a) +// c = tf.keras.backend.eval(b) +// # c = 0 +// # here a[0] = 1 which is the smallest element of a across axis 0 +// ``` +// +// Arguments: +// +// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. +// Describes which dimension of the input Tensor to reduce across. For vectors, +// use dimension = 0. +func ArgMin(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ArgMin", + Input: []tf.Input{ + input, dimension, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the reverse mode backpropagated gradient of the Cholesky algorithm. +// +// For an explanation see "Differentiation of the Cholesky algorithm" by +// Iain Murray http://arxiv.org/abs/1602.07527. +// +// Arguments: +// l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`. +// Algorithm depends only on lower triangular part of the innermost matrices of +// this tensor. +// grad: df/dl where f is some scalar function. Shape is `[..., M, M]`. +// Algorithm depends only on lower triangular part of the innermost matrices of +// this tensor. +// +// Returns Symmetrized version of df/dA . Shape is `[..., M, M]` +func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CholeskyGrad", + Input: []tf.Input{ + l, grad, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reshapes a tensor. +// +// Given `tensor`, this operation returns a tensor that has the same values +// as `tensor` with shape `shape`. +// +// If one component of 1-D tensor `shape` is the special value -1, the size of that +// dimension is computed so that the total size remains constant. In particular, a +// `shape` of `[-1]` flattens into 1-D. At most one component of `shape` may be +// unknown. +// +// The `shape` must be 1-D and the operation returns a tensor with shape +// `shape` filled with the values of `tensor`. In this case, the number of elements +// implied by `shape` must be the same as the number of elements in `tensor`. +// +// It is an error if `shape` is not 1-D. +// +// For example: +// +// ``` +// # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] +// # tensor 't' has shape [9] +// reshape(t, [3, 3]) ==> [[1, 2, 3], +// [4, 5, 6], +// [7, 8, 9]] +// +// # tensor 't' is [[[1, 1], [2, 2]], +// # [[3, 3], [4, 4]]] +// # tensor 't' has shape [2, 2, 2] +// reshape(t, [2, 4]) ==> [[1, 1, 2, 2], +// [3, 3, 4, 4]] +// +// # tensor 't' is [[[1, 1, 1], +// # [2, 2, 2]], +// # [[3, 3, 3], +// # [4, 4, 4]], +// # [[5, 5, 5], +// # [6, 6, 6]]] +// # tensor 't' has shape [3, 2, 3] +// # pass '[-1]' to flatten 't' +// reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6] +// +// # -1 can also be used to infer the shape +// +// # -1 is inferred to be 9: +// reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], +// [4, 4, 4, 5, 5, 5, 6, 6, 6]] +// # -1 is inferred to be 2: +// reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], +// [4, 4, 4, 5, 5, 5, 6, 6, 6]] +// # -1 is inferred to be 3: +// reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1], +// [2, 2, 2], +// [3, 3, 3]], +// [[4, 4, 4], +// [5, 5, 5], +// [6, 6, 6]]] +// +// # tensor 't' is [7] +// # shape `[]` reshapes to a scalar +// reshape(t, []) ==> 7 +// ``` +// +// Arguments: +// +// shape: Defines the shape of the output tensor. +func Reshape(scope *Scope, tensor tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Reshape", + Input: []tf.Input{ + tensor, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SnapshotDatasetAttr is an optional argument to SnapshotDataset. +type SnapshotDatasetAttr func(optionalAttr) + +// SnapshotDatasetCompression sets the optional compression attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetCompression(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["compression"] = value + } +} + +// SnapshotDatasetReaderPathPrefix sets the optional reader_path_prefix attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetReaderPathPrefix(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["reader_path_prefix"] = value + } +} + +// SnapshotDatasetWriterPathPrefix sets the optional writer_path_prefix attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetWriterPathPrefix(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["writer_path_prefix"] = value + } +} + +// SnapshotDatasetShardSizeBytes sets the optional shard_size_bytes attribute to value. +// If not specified, defaults to 10737418240 +func SnapshotDatasetShardSizeBytes(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["shard_size_bytes"] = value + } +} + +// SnapshotDatasetPendingSnapshotExpirySeconds sets the optional pending_snapshot_expiry_seconds attribute to value. +// If not specified, defaults to 86400 +func SnapshotDatasetPendingSnapshotExpirySeconds(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["pending_snapshot_expiry_seconds"] = value + } +} + +// SnapshotDatasetNumReaderThreads sets the optional num_reader_threads attribute to value. +// If not specified, defaults to 1 +func SnapshotDatasetNumReaderThreads(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["num_reader_threads"] = value + } +} + +// SnapshotDatasetReaderBufferSize sets the optional reader_buffer_size attribute to value. +// If not specified, defaults to 1 +func SnapshotDatasetReaderBufferSize(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["reader_buffer_size"] = value + } +} + +// SnapshotDatasetNumWriterThreads sets the optional num_writer_threads attribute to value. +// If not specified, defaults to 1 +func SnapshotDatasetNumWriterThreads(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["num_writer_threads"] = value + } +} + +// SnapshotDatasetWriterBufferSize sets the optional writer_buffer_size attribute to value. +// If not specified, defaults to 1 +func SnapshotDatasetWriterBufferSize(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["writer_buffer_size"] = value + } +} + +// SnapshotDatasetShuffleOnRead sets the optional shuffle_on_read attribute to value. +// If not specified, defaults to false +func SnapshotDatasetShuffleOnRead(value bool) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["shuffle_on_read"] = value + } +} + +// SnapshotDatasetSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func SnapshotDatasetSeed(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// SnapshotDatasetSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func SnapshotDatasetSeed2(value int64) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// SnapshotDatasetMode sets the optional mode attribute to value. +// If not specified, defaults to "auto" +func SnapshotDatasetMode(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["mode"] = value + } +} + +// SnapshotDatasetSnapshotName sets the optional snapshot_name attribute to value. +// If not specified, defaults to "" +func SnapshotDatasetSnapshotName(value string) SnapshotDatasetAttr { + return func(m optionalAttr) { + m["snapshot_name"] = value + } +} + +// Creates a dataset that will write to / read from a snapshot. +// +// This dataset attempts to determine whether a valid snapshot exists at the +// `snapshot_path`, and reads from the snapshot in lieu of using `input_dataset`. +// If not, it will run the preprocessing pipeline as usual, and write out a +// snapshot of the data processed for future use. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// path: The path we should write snapshots to / read snapshots from. +// +// +func SnapshotDataset(scope *Scope, input_dataset tf.Output, path tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...SnapshotDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SnapshotDataset", + Input: []tf.Input{ + input_dataset, path, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ArgMaxAttr is an optional argument to ArgMax. +type ArgMaxAttr func(optionalAttr) + +// ArgMaxOutputType sets the optional output_type attribute to value. +// If not specified, defaults to DT_INT64 +func ArgMaxOutputType(value tf.DataType) ArgMaxAttr { + return func(m optionalAttr) { + m["output_type"] = value + } +} + +// Returns the index with the largest value across dimensions of a tensor. +// +// Note that in case of ties the identity of the return value is not guaranteed. +// +// Usage: +// ```python +// import tensorflow as tf +// a = [1, 10, 26.9, 2.8, 166.32, 62.3] +// b = tf.math.argmax(input = a) +// c = tf.keras.backend.eval(b) +// # c = 4 +// # here a[4] = 166.32 which is the largest element of a across axis 0 +// ``` +// +// Arguments: +// +// dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. +// Describes which dimension of the input Tensor to reduce across. For vectors, +// use dimension = 0. +func ArgMax(scope *Scope, input tf.Output, dimension tf.Output, optional ...ArgMaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ArgMax", + Input: []tf.Input{ + input, dimension, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeBilinearGradAttr is an optional argument to ResizeBilinearGrad. +type ResizeBilinearGradAttr func(optionalAttr) + +// ResizeBilinearGradAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and grad tensors are +// aligned. Defaults to false. +// If not specified, defaults to false +func ResizeBilinearGradAlignCorners(value bool) ResizeBilinearGradAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeBilinearGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBilinearGradHalfPixelCenters(value bool) ResizeBilinearGradAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Computes the gradient of bilinear interpolation. +// +// Arguments: +// grads: 4-D with shape `[batch, height, width, channels]`. +// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`, +// The image tensor that was resized. +// +// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. +// Gradients with respect to the input image. Input image must have been +// float or double. +func ResizeBilinearGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBilinearGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeBilinearGrad", + Input: []tf.Input{ + grads, original_image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxAttr is an optional argument to Max. +type MaxAttr func(optionalAttr) + +// MaxKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func MaxKeepDims(value bool) MaxAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the maximum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Max(scope *Scope, input tf.Output, axis tf.Output, optional ...MaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Max", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Get the value of the tensor specified by its handle. +// +// Arguments: +// handle: The handle for a tensor stored in the session state. +// dtype: The type of the output value. +// +// Returns The tensor for the given handle. +func GetSessionTensor(scope *Scope, handle tf.Output, dtype tf.DataType) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "GetSessionTensor", + Input: []tf.Input{ + handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs a tensor containing the reduction across all input tensors. +// +// Outputs a tensor containing the reduction across all input tensors passed to ops +// within the same `shared_name. +// +// The graph should be constructed so if one op runs with shared_name value `c`, +// then `num_devices` ops will run with shared_name value `c`. Failure to do so +// will cause the graph execution to fail to complete. +// +// input: the input to the reduction +// data: the value of the reduction across all `num_devices` devices. +// reduction: the reduction operation to perform. +// num_devices: The number of devices participating in this reduction. +// shared_name: Identifier that shared between ops of the same reduction. +func NcclAllReduce(scope *Scope, input tf.Output, reduction string, num_devices int64, shared_name string) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"reduction": reduction, "num_devices": num_devices, "shared_name": shared_name} + opspec := tf.OpSpec{ + Type: "NcclAllReduce", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MinAttr is an optional argument to Min. +type MinAttr func(optionalAttr) + +// MinKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func MinKeepDims(value bool) MinAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the minimum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Min", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SampleDistortedBoundingBoxV2Attr is an optional argument to SampleDistortedBoundingBoxV2. +type SampleDistortedBoundingBoxV2Attr func(optionalAttr) + +// SampleDistortedBoundingBoxV2Seed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to non-zero, the random number +// generator is seeded by the given `seed`. Otherwise, it is seeded by a random +// seed. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxV2Seed(value int64) SampleDistortedBoundingBoxV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// SampleDistortedBoundingBoxV2Seed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// SampleDistortedBoundingBoxV2AspectRatioRange sets the optional aspect_ratio_range attribute to value. +// +// value: The cropped area of the image must have an aspect ratio = +// width / height within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr { + return func(m optionalAttr) { + m["aspect_ratio_range"] = value + } +} + +// SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value. +// +// value: The cropped area of the image must contain a fraction of the +// supplied image within this range. +// If not specified, defaults to +func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { + return func(m optionalAttr) { + m["area_range"] = value + } +} + +// SampleDistortedBoundingBoxV2MaxAttempts sets the optional max_attempts attribute to value. +// +// value: Number of attempts at generating a cropped region of the image +// of the specified constraints. After `max_attempts` failures, return the entire +// image. +// If not specified, defaults to 100 +func SampleDistortedBoundingBoxV2MaxAttempts(value int64) SampleDistortedBoundingBoxV2Attr { + return func(m optionalAttr) { + m["max_attempts"] = value + } +} + +// SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes sets the optional use_image_if_no_bounding_boxes attribute to value. +// +// value: Controls behavior if no bounding boxes supplied. +// If true, assume an implicit bounding box covering the whole input. If false, +// raise an error. +// If not specified, defaults to false +func SampleDistortedBoundingBoxV2UseImageIfNoBoundingBoxes(value bool) SampleDistortedBoundingBoxV2Attr { + return func(m optionalAttr) { + m["use_image_if_no_bounding_boxes"] = value + } +} + +// Generate a single randomly distorted bounding box for an image. +// +// Bounding box annotations are often supplied in addition to ground-truth labels +// in image recognition or object localization tasks. A common technique for +// training such a system is to randomly distort an image while preserving +// its content, i.e. *data augmentation*. This Op outputs a randomly distorted +// localization of an object, i.e. bounding box, given an `image_size`, +// `bounding_boxes` and a series of constraints. +// +// The output of this Op is a single bounding box that may be used to crop the +// original image. The output is returned as 3 tensors: `begin`, `size` and +// `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the +// image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize +// what the bounding box looks like. +// +// Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The +// bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and +// height of the underlying image. +// +// For example, +// +// ```python +// # Generate a single distorted bounding box. +// begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( +// tf.shape(image), +// bounding_boxes=bounding_boxes) +// +// # Draw the bounding box in an image summary. +// image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), +// bbox_for_draw) +// tf.summary.image('images_with_box', image_with_box) +// +// # Employ the bounding box to distort the image. +// distorted_image = tf.slice(image, begin, size) +// ``` +// +// Note that if no bounding box information is available, setting +// `use_image_if_no_bounding_boxes = true` will assume there is a single implicit +// bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is +// false and no bounding boxes are supplied, an error is raised. +// +// Arguments: +// image_size: 1-D, containing `[height, width, channels]`. +// bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes +// associated with the image. +// min_object_covered: The cropped area of the image must contain at least this +// fraction of any bounding box supplied. The value of this parameter should be +// non-negative. In the case of 0, the cropped area does not need to overlap +// any of the bounding boxes supplied. +// +// Returns: +// begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to +// `tf.slice`. +// size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to +// `tf.slice`. +// bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box. +// Provide as input to `tf.image.draw_bounding_boxes`. +func SampleDistortedBoundingBoxV2(scope *Scope, image_size tf.Output, bounding_boxes tf.Output, min_object_covered tf.Output, optional ...SampleDistortedBoundingBoxV2Attr) (begin tf.Output, size tf.Output, bboxes tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SampleDistortedBoundingBoxV2", + Input: []tf.Input{ + image_size, bounding_boxes, min_object_covered, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// EigAttr is an optional argument to Eig. +type EigAttr func(optionalAttr) + +// EigComputeV sets the optional compute_v attribute to value. +// +// value: If `True` then eigenvectors will be computed and returned in `v`. +// Otherwise, only the eigenvalues will be computed. +// If not specified, defaults to true +func EigComputeV(value bool) EigAttr { + return func(m optionalAttr) { + m["compute_v"] = value + } +} + +// Computes the eigen decomposition of one or more square matrices. +// +// Computes the eigenvalues and (optionally) right eigenvectors of each inner matrix in +// `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. The eigenvalues +// are sorted in non-decreasing order. +// +// ```python +// # a is a tensor. +// # e is a tensor of eigenvalues. +// # v is a tensor of eigenvectors. +// e, v = eig(a) +// e = eig(a, compute_v=False) +// ``` +// +// Arguments: +// input: `Tensor` input of shape `[N, N]`. +// +// +// Returns: +// e: Eigenvalues. Shape is `[N]`. +// v: Eigenvectors. Shape is `[N, N]`. +func Eig(scope *Scope, input tf.Output, Tout tf.DataType, optional ...EigAttr) (e tf.Output, v tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"Tout": Tout} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Eig", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ProdAttr is an optional argument to Prod. +type ProdAttr func(optionalAttr) + +// ProdKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func ProdKeepDims(value bool) ProdAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the product of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Prod(scope *Scope, input tf.Output, axis tf.Output, optional ...ProdAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Prod", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SumAttr is an optional argument to Sum. +type SumAttr func(optionalAttr) + +// SumKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SumKeepDims(value bool) SumAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the sum of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Sum(scope *Scope, input tf.Output, axis tf.Output, optional ...SumAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Sum", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesQuantileStreamResourceFlushAttr is an optional argument to BoostedTreesQuantileStreamResourceFlush. +type BoostedTreesQuantileStreamResourceFlushAttr func(optionalAttr) + +// BoostedTreesQuantileStreamResourceFlushGenerateQuantiles sets the optional generate_quantiles attribute to value. +// +// value: bool; If True, the output will be the num_quantiles for each stream where the ith +// entry is the ith quantile of the input with an approximation error of epsilon. +// Duplicate values may be present. +// If False, the output will be the points in the histogram that we got which roughly +// translates to 1/epsilon boundaries and without any duplicates. +// Default to False. +// If not specified, defaults to false +func BoostedTreesQuantileStreamResourceFlushGenerateQuantiles(value bool) BoostedTreesQuantileStreamResourceFlushAttr { + return func(m optionalAttr) { + m["generate_quantiles"] = value + } +} + +// Flush the summaries for a quantile stream resource. +// +// An op that flushes the summaries for a quantile stream resource. +// +// Arguments: +// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. +// num_buckets: int; approximate number of buckets unless using generate_quantiles. +// +// Returns the created operation. +func BoostedTreesQuantileStreamResourceFlush(scope *Scope, quantile_stream_resource_handle tf.Output, num_buckets tf.Output, optional ...BoostedTreesQuantileStreamResourceFlushAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesQuantileStreamResourceFlush", + Input: []tf.Input{ + quantile_stream_resource_handle, num_buckets, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// WholeFileReaderV2Attr is an optional argument to WholeFileReaderV2. +type WholeFileReaderV2Attr func(optionalAttr) + +// WholeFileReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func WholeFileReaderV2Container(value string) WholeFileReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// WholeFileReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func WholeFileReaderV2SharedName(value string) WholeFileReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A Reader that outputs the entire contents of a file as a value. +// +// To use, enqueue filenames in a Queue. The output of ReaderRead will +// be a filename (key) and the contents of that file (value). +// +// Returns The handle to reference the Reader. +func WholeFileReaderV2(scope *Scope, optional ...WholeFileReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "WholeFileReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ShapeNAttr is an optional argument to ShapeN. +type ShapeNAttr func(optionalAttr) + +// ShapeNOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func ShapeNOutType(value tf.DataType) ShapeNAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns shape of tensors. +// +// This operation returns N 1-D integer tensors representing shape of `input[i]s`. +func ShapeN(scope *Scope, input []tf.Output, optional ...ShapeNAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ShapeN", + Input: []tf.Input{ + tf.OutputList(input), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("ShapeN", err) + return + } + return output +} + +// ImageSummaryAttr is an optional argument to ImageSummary. +type ImageSummaryAttr func(optionalAttr) + +// ImageSummaryMaxImages sets the optional max_images attribute to value. +// +// value: Max number of batch elements to generate images for. +// If not specified, defaults to 3 +// +// REQUIRES: value >= 1 +func ImageSummaryMaxImages(value int64) ImageSummaryAttr { + return func(m optionalAttr) { + m["max_images"] = value + } +} + +// ImageSummaryBadColor sets the optional bad_color attribute to value. +// +// value: Color to use for pixels with non-finite values. +// If not specified, defaults to > int_val:255 int_val:0 int_val:0 int_val:255 > +func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { + return func(m optionalAttr) { + m["bad_color"] = value + } +} + +// Outputs a `Summary` protocol buffer with images. +// +// The summary has up to `max_images` summary values containing images. The +// images are built from `tensor` which must be 4-D with shape `[batch_size, +// height, width, channels]` and where `channels` can be: +// +// * 1: `tensor` is interpreted as Grayscale. +// * 3: `tensor` is interpreted as RGB. +// * 4: `tensor` is interpreted as RGBA. +// +// The images have the same number of channels as the input tensor. For float +// input, the values are normalized one image at a time to fit in the range +// `[0, 255]`. `uint8` values are unchanged. The op uses two different +// normalization algorithms: +// +// * If the input values are all positive, they are rescaled so the largest one +// is 255. +// +// * If any input value is negative, the values are shifted so input value 0.0 +// is at 127. They are then rescaled so that either the smallest value is 0, +// or the largest one is 255. +// +// The `tag` argument is a scalar `Tensor` of type `string`. It is used to +// build the `tag` of the summary values: +// +// * If `max_images` is 1, the summary value tag is '*tag*/image'. +// * If `max_images` is greater than 1, the summary value tags are +// generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. +// +// The `bad_color` argument is the color to use in the generated images for +// non-finite input values. It is a `uint8` 1-D tensor of length `channels`. +// Each element must be in the range `[0, 255]` (It represents the value of a +// pixel in the output image). Non-finite values in the input tensor are +// replaced by this tensor in the output image. The default value is the color +// red. +// +// Arguments: +// tag: Scalar. Used to build the `tag` attribute of the summary values. +// tensor: 4-D of shape `[batch_size, height, width, channels]` where +// `channels` is 1, 3, or 4. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func ImageSummary(scope *Scope, tag tf.Output, tensor tf.Output, optional ...ImageSummaryAttr) (summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ImageSummary", + Input: []tf.Input{ + tag, tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CollectiveBcastSendAttr is an optional argument to CollectiveBcastSend. +type CollectiveBcastSendAttr func(optionalAttr) + +// CollectiveBcastSendCommunicationHint sets the optional communication_hint attribute to value. +// If not specified, defaults to "auto" +func CollectiveBcastSendCommunicationHint(value string) CollectiveBcastSendAttr { + return func(m optionalAttr) { + m["communication_hint"] = value + } +} + +// CollectiveBcastSendTimeoutSeconds sets the optional timeout_seconds attribute to value. +// If not specified, defaults to 0 +func CollectiveBcastSendTimeoutSeconds(value float32) CollectiveBcastSendAttr { + return func(m optionalAttr) { + m["timeout_seconds"] = value + } +} + +// Broadcasts a tensor value to one or more other devices. +func CollectiveBcastSend(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, shape tf.Shape, optional ...CollectiveBcastSendAttr) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CollectiveBcastSend", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CombinedNonMaxSuppressionAttr is an optional argument to CombinedNonMaxSuppression. +type CombinedNonMaxSuppressionAttr func(optionalAttr) + +// CombinedNonMaxSuppressionPadPerClass sets the optional pad_per_class attribute to value. +// +// value: If false, the output nmsed boxes, scores and classes +// are padded/clipped to `max_total_size`. If true, the +// output nmsed boxes, scores and classes are padded to be of length +// `max_size_per_class`*`num_classes`, unless it exceeds `max_total_size` in +// which case it is clipped to `max_total_size`. Defaults to false. +// If not specified, defaults to false +func CombinedNonMaxSuppressionPadPerClass(value bool) CombinedNonMaxSuppressionAttr { + return func(m optionalAttr) { + m["pad_per_class"] = value + } +} + +// CombinedNonMaxSuppressionClipBoxes sets the optional clip_boxes attribute to value. +// +// value: If true, assume the box coordinates are between [0, 1] and clip the output boxes +// if they fall beyond [0, 1]. If false, do not do clipping and output the box +// coordinates as it is. +// If not specified, defaults to true +func CombinedNonMaxSuppressionClipBoxes(value bool) CombinedNonMaxSuppressionAttr { + return func(m optionalAttr) { + m["clip_boxes"] = value + } +} + +// Greedily selects a subset of bounding boxes in descending order of score, +// +// This operation performs non_max_suppression on the inputs per batch, across +// all classes. +// Prunes away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system. Also note that +// this algorithm is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is the final boxes, scores and classes tensor +// returned after performing non_max_suppression. +// +// Arguments: +// boxes: A 4-D float tensor of shape `[batch_size, num_boxes, q, 4]`. If `q` is 1 then +// same boxes are used for all classes otherwise, if `q` is equal to number of +// classes, class-specific boxes are used. +// scores: A 3-D float tensor of shape `[batch_size, num_boxes, num_classes]` +// representing a single score corresponding to each box (each row of boxes). +// max_output_size_per_class: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression per class +// max_total_size: A scalar representing maximum number of boxes retained over all classes. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns: +// nmsed_boxes: A [batch_size, max_detections, 4] float32 tensor +// containing the non-max suppressed boxes. +// nmsed_scores: A [batch_size, max_detections] float32 tensor +// containing the scores for the boxes. +// nmsed_classes: A [batch_size, max_detections] float32 tensor +// containing the classes for the boxes. +// valid_detections: A [batch_size] int32 tensor indicating the number of +// valid detections per batch item. Only the top num_detections[i] entries in +// nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the +// entries are zero paddings. +func CombinedNonMaxSuppression(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size_per_class tf.Output, max_total_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output, optional ...CombinedNonMaxSuppressionAttr) (nmsed_boxes tf.Output, nmsed_scores tf.Output, nmsed_classes tf.Output, valid_detections tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CombinedNonMaxSuppression", + Input: []tf.Input{ + boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Returns the truth value of x AND y element-wise. +// +// *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func LogicalAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogicalAnd", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ApproximateEqualAttr is an optional argument to ApproximateEqual. +type ApproximateEqualAttr func(optionalAttr) + +// ApproximateEqualTolerance sets the optional tolerance attribute to value. +// If not specified, defaults to 1e-05 +func ApproximateEqualTolerance(value float32) ApproximateEqualAttr { + return func(m optionalAttr) { + m["tolerance"] = value + } +} + +// Returns the truth value of abs(x-y) < tolerance element-wise. +func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...ApproximateEqualAttr) (z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ApproximateEqual", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LowerBoundAttr is an optional argument to LowerBound. +type LowerBoundAttr func(optionalAttr) + +// LowerBoundOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func LowerBoundOutType(value tf.DataType) LowerBoundAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Applies lower_bound(sorted_search_values, values) along each row. +// +// Each set of rows with the same index in (sorted_inputs, values) is treated +// independently. The resulting row is the equivalent of calling +// `np.searchsorted(sorted_inputs, values, side='left')`. +// +// The result is not a global index to the entire +// `Tensor`, but rather just the index in the last dimension. +// +// A 2-D example: +// sorted_sequence = [[0, 3, 9, 9, 10], +// [1, 2, 3, 4, 5]] +// values = [[2, 4, 9], +// [0, 2, 6]] +// +// result = LowerBound(sorted_sequence, values) +// +// result == [[1, 2, 2], +// [0, 1, 5]] +// +// Arguments: +// sorted_inputs: 2-D Tensor where each row is ordered. +// values: 2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains +// the values that will be searched for in `sorted_search_values`. +// +// Returns A `Tensor` with the same shape as `values`. It contains the first scalar index +// into the last dimension where values can be inserted without changing the +// ordered property. +func LowerBound(scope *Scope, sorted_inputs tf.Output, values tf.Output, optional ...LowerBoundAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LowerBound", + Input: []tf.Input{ + sorted_inputs, values, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x > y) element-wise. +// +// *NOTE*: `Greater` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// Example: +// +// ```python +// x = tf.constant([5, 4, 6]) +// y = tf.constant([5, 2, 5]) +// tf.math.greater(x, y) ==> [False, True, True] +// +// x = tf.constant([5, 4, 6]) +// y = tf.constant([5]) +// tf.math.greater(x, y) ==> [False, False, True] +// ``` +func Greater(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Greater", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the polygamma function \\(\psi^{(n)}(x)\\). +// +// The polygamma function is defined as: +// +// +// \\(\psi^{(a)}(x) = \frac{d^a}{dx^a} \psi(x)\\) +// +// where \\(\psi(x)\\) is the digamma function. +// The polygamma function is defined only for non-negative integer orders \\a\\. +func Polygamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Polygamma", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Shuffle dimensions of x according to a permutation. +// +// The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: +// `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` +func Transpose(scope *Scope, x tf.Output, perm tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Transpose", + Input: []tf.Input{ + x, perm, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AssertAttr is an optional argument to Assert. +type AssertAttr func(optionalAttr) + +// AssertSummarize sets the optional summarize attribute to value. +// +// value: Print this many entries of each tensor. +// If not specified, defaults to 3 +func AssertSummarize(value int64) AssertAttr { + return func(m optionalAttr) { + m["summarize"] = value + } +} + +// Asserts that the given condition is true. +// +// If `condition` evaluates to false, print the list of tensors in `data`. +// `summarize` determines how many entries of the tensors to print. +// +// Arguments: +// condition: The condition to evaluate. +// data: The tensors to print out when condition is false. +// +// Returns the created operation. +func Assert(scope *Scope, condition tf.Output, data []tf.Output, optional ...AssertAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Assert", + Input: []tf.Input{ + condition, tf.OutputList(data), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the gradient of `igamma(a, x)` wrt `a`. +func IgammaGradA(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IgammaGradA", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the upper regularized incomplete Gamma function `Q(a, x)`. +// +// The upper regularized incomplete Gamma function is defined as: +// +// \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\) +// +// where +// +// \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\) +// +// is the upper incomplete Gama function. +// +// Note, above `P(a, x)` (`Igamma`) is the lower regularized complete +// Gamma function. +func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Igammac", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise remainder of division. This emulates C semantics in that +// +// the result here is consistent with a truncating divide. E.g. `truncate(x / y) * +// y + truncate_mod(x, y) = x`. +// +// *NOTE*: `TruncateMod` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func TruncateMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TruncateMod", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise remainder of division. This emulates C semantics in that +// +// the result here is consistent with a truncating divide. E.g. +// `tf.truncatediv(x, y) * y + truncate_mod(x, y) = x`. +// +// *NOTE*: `Mod` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Mod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Mod", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A substitute for `InterleaveDataset` on a fixed list of `N` datasets. +// +// Arguments: +// selector_input_dataset: A dataset of scalar `DT_INT64` elements that determines which of the +// `N` data inputs should produce the next output element. +// data_input_datasets: `N` datasets with the same type that will be interleaved according to +// the values of `selector_input_dataset`. +// +// +func ExperimentalDirectedInterleaveDataset(scope *Scope, selector_input_dataset tf.Output, data_input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalDirectedInterleaveDataset", + Input: []tf.Input{ + selector_input_dataset, tf.OutputList(data_input_datasets), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the min of x and y (i.e. x < y ? x : y) element-wise. +// +// *NOTE*: `Minimum` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Minimum", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the max of x and y (i.e. x > y ? x : y) element-wise. +// +// *NOTE*: `Maximum` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Maximum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Maximum", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns 0 if x == 0, and x * log(y) otherwise, elementwise. +func Xlogy(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Xlogy", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Increments variable pointed to by 'resource' until it reaches 'limit'. +// +// Arguments: +// resource: Should be from a scalar `Variable` node. +// limit: If incrementing ref would bring it above limit, instead generates an +// 'OutOfRange' error. +// +// +// Returns A copy of the input before increment. If nothing else modifies the +// input, the values produced will all be distinct. +func ResourceCountUpTo(scope *Scope, resource tf.Output, limit int64, T tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"limit": limit, "T": T} + opspec := tf.OpSpec{ + Type: "ResourceCountUpTo", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatefulStandardNormalAttr is an optional argument to StatefulStandardNormal. +type StatefulStandardNormalAttr func(optionalAttr) + +// StatefulStandardNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulStandardNormalDtype(value tf.DataType) StatefulStandardNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from a normal distribution. This op is deprecated in favor of op 'StatefulStandardNormalV2' +// +// DEPRECATED at GraphDef version 29: Use StatefulStandardNormalV2 instead +// +// The generated values will have mean 0 and standard deviation 1. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// shape: The shape of the output tensor. +// +// Returns A tensor of the specified shape filled with random normal values. +func StatefulStandardNormal(scope *Scope, resource tf.Output, shape tf.Output, optional ...StatefulStandardNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulStandardNormal", + Input: []tf.Input{ + resource, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x / y element-wise for real types. +// +// If `x` and `y` are reals, this will return the floating-point division. +// +// *NOTE*: `Div` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RealDiv", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x / y element-wise for integer types. +// +// Truncation designates that negative numbers will round fractional quantities +// toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different +// than Python semantics. See `FloorDiv` for a division function that matches +// Python Semantics. +// +// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TruncateDiv", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns 0 if the denominator is zero. +// +// +// *NOTE*: `DivNoNan` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func DivNoNan(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DivNoNan", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Scatter `updates` into an existing tensor according to `indices`. +// +// This operation creates a new tensor by applying sparse `updates` to the passed +// in `tensor`. +// This operation is very similar to `tf.scatter_nd`, except that the updates are +// scattered onto an existing tensor (as opposed to a zero-tensor). If the memory +// for the existing tensor cannot be re-used, a copy is made and updated. +// +// If `indices` contains duplicates, then their updates are accumulated (summed). +// +// **WARNING**: The order in which updates are applied is nondeterministic, so the +// output will be nondeterministic if `indices` contains duplicates -- because +// of some numerical approximation issues, numbers summed in different order +// may yield different results. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of scatter is to insert individual elements in a tensor by +// index. For example, say we want to insert 4 scattered elements in a rank-1 +// tensor with 8 elements. +// +//
+// +//
+// +// In Python, this scatter operation would look like this: +// +// >>> indices = tf.constant([[4], [3], [1], [7]]) +// >>> updates = tf.constant([9, 10, 11, 12]) +// >>> tensor = tf.ones([8], dtype=tf.int32) +// >>> print(tf.tensor_scatter_nd_update(tensor, indices, updates)) +// tf.Tensor([ 1 11 1 10 9 1 1 12], shape=(8,), dtype=int32) +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +// In Python, this scatter operation would look like this: +// +// >>> indices = tf.constant([[0], [2]]) +// >>> updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// ... [7, 7, 7, 7], [8, 8, 8, 8]], +// ... [[5, 5, 5, 5], [6, 6, 6, 6], +// ... [7, 7, 7, 7], [8, 8, 8, 8]]]) +// >>> tensor = tf.ones([4, 4, 4], dtype=tf.int32) +// >>> print(tf.tensor_scatter_nd_update(tensor, indices, updates).numpy()) +// [[[5 5 5 5] +// [6 6 6 6] +// [7 7 7 7] +// [8 8 8 8]] +// [[1 1 1 1] +// [1 1 1 1] +// [1 1 1 1] +// [1 1 1 1]] +// [[5 5 5 5] +// [6 6 6 6] +// [7 7 7 7] +// [8 8 8 8]] +// [[1 1 1 1] +// [1 1 1 1] +// [1 1 1 1] +// [1 1 1 1]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// tensor: Tensor to copy/update. +// indices: Index tensor. +// updates: Updates to scatter into output. +// +// Returns A new tensor with the given shape and updates applied according +// to the indices. +func TensorScatterUpdate(scope *Scope, tensor tf.Output, indices tf.Output, updates tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorScatterUpdate", + Input: []tf.Input{ + tensor, indices, updates, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains `count` elements from the `input_dataset`. +// +// Arguments: +// +// count: A scalar representing the number of elements from the `input_dataset` +// that should be taken. A value of `-1` indicates that all of `input_dataset` +// is taken. +// +// +func TakeDataset(scope *Scope, input_dataset tf.Output, count tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "TakeDataset", + Input: []tf.Input{ + input_dataset, count, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the last element of the input list as well as a list with all but that element. +// +// Fails if the list is empty. +// +// input_handle: the input list +// tensor: the withdrawn last element of the list +// element_dtype: the type of elements in the list +// element_shape: the shape of the output tensor +func TensorListPopBack(scope *Scope, input_handle tf.Output, element_shape tf.Output, element_dtype tf.DataType) (output_handle tf.Output, tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "TensorListPopBack", + Input: []tf.Input{ + input_handle, element_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// QueueDequeueManyV2Attr is an optional argument to QueueDequeueManyV2. +type QueueDequeueManyV2Attr func(optionalAttr) + +// QueueDequeueManyV2TimeoutMs sets the optional timeout_ms attribute to value. +// +// value: If the queue has fewer than n elements, this operation +// will block for up to timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueDequeueManyV2TimeoutMs(value int64) QueueDequeueManyV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Dequeues `n` tuples of one or more tensors from the given queue. +// +// If the queue is closed and there are fewer than `n` elements, then an +// OutOfRange error is returned. +// +// This operation concatenates queue-element component tensors along the +// 0th dimension to make a single component tensor. All of the components +// in the dequeued tuple will have size `n` in the 0th dimension. +// +// This operation has `k` outputs, where `k` is the number of components in +// the tuples stored in the given queue, and output `i` is the ith +// component of the dequeued tuple. +// +// N.B. If the queue is empty, this operation will block until `n` elements +// have been dequeued (or 'timeout_ms' elapses, if specified). +// +// Arguments: +// handle: The handle to a queue. +// n: The number of tuples to dequeue. +// component_types: The type of each component in a tuple. +// +// Returns One or more tensors that were dequeued as a tuple. +func QueueDequeueManyV2(scope *Scope, handle tf.Output, n tf.Output, component_types []tf.DataType, optional ...QueueDequeueManyV2Attr) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueDequeueManyV2", + Input: []tf.Input{ + handle, n, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("QueueDequeueManyV2", err) + return + } + return components +} + +// Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN. +// +// *NOTE*: `MulNoNan` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func MulNoNan(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MulNoNan", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AsStringAttr is an optional argument to AsString. +type AsStringAttr func(optionalAttr) + +// AsStringPrecision sets the optional precision attribute to value. +// +// value: The post-decimal precision to use for floating point numbers. +// Only used if precision > -1. +// If not specified, defaults to -1 +func AsStringPrecision(value int64) AsStringAttr { + return func(m optionalAttr) { + m["precision"] = value + } +} + +// AsStringScientific sets the optional scientific attribute to value. +// +// value: Use scientific notation for floating point numbers. +// If not specified, defaults to false +func AsStringScientific(value bool) AsStringAttr { + return func(m optionalAttr) { + m["scientific"] = value + } +} + +// AsStringShortest sets the optional shortest attribute to value. +// +// value: Use shortest representation (either scientific or standard) for +// floating point numbers. +// If not specified, defaults to false +func AsStringShortest(value bool) AsStringAttr { + return func(m optionalAttr) { + m["shortest"] = value + } +} + +// AsStringWidth sets the optional width attribute to value. +// +// value: Pad pre-decimal numbers to this width. +// Applies to both floating point and integer numbers. +// Only used if width > -1. +// If not specified, defaults to -1 +func AsStringWidth(value int64) AsStringAttr { + return func(m optionalAttr) { + m["width"] = value + } +} + +// AsStringFill sets the optional fill attribute to value. +// +// value: The value to pad if width > -1. If empty, pads with spaces. +// Another typical value is '0'. String cannot be longer than 1 character. +// If not specified, defaults to "" +func AsStringFill(value string) AsStringAttr { + return func(m optionalAttr) { + m["fill"] = value + } +} + +// Converts each entry in the given tensor to strings. +// +// Supports many numeric types and boolean. +// +// For Unicode, see the +// [https://www.tensorflow.org/tutorials/representation/unicode](Working with Unicode text) +// tutorial. +// +// Examples: +// +// >>> tf.strings.as_string([3, 2]) +// +// >>> tf.strings.as_string([3.1415926, 2.71828], precision=2).numpy() +// array([b'3.14', b'2.72'], dtype=object) +func AsString(scope *Scope, input tf.Output, optional ...AsStringAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AsString", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv3DBackpropFilterV2Attr is an optional argument to Conv3DBackpropFilterV2. +type Conv3DBackpropFilterV2Attr func(optionalAttr) + +// Conv3DBackpropFilterV2DataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv3DBackpropFilterV2Dilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 5. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of 3-D convolution with respect to the filter. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 5-D +// `[filter_depth, filter_height, filter_width, in_channels, out_channels]` +// tensor. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropFilterV2(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv3DBackpropFilterV2", + Input: []tf.Input{ + input, filter_sizes, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x + y element-wise. +// +// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AddV2", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UniformCandidateSamplerAttr is an optional argument to UniformCandidateSampler. +type UniformCandidateSamplerAttr func(optionalAttr) + +// UniformCandidateSamplerSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func UniformCandidateSamplerSeed(value int64) UniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// UniformCandidateSamplerSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func UniformCandidateSamplerSeed2(value int64) UniformCandidateSamplerAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Generates labels for candidate sampling with a uniform distribution. +// +// See explanations of candidate sampling and the data formats at +// go/candidate-sampling. +// +// For each batch, this op picks a single set of sampled candidate labels. +// +// The advantages of sampling candidates per-batch are simplicity and the +// possibility of efficient dense matrix multiplication. The disadvantage is that +// the sampled candidates must be chosen independently of the context and of the +// true labels. +// +// Arguments: +// true_classes: A batch_size * num_true matrix, in which each row contains the +// IDs of the num_true target_classes in the corresponding original label. +// num_true: Number of true labels per context. +// num_sampled: Number of candidates to randomly sample. +// unique: If unique is true, we sample with rejection, so that all sampled +// candidates in a batch are unique. This requires some approximation to +// estimate the post-rejection sampling probabilities. +// range_max: The sampler will sample integers from the interval [0, range_max). +// +// Returns: +// sampled_candidates: A vector of length num_sampled, in which each element is +// the ID of a sampled candidate. +// true_expected_count: A batch_size * num_true matrix, representing +// the number of times each candidate is expected to occur in a batch +// of sampled candidates. If unique=true, then this is a probability. +// sampled_expected_count: A vector of length num_sampled, for each sampled +// candidate representing the number of times the candidate is expected +// to occur in a batch of sampled candidates. If unique=true, then this is a +// probability. +func UniformCandidateSampler(scope *Scope, true_classes tf.Output, num_true int64, num_sampled int64, unique bool, range_max int64, optional ...UniformCandidateSamplerAttr) (sampled_candidates tf.Output, true_expected_count tf.Output, sampled_expected_count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true, "num_sampled": num_sampled, "unique": unique, "range_max": range_max} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UniformCandidateSampler", + Input: []tf.Input{ + true_classes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// TryRpcAttr is an optional argument to TryRpc. +type TryRpcAttr func(optionalAttr) + +// TryRpcProtocol sets the optional protocol attribute to value. +// +// value: RPC protocol to use. Empty string means use the default protocol. +// Options include 'grpc'. +// If not specified, defaults to "" +func TryRpcProtocol(value string) TryRpcAttr { + return func(m optionalAttr) { + m["protocol"] = value + } +} + +// TryRpcFailFast sets the optional fail_fast attribute to value. +// +// value: `boolean`. If `true` (default), then failures to connect +// (i.e., the server does not immediately respond) cause an RPC failure. +// If not specified, defaults to true +func TryRpcFailFast(value bool) TryRpcAttr { + return func(m optionalAttr) { + m["fail_fast"] = value + } +} + +// TryRpcTimeoutInMs sets the optional timeout_in_ms attribute to value. +// +// value: `int`. If `0` (default), then the kernel will run the RPC +// request and only time out if the RPC deadline passes or the session times out. +// If this value is greater than `0`, then the op will raise an exception if +// the RPC takes longer than `timeout_in_ms`. +// If not specified, defaults to 0 +func TryRpcTimeoutInMs(value int64) TryRpcAttr { + return func(m optionalAttr) { + m["timeout_in_ms"] = value + } +} + +// Perform batches of RPC requests. +// +// This op asynchronously performs either a single RPC request, or a batch +// of requests. RPC requests are defined by three main parameters: +// +// - `address` (the host+port or BNS address of the request) +// - `method` (the method name for the request) +// - `request` (the serialized proto string, or vector of strings, +// of the RPC request argument). +// +// For example, if you have an RPC service running on port localhost:2345, +// and its interface is configured with the following proto declaration: +// +// ``` +// service MyService { +// rpc MyMethod(MyRequestProto) returns (MyResponseProto) { +// } +// }; +// ``` +// +// then call this op with arguments: +// +// ``` +// address = "localhost:2345" +// method = "MyService/MyMethod" +// ``` +// +// The `request` tensor is a string tensor representing serialized `MyRequestProto` +// strings; and the output string tensor `response` will have the same shape +// and contain (upon successful completion) corresponding serialized +// `MyResponseProto` strings. +// +// For example, to send a single, empty, `MyRequestProto`, call +// this op with `request = ""`. To send 5 **parallel** empty requests, +// call this op with `request = ["", "", "", "", ""]`. +// +// More generally, one can create a batch of `MyRequestProto` serialized protos +// from regular batched tensors using the `encode_proto` op, and convert +// the response `MyResponseProto` serialized protos to batched tensors +// using the `decode_proto` op. +// +// **NOTE** Working with serialized proto strings is faster than instantiating +// actual proto objects in memory, so no performance degradation is expected +// compared to writing custom kernels for this workflow. +// +// Unlike the standard `Rpc` op, if the connection fails or the remote worker +// returns an error status, this op does **not** reraise the exception. +// Instead, the `status_code` and `status_message` entry for the corresponding RPC +// call is set with the error returned from the RPC call. The `response` tensor +// will contain valid response values for those minibatch entries whose RPCs did +// not fail; the rest of the entries will have empty strings. +// +// Arguments: +// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `method` and `request`. +// method: `0-D` or `1-D`. The method address on the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `request`. +// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `method`. +// +// Returns: +// response: Same shape as `request`. Serialized proto strings: the rpc responses. +// status_code: Same shape as `request`. Values correspond to tensorflow Status enum codes. +// status_message: Same shape as `request`. Values correspond to Status messages +// returned from the RPC calls. +func TryRpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...TryRpcAttr) (response tf.Output, status_code tf.Output, status_message tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TryRpc", + Input: []tf.Input{ + address, method, request, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResourceGatherAttr is an optional argument to ResourceGather. +type ResourceGatherAttr func(optionalAttr) + +// ResourceGatherBatchDims sets the optional batch_dims attribute to value. +// If not specified, defaults to 0 +func ResourceGatherBatchDims(value int64) ResourceGatherAttr { + return func(m optionalAttr) { + m["batch_dims"] = value + } +} + +// ResourceGatherValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func ResourceGatherValidateIndices(value bool) ResourceGatherAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Gather slices from the variable pointed to by `resource` according to `indices`. +// +// `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). +// Produces an output tensor with shape `indices.shape + params.shape[1:]` where: +// +// ```python +// # Scalar indices +// output[:, ..., :] = params[indices, :, ... :] +// +// # Vector indices +// output[i, :, ..., :] = params[indices[i], :, ... :] +// +// # Higher rank indices +// output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] +// ``` +func ResourceGather(scope *Scope, resource tf.Output, indices tf.Output, dtype tf.DataType, optional ...ResourceGatherAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceGather", + Input: []tf.Input{ + resource, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x + y element-wise. +// +// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Add", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise smallest integer not less than x. +func Ceil(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Ceil", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise largest integer not greater than x. +func Floor(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Floor", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the trignometric inverse tangent of x element-wise. +// +// The `tf.math.atan` operation returns the inverse of `tf.math.tan`, such that +// if `y = tf.math.tan(x)` then, `x = tf.math.atan(y)`. +// +// **Note**: The output of `tf.math.atan` will lie within the invertible range +// of tan, i.e (-pi/2, pi/2). +// +// For example: +// +// ```python +// # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)] +// x = tf.constant([1.047, 0.785]) +// y = tf.math.tan(x) # [1.731261, 0.99920404] +// +// tf.math.atan(y) # [1.047, 0.785] = x +// ``` +// +func Atan(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Atan", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes acos of x element-wise. +func Acos(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Acos", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FusedBatchNormV2Attr is an optional argument to FusedBatchNormV2. +type FusedBatchNormV2Attr func(optionalAttr) + +// FusedBatchNormV2Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormV2Epsilon(value float32) FusedBatchNormV2Attr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormV2ExponentialAvgFactor sets the optional exponential_avg_factor attribute to value. +// If not specified, defaults to 1 +func FusedBatchNormV2ExponentialAvgFactor(value float32) FusedBatchNormV2Attr { + return func(m optionalAttr) { + m["exponential_avg_factor"] = value + } +} + +// FusedBatchNormV2DataFormat sets the optional data_format attribute to value. +// +// value: The data format for x and y. Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormV2DataFormat(value string) FusedBatchNormV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormV2IsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormV2IsTraining(value bool) FusedBatchNormV2Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// offset: A 1D Tensor for offset, to shift to the normalized x. +// mean: A 1D Tensor for population mean. Used for inference only; +// must be empty for training. +// variance: A 1D Tensor for population variance. Used for inference only; +// must be empty for training. +// +// Returns: +// y: A 4D Tensor for output data. +// batch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow +// to compute the running mean. +// batch_variance: A 1D Tensor for the computed batch variance, to be used by +// TensorFlow to compute the running variance. +// reserve_space_1: A 1D Tensor for the computed batch mean, to be reused +// in the gradient computation. +// reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance +// in the cuDNN case), to be reused in the gradient computation. +func FusedBatchNormV2(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormV2Attr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormV2", + Input: []tf.Input{ + x, scale, offset, mean, variance, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Computes sine of x element-wise. +// +// Given an input tensor, this function computes sine of every +// element in the tensor. Input range is `(-inf, inf)` and +// output range is `[-1,1]`. +// +// ```python +// x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")]) +// tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan] +// ``` +func Sin(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sin", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PrintAttr is an optional argument to Print. +type PrintAttr func(optionalAttr) + +// PrintMessage sets the optional message attribute to value. +// +// value: A string, prefix of the error message. +// If not specified, defaults to "" +func PrintMessage(value string) PrintAttr { + return func(m optionalAttr) { + m["message"] = value + } +} + +// PrintFirstN sets the optional first_n attribute to value. +// +// value: Only log `first_n` number of times. -1 disables logging. +// If not specified, defaults to -1 +func PrintFirstN(value int64) PrintAttr { + return func(m optionalAttr) { + m["first_n"] = value + } +} + +// PrintSummarize sets the optional summarize attribute to value. +// +// value: Only print this many entries of each tensor. +// If not specified, defaults to 3 +func PrintSummarize(value int64) PrintAttr { + return func(m optionalAttr) { + m["summarize"] = value + } +} + +// Prints a list of tensors. +// +// Passes `input` through to `output` and prints `data` when evaluating. +// +// Arguments: +// input: The tensor passed to `output` +// data: A list of tensors to print out when op is evaluated. +// +// Returns = The unmodified `input` tensor +func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Print", + Input: []tf.Input{ + input, tf.OutputList(data), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the Approximate Minimum Degree (AMD) ordering of `input`. +// +// Computes the Approximate Minimum Degree (AMD) ordering for a sparse matrix. +// +// The returned permutation may be used to permute the rows and columns of the +// given sparse matrix. This typically results in permuted sparse matrix's sparse +// Cholesky (or other decompositions) in having fewer zero fill-in compared to +// decomposition of the original matrix. +// +// The input sparse matrix may have rank 2 or rank 3. The output Tensor, +// representing would then have rank 1 or 2 respectively, with the same batch +// shape as the input. +// +// Each component of the input sparse matrix must represent a square symmetric +// matrix; only the lower triangular part of the matrix is read. The values of the +// sparse matrix does not affect the returned permutation, only the sparsity +// pattern of the sparse matrix is used. Hence, a single AMD ordering may be +// reused for the Cholesky decompositions of sparse matrices with the same sparsity +// pattern but with possibly different values. +// +// Each batch component of the output permutation represents a permutation of `N` +// elements, where the input sparse matrix components each have `N` rows. That is, +// the component contains each of the integers `{0, .. N-1}` exactly once. The +// `i`th element represents the row index that the `i`th row maps to. +// +// Usage example: +// +// ```python +// from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops +// +// a_indices = np.array([[0, 0], [1, 1], [2, 1], [2, 2], [3, 3]]) +// a_values = np.array([1.0, 2.0, 1.0, 3.0, 4.0], np.float32) +// a_dense_shape = [4, 4] +// +// with tf.Session() as sess: +// # Define (COO format) SparseTensor over Numpy array. +// a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape) +// +// # Convert SparseTensors to CSR SparseMatrix. +// a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix( +// a_st.indices, a_st.values, a_st.dense_shape) +// +// # Obtain the AMD Ordering for the CSR SparseMatrix. +// ordering_amd = sparse_csr_matrix_ops.sparse_matrix_ordering_amd(sparse_matrix) +// +// ordering_amd_value = sess.run(ordering_amd) +// ``` +// +// `ordering_amd_value` stores the AMD ordering: `[1 2 3 0]`. +// +// input: A `CSRSparseMatrix`. +// +// Arguments: +// input: A `CSRSparseMatrix`. +// +// Returns The Approximate Minimum Degree (AMD) ordering of `input`. +func SparseMatrixOrderingAMD(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseMatrixOrderingAMD", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes Psi, the derivative of Lgamma (the log of the absolute value of +// +// `Gamma(x)`), element-wise. +func Digamma(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Digamma", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient for the tanh of `x` wrt its input. +// +// Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy` +// is the corresponding input gradient. +func TanhGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TanhGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FusedBatchNormAttr is an optional argument to FusedBatchNorm. +type FusedBatchNormAttr func(optionalAttr) + +// FusedBatchNormEpsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormEpsilon(value float32) FusedBatchNormAttr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormExponentialAvgFactor sets the optional exponential_avg_factor attribute to value. +// If not specified, defaults to 1 +func FusedBatchNormExponentialAvgFactor(value float32) FusedBatchNormAttr { + return func(m optionalAttr) { + m["exponential_avg_factor"] = value + } +} + +// FusedBatchNormDataFormat sets the optional data_format attribute to value. +// +// value: The data format for x and y. Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormDataFormat(value string) FusedBatchNormAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormIsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormIsTraining(value bool) FusedBatchNormAttr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// offset: A 1D Tensor for offset, to shift to the normalized x. +// mean: A 1D Tensor for population mean. Used for inference only; +// must be empty for training. +// variance: A 1D Tensor for population variance. Used for inference only; +// must be empty for training. +// +// Returns: +// y: A 4D Tensor for output data. +// batch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow +// to compute the running mean. +// batch_variance: A 1D Tensor for the computed batch variance, to be used by +// TensorFlow to compute the running variance. +// reserve_space_1: A 1D Tensor for the computed batch mean, to be reused +// in the gradient computation. +// reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance +// in the cuDNN case), to be reused in the gradient computation. +func FusedBatchNorm(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormAttr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNorm", + Input: []tf.Input{ + x, scale, offset, mean, variance, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// SparseMatMulAttr is an optional argument to SparseMatMul. +type SparseMatMulAttr func(optionalAttr) + +// SparseMatMulTransposeA sets the optional transpose_a attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeA(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatMulTransposeB sets the optional transpose_b attribute to value. +// If not specified, defaults to false +func SparseMatMulTransposeB(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatMulAIsSparse sets the optional a_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulAIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["a_is_sparse"] = value + } +} + +// SparseMatMulBIsSparse sets the optional b_is_sparse attribute to value. +// If not specified, defaults to false +func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { + return func(m optionalAttr) { + m["b_is_sparse"] = value + } +} + +// Multiply matrix "a" by matrix "b". +// +// The inputs must be two-dimensional matrices and the inner dimension of "a" must +// match the outer dimension of "b". Both "a" and "b" must be `Tensor`s not +// `SparseTensor`s. This op is optimized for the case where at least one of "a" or +// "b" is sparse, in the sense that they have a large proportion of zero values. +// The breakeven for using this versus a dense matrix multiply on one platform was +// 30% zero values in the sparse matrix. +// +// The gradient computation of this operation will only take advantage of sparsity +// in the input gradient when that gradient comes from a Relu. +func SparseMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Resizes the list. +// +// +// input_handle: the input list +// size: size of the output list +// +func TensorListResize(scope *Scope, input_handle tf.Output, size tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListResize", + Input: []tf.Input{ + input_handle, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes inverse hyperbolic tangent of x element-wise. +// +// Given an input tensor, this function computes inverse hyperbolic tangent +// for every element in the tensor. Input range is `[-1,1]` and output range is +// `[-inf, inf]`. If input is `-1`, output will be `-inf` and if the +// input is `1`, output will be `inf`. Values outside the range will have +// `nan` as output. +// +// ```python +// x = tf.constant([-float("inf"), -1, -0.5, 1, 0, 0.5, 10, float("inf")]) +// tf.math.atanh(x) ==> [nan -inf -0.54930615 inf 0. 0.54930615 nan nan] +// ``` +func Atanh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Atanh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes hyperbolic tangent of `x` element-wise. +// +// Given an input tensor, this function computes hyperbolic tangent of every +// element in the tensor. Input range is `[-inf, inf]` and +// output range is `[-1,1]`. +// +// ```python +// x = tf.constant([-float("inf"), -5, -0.5, 1, 1.2, 2, 3, float("inf")]) +// tf.math.tanh(x) ==> [-1. -0.99990916 -0.46211717 0.7615942 0.8336547 0.9640276 0.9950547 1.] +// ``` +func Tanh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Tanh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes hyperbolic sine of x element-wise. +// +// Given an input tensor, this function computes hyperbolic sine of every +// element in the tensor. Input range is `[-inf,inf]` and output range +// is `[-inf,inf]`. +// +// ```python +// x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")]) +// tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf] +// ``` +func Sinh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sinh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad. +type ResourceApplyProximalAdagradAttr func(optionalAttr) + +// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. +// +// accum += grad * grad +// prox_v = var - lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyProximalAdagrad", + Input: []tf.Input{ + var_, accum, lr, l1, l2, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Divides sparse updates into the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] /= updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] /= updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions multiply. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterDiv", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Computes the trignometric inverse sine of x element-wise. +// +// The `tf.math.asin` operation returns the inverse of `tf.math.sin`, such that +// if `y = tf.math.sin(x)` then, `x = tf.math.asin(y)`. +// +// **Note**: The output of `tf.math.asin` will lie within the invertible range +// of sine, i.e [-pi/2, pi/2]. +// +// For example: +// +// ```python +// # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)] +// x = tf.constant([1.047, 0.785]) +// y = tf.math.sin(x) # [0.8659266, 0.7068252] +// +// tf.math.asin(y) # [1.047, 0.785] = x +// ``` +// +func Asin(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Asin", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes natural logarithm of (1 + x) element-wise. +// +// I.e., \\(y = \log_e (1 + x)\\). +// +// Example: +// +// ```python +// x = tf.constant([0, 0.5, 1, 5]) +// tf.math.log1p(x) ==> [0., 0.4054651, 0.6931472, 1.7917595] +// ``` +func Log1p(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Log1p", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts the quantized `input` tensor into a lower-precision `output`. +// +// Converts the quantized `input` tensor into a lower-precision `output`, using the +// output range specified with `requested_output_min` and `requested_output_max`. +// +// `[input_min, input_max]` are scalar floats that specify the range for the float +// interpretation of the `input` data. For example, if `input_min` is -1.0f and +// `input_max` is 1.0f, and we are dealing with `quint16` quantized data, then a 0 +// value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. +// +// Arguments: +// +// input_min: The float value that the minimum quantized input value represents. +// input_max: The float value that the maximum quantized input value represents. +// requested_output_min: The float value that the minimum quantized output value represents. +// requested_output_max: The float value that the maximum quantized output value represents. +// out_type: The type of the output. Should be a lower bit depth than Tinput. +// +// Returns: +// output +// output_min: The requested_output_min value is copied into this output. +// output_max: The requested_output_max value is copied into this output. +func Requantize(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, out_type tf.DataType) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "Requantize", + Input: []tf.Input{ + input, input_min, input_max, requested_output_min, requested_output_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Conv2DBackpropInputAttr is an optional argument to Conv2DBackpropInput. +type Conv2DBackpropInputAttr func(optionalAttr) + +// Conv2DBackpropInputUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. +// If not specified, defaults to true +func Conv2DBackpropInputUseCudnnOnGpu(value bool) Conv2DBackpropInputAttr { + return func(m optionalAttr) { + m["use_cudnn_on_gpu"] = value + } +} + +// Conv2DBackpropInputExplicitPaddings sets the optional explicit_paddings attribute to value. +// +// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith +// dimension, the amount of padding inserted before and after the dimension is +// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If +// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. +// If not specified, defaults to <> +func Conv2DBackpropInputExplicitPaddings(value []int64) Conv2DBackpropInputAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// Conv2DBackpropInputDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv2DBackpropInputDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of convolution with respect to the input. +// +// Arguments: +// input_sizes: An integer vector representing the shape of `input`, +// where `input` is a 4-D `[batch, height, width, channels]` tensor. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. +// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. Must be in the same order as the dimension specified with +// format. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient +// w.r.t. the input of the convolution. +func Conv2DBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropInputAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv2DBackpropInput", + Input: []tf.Input{ + input_sizes, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes `exp(x) - 1` element-wise. +// +// i.e. `exp(x) - 1` or `e^(x) - 1`, where `x` is the input tensor. +// `e` denotes Euler's number and is approximately equal to 2.718281. +// +// ```python +// x = tf.constant(2.0) +// tf.math.expm1(x) ==> 6.389056 +// +// x = tf.constant([2.0, 8.0]) +// tf.math.expm1(x) ==> array([6.389056, 2979.958], dtype=float32) +// +// x = tf.constant(1 + 1j) +// tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j) +// ``` +func Expm1(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Expm1", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes exponential of x element-wise. \\(y = e^x\\). +// +// This function computes the exponential of every element in the input tensor. +// i.e. `exp(x)` or `e^(x)`, where `x` is the input tensor. +// `e` denotes Euler's number and is approximately equal to 2.718281. +// Output is positive for any real input. +// +// ```python +// x = tf.constant(2.0) +// tf.math.exp(x) ==> 7.389056 +// +// x = tf.constant([2.0, 8.0]) +// tf.math.exp(x) ==> array([7.389056, 2980.958], dtype=float32) +// ``` +// +// For complex numbers, the exponential value is calculated as follows: +// +// ``` +// e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y) +// ``` +// +// Let's consider complex number 1+1j as an example. +// e^1 * (cos 1 + i sin 1) = 2.7182818284590 * (0.54030230586+0.8414709848j) +// +// ```python +// x = tf.constant(1 + 1j) +// tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j +// ``` +func Exp(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Exp", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes square of x element-wise. +// +// I.e., \\(y = x * x = x^2\\). +func Square(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Square", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient for the inverse of `x` wrt its input. +// +// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` +// is the corresponding input gradient. +func ReciprocalGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReciprocalGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the reciprocal of x element-wise. +// +// I.e., \\(y = 1 / x\\). +func Inv(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Inv", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ComplexAbsAttr is an optional argument to ComplexAbs. +type ComplexAbsAttr func(optionalAttr) + +// ComplexAbsTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func ComplexAbsTout(value tf.DataType) ComplexAbsAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Computes the complex absolute value of a tensor. +// +// Given a tensor `x` of complex numbers, this operation returns a tensor of type +// `float` or `double` that is the absolute value of each element in `x`. All +// elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute +// value is computed as \\( \sqrt{a^2 + b^2}\\). +func ComplexAbs(scope *Scope, x tf.Output, optional ...ComplexAbsAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ComplexAbs", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the absolute value of a tensor. +// +// Given a tensor `x`, this operation returns a tensor containing the absolute +// value of each element in `x`. For example, if x is an input element and y is +// an output element, this operation computes \\(y = |x|\\). +func Abs(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Abs", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces a summary of any statistics recorded by the given statistics manager. +func ExperimentalStatsAggregatorSummary(scope *Scope, iterator tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExperimentalStatsAggregatorSummary", + Input: []tf.Input{ + iterator, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MeanAttr is an optional argument to Mean. +type MeanAttr func(optionalAttr) + +// MeanKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func MeanKeepDims(value bool) MeanAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the mean of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func Mean(scope *Scope, input tf.Output, axis tf.Output, optional ...MeanAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Mean", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomStandardNormalAttr is an optional argument to RandomStandardNormal. +type RandomStandardNormalAttr func(optionalAttr) + +// RandomStandardNormalSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomStandardNormalSeed(value int64) RandomStandardNormalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomStandardNormalSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomStandardNormalSeed2(value int64) RandomStandardNormalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// Arguments: +// shape: The shape of the output tensor. +// dtype: The type of the output. +// +// Returns A tensor of the specified shape filled with random normal values. +func RandomStandardNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomStandardNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomStandardNormal", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the Gauss error function of `x` element-wise. +func Erf(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Erf", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the maximum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \max_j(data_j)\\) where `max` is over `j` such +// that `segment_ids[j] == i`. +// +// If the max is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_max(c, tf.constant([0, 0, 1])) +// # ==> [[4, 3, 3, 4], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMax", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CastAttr is an optional argument to Cast. +type CastAttr func(optionalAttr) + +// CastTruncate sets the optional Truncate attribute to value. +// If not specified, defaults to false +func CastTruncate(value bool) CastAttr { + return func(m optionalAttr) { + m["Truncate"] = value + } +} + +// Cast x of type SrcT to y of DstT. +func Cast(scope *Scope, x tf.Output, DstT tf.DataType, optional ...CastAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"DstT": DstT} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Cast", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generate a sharded filename. The filename is printf formatted as +// +// %s-%05d-of-%05d, basename, shard, num_shards. +func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shards tf.Output) (filename tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ShardedFilename", + Input: []tf.Input{ + basename, shard, num_shards, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Elementwise computes the bitwise OR of `x` and `y`. +// +// The result will have those bits set, that are set in `x`, `y` or both. The +// computation is performed on the underlying representations of `x` and `y`. +// +// For example: +// +// ```python +// import tensorflow as tf +// from tensorflow.python.ops import bitwise_ops +// dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64, +// tf.uint8, tf.uint16, tf.uint32, tf.uint64] +// +// for dtype in dtype_list: +// lhs = tf.constant([0, 5, 3, 14], dtype=dtype) +// rhs = tf.constant([5, 0, 7, 11], dtype=dtype) +// exp = tf.constant([5, 5, 7, 15], dtype=tf.float32) +// +// res = bitwise_ops.bitwise_or(lhs, rhs) +// tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE +// ``` +// +func BitwiseOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BitwiseOr", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SendAttr is an optional argument to Send. +type SendAttr func(optionalAttr) + +// SendClientTerminated sets the optional client_terminated attribute to value. +// +// value: If set to true, this indicates that the node was added +// to the graph as a result of a client-side feed or fetch of Tensor data, +// in which case the corresponding send or recv is expected to be managed +// locally by the caller. +// If not specified, defaults to false +func SendClientTerminated(value bool) SendAttr { + return func(m optionalAttr) { + m["client_terminated"] = value + } +} + +// Sends the named tensor from send_device to recv_device. +// +// Arguments: +// tensor: The tensor to send. +// tensor_name: The name of the tensor to send. +// send_device: The name of the device sending the tensor. +// send_device_incarnation: The current incarnation of send_device. +// recv_device: The name of the device receiving the tensor. +// +// Returns the created operation. +func Send(scope *Scope, tensor tf.Output, tensor_name string, send_device string, send_device_incarnation int64, recv_device string, optional ...SendAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"tensor_name": tensor_name, "send_device": send_device, "send_device_incarnation": send_device_incarnation, "recv_device": recv_device} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Send", + Input: []tf.Input{ + tensor, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// BatchMatMulV2Attr is an optional argument to BatchMatMulV2. +type BatchMatMulV2Attr func(optionalAttr) + +// BatchMatMulV2AdjX sets the optional adj_x attribute to value. +// +// value: If `True`, adjoint the slices of `x`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulV2AdjX(value bool) BatchMatMulV2Attr { + return func(m optionalAttr) { + m["adj_x"] = value + } +} + +// BatchMatMulV2AdjY sets the optional adj_y attribute to value. +// +// value: If `True`, adjoint the slices of `y`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulV2AdjY(value bool) BatchMatMulV2Attr { + return func(m optionalAttr) { + m["adj_y"] = value + } +} + +// Multiplies slices of two tensors in batches. +// +// Multiplies all slices of `Tensor` `x` and `y` (each slice can be +// viewed as an element of a batch), and arranges the individual results +// in a single output tensor of the same batch size. Each of the +// individual slices can optionally be adjointed (to adjoint a matrix +// means to transpose and conjugate it) before multiplication by setting +// the `adj_x` or `adj_y` flag to `True`, which are by default `False`. +// +// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` +// and `[..., r_y, c_y]`. +// +// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: +// +// r_o = c_x if adj_x else r_x +// c_o = r_y if adj_y else c_y +// +// It is computed as: +// +// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) +// +// *NOTE*: `BatchMatMulV2` supports broadcasting in the batch dimensions. More +// about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). +// +// +// Arguments: +// x: 2-D or higher with shape `[..., r_x, c_x]`. +// y: 2-D or higher with shape `[..., r_y, c_y]`. +// +// Returns 3-D or higher with shape `[..., r_o, c_o]` +func BatchMatMulV2(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BatchMatMulV2", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns 0 if x == 0, and x / y otherwise, elementwise. +func Xdivy(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Xdivy", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the pairwise cross product. +// +// `a` and `b` must be the same shape; they can either be simple 3-element vectors, +// or any shape where the innermost dimension is 3. In the latter case, each pair +// of corresponding 3-element vectors is cross-multiplied independently. +// +// Arguments: +// a: A tensor containing 3-element vectors. +// b: Another tensor, of same type and shape as `a`. +// +// Returns Pairwise cross product of the vectors in `a` and `b`. +func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Cross", + Input: []tf.Input{ + a, b, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Sends `input` to all devices that are connected to the output. +// +// Sends `input` to all devices that are connected to the output. +// +// The graph should be constructed so that all ops connected to the output have a +// valid device assignment, and the op itself is assigned one of these devices. +// +// input: The input to the broadcast. +// output: The same as input. +// shape: The shape of the input tensor. +// +func NcclBroadcast(scope *Scope, input tf.Output, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "NcclBroadcast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv2DAttr is an optional argument to Conv2D. +type Conv2DAttr func(optionalAttr) + +// Conv2DUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. +// If not specified, defaults to true +func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr { + return func(m optionalAttr) { + m["use_cudnn_on_gpu"] = value + } +} + +// Conv2DExplicitPaddings sets the optional explicit_paddings attribute to value. +// +// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith +// dimension, the amount of padding inserted before and after the dimension is +// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If +// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. +// If not specified, defaults to <> +func Conv2DExplicitPaddings(value []int64) Conv2DAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// Conv2DDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func Conv2DDataFormat(value string) Conv2DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv2DDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv2DDilations(value []int64) Conv2DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 2-D convolution given 4-D `input` and `filter` tensors. +// +// Given an input tensor of shape `[batch, in_height, in_width, in_channels]` +// and a filter / kernel tensor of shape +// `[filter_height, filter_width, in_channels, out_channels]`, this op +// performs the following: +// +// 1. Flattens the filter to a 2-D matrix with shape +// `[filter_height * filter_width * in_channels, output_channels]`. +// 2. Extracts image patches from the input tensor to form a *virtual* +// tensor of shape `[batch, out_height, out_width, +// filter_height * filter_width * in_channels]`. +// 3. For each patch, right-multiplies the filter matrix and the image patch +// vector. +// +// In detail, with the default NHWC format, +// +// output[b, i, j, k] = +// sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * +// filter[di, dj, q, k] +// +// Must have `strides[0] = strides[3] = 1`. For the most common case of the same +// horizontal and vertices strides, `strides = [1, stride, stride, 1]`. +// +// Arguments: +// input: A 4-D tensor. The dimension order is interpreted according to the value +// of `data_format`, see below for details. +// filter: A 4-D tensor of shape +// `[filter_height, filter_width, in_channels, out_channels]` +// strides: 1-D tensor of length 4. The stride of the sliding window for each +// dimension of `input`. The dimension order is determined by the value of +// `data_format`, see below for details. +// padding: The type of padding algorithm to use. +// +// Returns A 4-D tensor. The dimension order is determined by the value of +// `data_format`, see below for details. +func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv2D", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns locations of nonzero / true values in a tensor. +// +// This operation returns the coordinates of true elements in `condition`. The +// coordinates are returned in a 2-D tensor where the first dimension (rows) +// represents the number of true elements, and the second dimension (columns) +// represents the coordinates of the true elements. Keep in mind, the shape of +// the output tensor can vary depending on how many true values there are in +// `condition`. Indices are output in row-major order. +// +// For example: +// +// ``` +// # 'input' tensor is [[True, False] +// # [True, False]] +// # 'input' has two true values, so output has two coordinates. +// # 'input' has rank of 2, so coordinates have two indices. +// where(input) ==> [[0, 0], +// [1, 0]] +// +// # `condition` tensor is [[[True, False] +// # [True, False]] +// # [[False, True] +// # [False, True]] +// # [[False, False] +// # [False, True]]] +// # 'input' has 5 true values, so output has 5 coordinates. +// # 'input' has rank of 3, so coordinates have three indices. +// where(input) ==> [[0, 0, 0], +// [0, 1, 0], +// [1, 0, 1], +// [1, 1, 1], +// [2, 1, 1]] +// +// # `condition` tensor is [[[1.5, 0.0] +// # [-0.5, 0.0]] +// # [[0.0, 0.25] +// # [0.0, 0.75]] +// # [[0.0, 0.0] +// # [0.0, 0.01]]] +// # 'input' has 5 nonzero values, so output has 5 coordinates. +// # 'input' has rank of 3, so coordinates have three indices. +// where(input) ==> [[0, 0, 0], +// [0, 1, 0], +// [1, 0, 1], +// [1, 1, 1], +// [2, 1, 1]] +// +// # `condition` tensor is [[[1.5 + 0.0j, 0.0 + 0.0j] +// # [0.0 + 0.5j, 0.0 + 0.0j]] +// # [[0.0 + 0.0j, 0.25 + 1.5j] +// # [0.0 + 0.0j, 0.75 + 0.0j]] +// # [[0.0 + 0.0j, 0.0 + 0.0j] +// # [0.0 + 0.0j, 0.01 + 0.0j]]] +// # 'input' has 5 nonzero magnitude values, so output has 5 coordinates. +// # 'input' has rank of 3, so coordinates have three indices. +// where(input) ==> [[0, 0, 0], +// [0, 1, 0], +// [1, 0, 1], +// [1, 1, 1], +// [2, 1, 1]] +// ``` +func Where(scope *Scope, condition tf.Output) (index tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Where", + Input: []tf.Input{ + condition, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated, use python implementation tf.linalg.matrix_exponential. +// +// DEPRECATED at GraphDef version 27: Use Python implementation tf.linalg.matrix_exponential instead. +func MatrixExponential(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixExponential", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reduces `input` from `num_devices` using `reduction` to a single device. +// +// Reduces `input` from `num_devices` using `reduction` to a single device. +// +// The graph should be constructed so that all inputs have a valid device +// assignment, and the op itself is assigned one of these devices. +// +// input: The input to the reduction. +// data: the value of the reduction across all `num_devices` devices. +// reduction: the reduction operation to perform. +func NcclReduce(scope *Scope, input []tf.Output, reduction string) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"reduction": reduction} + opspec := tf.OpSpec{ + Type: "NcclReduce", + Input: []tf.Input{ + tf.OutputList(input), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr is an optional argument to QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize. +type QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QUINT8 +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value. +// +// value: List of dilation values. +// If not specified, defaults to +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizePaddingList sets the optional padding_list attribute to value. +// If not specified, defaults to <> +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizePaddingList(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["padding_list"] = value + } +} + +// Computes quantized depthwise Conv2D with Bias, Relu and Requantize. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// bias: The original bias tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// min_freezed_output: The minimum float value of the output tensor. +// max_freezed_output: The maximum float value of the output tensor. +// strides: List of stride values. +// +// +// Returns: +// output: The output tensor. +// min_output: The float value that the minimum quantized output value represents. +// max_output: The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, min_freezed_output tf.Output, max_freezed_output tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize", + Input: []tf.Input{ + input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QuantizedDepthwiseConv2DWithBiasAndReluAttr is an optional argument to QuantizedDepthwiseConv2DWithBiasAndRelu. +type QuantizedDepthwiseConv2DWithBiasAndReluAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DWithBiasAndReluOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QINT32 +func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAndReluAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value. +// +// value: List of dilation values. +// If not specified, defaults to +func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// QuantizedDepthwiseConv2DWithBiasAndReluPaddingList sets the optional padding_list attribute to value. +// If not specified, defaults to <> +func QuantizedDepthwiseConv2DWithBiasAndReluPaddingList(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr { + return func(m optionalAttr) { + m["padding_list"] = value + } +} + +// Computes quantized depthwise Conv2D with Bias and Relu. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// bias: The original bias tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// strides: List of stride values. +// +// +// Returns: +// output: The output tensor. +// min_output: The float value that the minimum quantized output value represents. +// max_output: The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2DWithBiasAndRelu(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAndReluAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedDepthwiseConv2DWithBiasAndRelu", + Input: []tf.Input{ + input, filter, bias, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints. +type MergeV2CheckpointsAttr func(optionalAttr) + +// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value. +// +// value: see above. +// If not specified, defaults to true +func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr { + return func(m optionalAttr) { + m["delete_old_dirs"] = value + } +} + +// V2 format specific: merges the metadata files of sharded checkpoints. The +// +// result is one logical checkpoint, with one physical metadata file and renamed +// data files. +// +// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup. +// +// If delete_old_dirs is true, attempts to delete recursively the dirname of each +// path in the input checkpoint_prefixes. This is useful when those paths are non +// user-facing temporary locations. +// +// Arguments: +// checkpoint_prefixes: prefixes of V2 checkpoints to merge. +// destination_prefix: scalar. The desired final prefix. Allowed to be the same +// as one of the checkpoint_prefixes. +// +// Returns the created operation. +func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MergeV2Checkpoints", + Input: []tf.Input{ + checkpoint_prefixes, destination_prefix, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QuantizedDepthwiseConv2DWithBiasAttr is an optional argument to QuantizedDepthwiseConv2DWithBias. +type QuantizedDepthwiseConv2DWithBiasAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DWithBiasOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QINT32 +func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwiseConv2DWithBiasAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value. +// +// value: List of dilation values. +// If not specified, defaults to +func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes quantized depthwise Conv2D with Bias. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// bias: The original bias tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// strides: List of stride values. +// +// +// Returns: +// output: The output tensor. +// min_output: The float value that the minimum quantized output value represents. +// max_output: The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2DWithBias(scope *Scope, input tf.Output, filter tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DWithBiasAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedDepthwiseConv2DWithBias", + Input: []tf.Input{ + input, filter, bias, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QuantizedDepthwiseConv2DAttr is an optional argument to QuantizedDepthwiseConv2D. +type QuantizedDepthwiseConv2DAttr func(optionalAttr) + +// QuantizedDepthwiseConv2DOutType sets the optional out_type attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_QINT32 +func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2DAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value. +// +// value: List of dilation values. +// If not specified, defaults to +func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes quantized depthwise Conv2D. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// min_input: The float value that the minimum quantized input value represents. +// max_input: The float value that the maximum quantized input value represents. +// min_filter: The float value that the minimum quantized filter value represents. +// max_filter: The float value that the maximum quantized filter value represents. +// strides: List of stride values. +// +// +// Returns: +// output: The output tensor. +// min_output: The float value that the minimum quantized output value represents. +// max_output: The float value that the maximum quantized output value represents. +func QuantizedDepthwiseConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedDepthwiseConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedDepthwiseConv2D", + Input: []tf.Input{ + input, filter, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QuantizedConv2DPerChannelAttr is an optional argument to QuantizedConv2DPerChannel. +type QuantizedConv2DPerChannelAttr func(optionalAttr) + +// QuantizedConv2DPerChannelOutType sets the optional out_type attribute to value. +// +// value: The quantized type of output tensor that needs to be converted. +// If not specified, defaults to DT_QINT32 +func QuantizedConv2DPerChannelOutType(value tf.DataType) QuantizedConv2DPerChannelAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value. +// +// value: list of dilation values. +// If not specified, defaults to +func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes QuantizedConv2D per channel. +// +// Arguments: +// input: The original input tensor. +// filter: The original filter tensor. +// min_input: The minimum value of the input tensor +// max_input: The maximum value of the input tensor. +// min_filter: The minimum value of the filter tensor. +// max_filter: The maximum value of the filter tensor. +// strides: list of stride values. +// +// +// Returns: +// output: The output tensor. +// min_output: The minimum value of the final output tensor. +// max_output: The maximum value of the final output tensor. +func QuantizedConv2DPerChannel(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DPerChannelAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedConv2DPerChannel", + Input: []tf.Input{ + input, filter, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Concatenates quantized tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// input_mins: The minimum scalar values for each of the input tensors. +// input_maxes: The maximum scalar values for each of the input tensors. +// +// Returns: +// output: A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +// output_min: The float value that the minimum quantized output value represents. +// output_max: The float value that the maximum quantized output value represents. +func QuantizedConcat(scope *Scope, concat_dim tf.Output, values []tf.Output, input_mins []tf.Output, input_maxes []tf.Output) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QuantizedConcat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), tf.OutputList(input_mins), tf.OutputList(input_maxes), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the batched diagonal part of a batched tensor. +// +// Returns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched +// `input`. +// +// Assume `input` has `r` dimensions `[I, J, ..., L, M, N]`. +// Let `max_diag_len` be the maximum length among all diagonals to be extracted, +// `max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))` +// Let `num_diags` be the number of diagonals to extract, +// `num_diags = k[1] - k[0] + 1`. +// +// If `num_diags == 1`, the output tensor is of rank `r - 1` with shape +// `[I, J, ..., L, max_diag_len]` and values: +// +// ``` +// diagonal[i, j, ..., l, n] +// = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, +// padding_value ; otherwise. +// ``` +// where `y = max(-k[1], 0)`, `x = max(k[1], 0)`. +// +// Otherwise, the output tensor has rank `r` with dimensions +// `[I, J, ..., L, num_diags, max_diag_len]` with values: +// +// ``` +// diagonal[i, j, ..., l, m, n] +// = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, +// padding_value ; otherwise. +// ``` +// where `d = k[1] - m`, `y = max(-d, 0)`, and `x = max(d, 0)`. +// +// The input must be at least a matrix. +// +// For example: +// +// ``` +// input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4) +// [5, 6, 7, 8], +// [9, 8, 7, 6]], +// [[5, 4, 3, 2], +// [1, 2, 3, 4], +// [5, 6, 7, 8]]]) +// +// # A main diagonal from each batch. +// tf.matrix_diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3) +// [5, 2, 7]] +// +// # A superdiagonal from each batch. +// tf.matrix_diag_part(input, k = 1) +// ==> [[2, 7, 6], # Output shape: (2, 3) +// [4, 3, 8]] +// +// # A tridiagonal band from each batch. +// tf.matrix_diag_part(input, k = (-1, 1)) +// ==> [[[2, 7, 6], # Output shape: (2, 3, 3) +// [1, 6, 7], +// [5, 8, 0]], +// [[4, 3, 8], +// [5, 2, 7], +// [1, 6, 0]]] +// +// # Padding value = 9 +// tf.matrix_diag_part(input, k = (1, 3), padding_value = 9) +// ==> [[[4, 9, 9], # Output shape: (2, 3, 3) +// [3, 8, 9], +// [2, 7, 6]], +// [[2, 9, 9], +// [3, 4, 9], +// [4, 3, 8]]] +// ``` +// +// Arguments: +// input: Rank `r` tensor where `r >= 2`. +// k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main +// diagonal, and negative value means subdiagonals. `k` can be a single integer +// (for a single diagonal) or a pair of integers specifying the low and high ends +// of a matrix band. `k[0]` must not be larger than `k[1]`. +// padding_value: The value to fill the area outside the specified diagonal band with. +// Default is 0. +// +// Returns The extracted diagonal(s). +func MatrixDiagPartV2(scope *Scope, input tf.Output, k tf.Output, padding_value tf.Output) (diagonal tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDiagPartV2", + Input: []tf.Input{ + input, k, padding_value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A container for a multi device iterator resource. +// +// Returns: +// handle: A handle to a multi device iterator that can be passed to a +// "MultiDeviceIteratorGetNextFromShard" op. In contrast to MultiDeviceIterator, +// AnonymousIterator prevents resource sharing by name, and does not keep a +// reference to the resource container. +// deleter: A variant deleter that should be passed into the op that deletes the iterator. +func AnonymousMultiDeviceIterator(scope *Scope, devices []string, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output, deleter tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"devices": devices, "output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "AnonymousMultiDeviceIterator", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Provides the time since epoch in seconds. +// +// Returns the timestamp as a `float64` for seconds since the Unix epoch. +// +// Note: the timestamp is computed when the op is executed, not when it is added +// to the graph. +func Timestamp(scope *Scope) (ts tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Timestamp", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x <= y) element-wise. +// +// *NOTE*: `LessEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// Example: +// +// ```python +// x = tf.constant([5, 4, 6]) +// y = tf.constant([5]) +// tf.math.less_equal(x, y) ==> [True, True, False] +// +// x = tf.constant([5, 4, 6]) +// y = tf.constant([5, 6, 6]) +// tf.math.less_equal(x, y) ==> [True, True, True] +// ``` +func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LessEqual", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingADAMParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingADAMParametersGradAccumDebug. +type LoadTPUEmbeddingADAMParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingADAMParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingADAMParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingADAMParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingADAMParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingADAMParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingADAMParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load ADAM embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the ADAM optimization algorithm. +// momenta: Value of momenta used in the ADAM optimization algorithm. +// velocities: Value of velocities used in the ADAM optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the ADAM optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingADAMParametersGradAccumDebug(scope *Scope, parameters tf.Output, momenta tf.Output, velocities tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingADAMParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingADAMParametersGradAccumDebug", + Input: []tf.Input{ + parameters, momenta, velocities, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// RetrieveTPUEmbeddingRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingRMSPropParameters. +type RetrieveTPUEmbeddingRMSPropParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingRMSPropParametersTableName(value string) RetrieveTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingRMSPropParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingRMSPropParametersConfig(value string) RetrieveTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve RMSProp embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the RMSProp optimization algorithm. +// ms: Parameter ms updated by the RMSProp optimization algorithm. +// mom: Parameter mom updated by the RMSProp optimization algorithm. +func RetrieveTPUEmbeddingRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingRMSPropParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// QuantizedMatMulWithBiasAttr is an optional argument to QuantizedMatMulWithBias. +type QuantizedMatMulWithBiasAttr func(optionalAttr) + +// QuantizedMatMulWithBiasToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedMatMulWithBiasToutput(value tf.DataType) QuantizedMatMulWithBiasAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// QuantizedMatMulWithBiasTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, `a` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasTransposeA(value bool) QuantizedMatMulWithBiasAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// QuantizedMatMulWithBiasTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, `b` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasTransposeB(value bool) QuantizedMatMulWithBiasAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// QuantizedMatMulWithBiasInputQuantMode sets the optional input_quant_mode attribute to value. +// +// value: Input data quantization mode. Either MIN_FIRST(default) or SCALED. +// If not specified, defaults to "MIN_FIRST" +func QuantizedMatMulWithBiasInputQuantMode(value string) QuantizedMatMulWithBiasAttr { + return func(m optionalAttr) { + m["input_quant_mode"] = value + } +} + +// Performs a quantized matrix multiplication of `a` by the matrix `b` with bias +// add. +// +// The inputs must be two-dimensional matrices and 1D bias vector. And the inner +// dimension of `a` (after being transposed if `transpose_a` is non-zero) must +// match the outer dimension of `b` (after being transposed if `transposed_b` is +// non-zero). Then do broadcast add operation with bias values on the matrix +// multiplication result. The bias size must match inner dimension of `b`. +// +// Arguments: +// a: A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. +// b: A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. +// bias: A 1D bias tensor with size matching inner dimension of `b` (after being +// transposed if `transposed_b` is non-zero). +// min_a: The float value that the lowest quantized `a` value represents. +// max_a: The float value that the highest quantized `a` value represents. +// min_b: The float value that the lowest quantized `b` value represents. +// max_b: The float value that the highest quantized `b` value represents. +// +// Returns: +// out +// min_out: The float value that the lowest quantized output value represents. +// max_out: The float value that the highest quantized output value represents. +func QuantizedMatMulWithBias(scope *Scope, a tf.Output, b tf.Output, bias tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulWithBiasAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMatMulWithBias", + Input: []tf.Input{ + a, b, bias, min_a, max_a, min_b, max_b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// TensorArrayGatherV2Attr is an optional argument to TensorArrayGatherV2. +type TensorArrayGatherV2Attr func(optionalAttr) + +// TensorArrayGatherV2ElementShape sets the optional element_shape attribute to value. +// If not specified, defaults to +func TensorArrayGatherV2ElementShape(value tf.Shape) TensorArrayGatherV2Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// Deprecated. Use TensorArrayGatherV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayGatherV3 +func TensorArrayGatherV2(scope *Scope, handle tf.Output, indices tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayGatherV2Attr) (value tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayGatherV2", + Input: []tf.Input{ + handle, indices, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RFFT3DAttr is an optional argument to RFFT3D. +type RFFT3DAttr func(optionalAttr) + +// RFFT3DTcomplex sets the optional Tcomplex attribute to value. +// If not specified, defaults to DT_COMPLEX64 +func RFFT3DTcomplex(value tf.DataType) RFFT3DAttr { + return func(m optionalAttr) { + m["Tcomplex"] = value + } +} + +// 3D real-valued fast Fourier transform. +// +// Computes the 3-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most 3 dimensions of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT3D` only returns the +// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension +// of `output`: the zero-frequency term, followed by the `fft_length / 2` +// positive-frequency terms. +// +// Along each axis `RFFT3D` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. +// +// Returns A complex64 tensor of the same rank as `input`. The inner-most 3 +// dimensions of `input` are replaced with the their 3D Fourier transform. The +// inner-most dimension contains `fft_length / 2 + 1` unique frequency +// components. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfftn with 3 dimensions. +// @end_compatibility +func RFFT3D(scope *Scope, input tf.Output, fft_length tf.Output, optional ...RFFT3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RFFT3D", + Input: []tf.Input{ + input, fft_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reorders a SparseTensor into the canonical, row-major ordering. +// +// Note that by convention, all sparse ops preserve the canonical ordering along +// increasing dimension number. The only time ordering can be violated is during +// manual manipulation of the indices and values vectors to add entries. +// +// Reordering does not affect the shape of the SparseTensor. +// +// If the tensor has rank `R` and `N` non-empty values, `input_indices` has +// shape `[N, R]`, input_values has length `N`, and input_shape has length `R`. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// +// Returns: +// output_indices: 2-D. `N x R` matrix with the same indices as input_indices, but +// in canonical row-major ordering. +// output_values: 1-D. `N` non-empty values corresponding to `output_indices`. +func SparseReorder(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseReorder", + Input: []tf.Input{ + input_indices, input_values, input_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Generates fingerprint values. +// +// Generates fingerprint values of `data`. +// +// Fingerprint op considers the first dimension of `data` as the batch dimension, +// and `output[i]` contains the fingerprint value generated from contents in +// `data[i, ...]` for all `i`. +// +// Fingerprint op writes fingerprint values as byte arrays. For example, the +// default method `farmhash64` generates a 64-bit fingerprint value at a time. +// This 8-byte value is written out as an `uint8` array of size 8, in little-endian +// order. +// +// For example, suppose that `data` has data type `DT_INT32` and shape (2, 3, 4), +// and that the fingerprint method is `farmhash64`. In this case, the output shape +// is (2, 8), where 2 is the batch dimension size of `data`, and 8 is the size of +// each fingerprint value in bytes. `output[0, :]` is generated from 12 integers in +// `data[0, :, :]` and similarly `output[1, :]` is generated from other 12 integers +// in `data[1, :, :]`. +// +// Note that this op fingerprints the raw underlying buffer, and it does not +// fingerprint Tensor's metadata such as data type and/or shape. For example, the +// fingerprint values are invariant under reshapes and bitcasts as long as the +// batch dimension remain the same: +// +// ``` +// Fingerprint(data) == Fingerprint(Reshape(data, ...)) +// Fingerprint(data) == Fingerprint(Bitcast(data, ...)) +// ``` +// +// For string data, one should expect `Fingerprint(data) != +// Fingerprint(ReduceJoin(data))` in general. +// +// Arguments: +// data: Must have rank 1 or higher. +// method: Fingerprint method used by this op. Currently available method is +// `farmhash::fingerprint64`. +// +// Returns A two-dimensional `Tensor` of type `tf.uint8`. The first dimension equals to +// `data`'s first dimension, and the second dimension size depends on the +// fingerprint algorithm. +func Fingerprint(scope *Scope, data tf.Output, method tf.Output) (fingerprint tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Fingerprint", + Input: []tf.Input{ + data, method, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CopyAttr is an optional argument to Copy. +type CopyAttr func(optionalAttr) + +// CopyTensorName sets the optional tensor_name attribute to value. +// +// value: The name of the input tensor. +// If not specified, defaults to "" +func CopyTensorName(value string) CopyAttr { + return func(m optionalAttr) { + m["tensor_name"] = value + } +} + +// CopyDebugOpsSpec sets the optional debug_ops_spec attribute to value. +// +// value: A list of debug op spec (op, url, gated_grpc) for attached debug +// ops. Each element of the list has the format +// ;;, wherein gated_grpc is boolean represented +// as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1", +// "DebugIdentity;file:///tmp/tfdbg_1;0". +// If not specified, defaults to <> +func CopyDebugOpsSpec(value []string) CopyAttr { + return func(m optionalAttr) { + m["debug_ops_spec"] = value + } +} + +// Copy a tensor from CPU-to-CPU or GPU-to-GPU. +// +// Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the +// device on which the tensor is allocated. +// N.B.: If the all downstream attached debug ops are disabled given the current +// gRPC gating status, the output will simply forward the input tensor without +// deep-copying. See the documentation of Debug* ops for more details. +// +// Unlike the CopyHost Op, this op does not have HostMemory constraint on its +// input or output. +// +// Arguments: +// input: Input tensor. +func Copy(scope *Scope, input tf.Output, optional ...CopyAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Copy", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Updates specified rows 'i' with values 'v'. +// +// Computes `x[i, :] = v; return x`. +// +// Originally this function is mutative however for compilation we make this +// operation create / operate on a copy of `x`. +// +// Arguments: +// x: A tensor of type `T`. +// i: A vector. Indices into the left-most dimension of `x`. +// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// +// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. +func InplaceUpdate(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InplaceUpdate", + Input: []tf.Input{ + x, i, v, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Table initializer that takes two tensors for keys and values respectively. +// +// Arguments: +// table_handle: Handle to a table which will be initialized. +// keys: Keys of type Tkey. +// values: Values of type Tval. +// +// Returns the created operation. +func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InitializeTableV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} + +// BatchToSpace for N-D tensors of type T. +// +// This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape +// `block_shape + [batch]`, interleaves these blocks back into the grid defined by +// the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as +// the input. The spatial dimensions of this intermediate result are then +// optionally cropped according to `crops` to produce the output. This is the +// reverse of SpaceToBatch. See below for a precise description. +// +// Arguments: +// input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, +// where spatial_shape has M dimensions. +// block_shape: 1-D with shape `[M]`, all values must be >= 1. +// crops: 2-D with shape `[M, 2]`, all values must be >= 0. +// `crops[i] = [crop_start, crop_end]` specifies the amount to crop from input +// dimension `i + 1`, which corresponds to spatial dimension `i`. It is +// required that +// `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`. +// +// This operation is equivalent to the following steps: +// +// 1. Reshape `input` to `reshaped` of shape: +// [block_shape[0], ..., block_shape[M-1], +// batch / prod(block_shape), +// input_shape[1], ..., input_shape[N-1]] +// +// 2. Permute dimensions of `reshaped` to produce `permuted` of shape +// [batch / prod(block_shape), +// +// input_shape[1], block_shape[0], +// ..., +// input_shape[M], block_shape[M-1], +// +// input_shape[M+1], ..., input_shape[N-1]] +// +// 3. Reshape `permuted` to produce `reshaped_permuted` of shape +// [batch / prod(block_shape), +// +// input_shape[1] * block_shape[0], +// ..., +// input_shape[M] * block_shape[M-1], +// +// input_shape[M+1], +// ..., +// input_shape[N-1]] +// +// 4. Crop the start and end of dimensions `[1, ..., M]` of +// `reshaped_permuted` according to `crops` to produce the output of shape: +// [batch / prod(block_shape), +// +// input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1], +// ..., +// input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1], +// +// input_shape[M+1], ..., input_shape[N-1]] +// +// Some examples: +// +// (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and +// `crops = [[0, 0], [0, 0]]`: +// +// ``` +// [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] +// ``` +// +// The output tensor has shape `[1, 2, 2, 1]` and value: +// +// ``` +// x = [[[[1], [2]], [[3], [4]]]] +// ``` +// +// (2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and +// `crops = [[0, 0], [0, 0]]`: +// +// ``` +// [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] +// ``` +// +// The output tensor has shape `[1, 2, 2, 3]` and value: +// +// ``` +// x = [[[[1, 2, 3], [4, 5, 6]], +// [[7, 8, 9], [10, 11, 12]]]] +// ``` +// +// (3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and +// `crops = [[0, 0], [0, 0]]`: +// +// ``` +// x = [[[[1], [3]], [[9], [11]]], +// [[[2], [4]], [[10], [12]]], +// [[[5], [7]], [[13], [15]]], +// [[[6], [8]], [[14], [16]]]] +// ``` +// +// The output tensor has shape `[1, 4, 4, 1]` and value: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]], +// [[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +// +// (4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and +// `crops = [[0, 0], [2, 0]]`: +// +// ``` +// x = [[[[0], [1], [3]]], [[[0], [9], [11]]], +// [[[0], [2], [4]]], [[[0], [10], [12]]], +// [[[0], [5], [7]]], [[[0], [13], [15]]], +// [[[0], [6], [8]]], [[[0], [14], [16]]]] +// ``` +// +// The output tensor has shape `[2, 2, 4, 1]` and value: +// +// ``` +// x = [[[[1], [2], [3], [4]], +// [[5], [6], [7], [8]]], +// [[[9], [10], [11], [12]], +// [[13], [14], [15], [16]]]] +// ``` +func BatchToSpaceND(scope *Scope, input tf.Output, block_shape tf.Output, crops tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BatchToSpaceND", + Input: []tf.Input{ + input, block_shape, crops, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FIFOQueueV2Attr is an optional argument to FIFOQueueV2. +type FIFOQueueV2Attr func(optionalAttr) + +// FIFOQueueV2Shapes sets the optional shapes attribute to value. +// +// value: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. If the length of +// this attr is 0, the shapes of queue elements are not constrained, and +// only one element may be dequeued at a time. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func FIFOQueueV2Shapes(value []tf.Shape) FIFOQueueV2Attr { + return func(m optionalAttr) { + m["shapes"] = value + } +} + +// FIFOQueueV2Capacity sets the optional capacity attribute to value. +// +// value: The upper bound on the number of elements in this queue. +// Negative numbers mean no limit. +// If not specified, defaults to -1 +func FIFOQueueV2Capacity(value int64) FIFOQueueV2Attr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// FIFOQueueV2Container sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func FIFOQueueV2Container(value string) FIFOQueueV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// FIFOQueueV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this queue will be shared under the given name +// across multiple sessions. +// If not specified, defaults to "" +func FIFOQueueV2SharedName(value string) FIFOQueueV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A queue that produces elements in first-in first-out order. +// +// Arguments: +// component_types: The type of each component in a value. +// +// Returns The handle to the queue. +func FIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...FIFOQueueV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FIFOQueueV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Quantized Batch normalization. +// +// This op is deprecated and will be removed in the future. Prefer +// `tf.nn.batch_normalization`. +// +// Arguments: +// t: A 4D input Tensor. +// t_min: The value represented by the lowest quantized input. +// t_max: The value represented by the highest quantized input. +// m: A 1D mean Tensor with size matching the last dimension of t. +// This is the first output from tf.nn.moments, +// or a saved moving average thereof. +// m_min: The value represented by the lowest quantized mean. +// m_max: The value represented by the highest quantized mean. +// v: A 1D variance Tensor with size matching the last dimension of t. +// This is the second output from tf.nn.moments, +// or a saved moving average thereof. +// v_min: The value represented by the lowest quantized variance. +// v_max: The value represented by the highest quantized variance. +// beta: A 1D beta Tensor with size matching the last dimension of t. +// An offset to be added to the normalized tensor. +// beta_min: The value represented by the lowest quantized offset. +// beta_max: The value represented by the highest quantized offset. +// gamma: A 1D gamma Tensor with size matching the last dimension of t. +// If "scale_after_normalization" is true, this tensor will be multiplied +// with the normalized tensor. +// gamma_min: The value represented by the lowest quantized gamma. +// gamma_max: The value represented by the highest quantized gamma. +// +// variance_epsilon: A small float number to avoid dividing by 0. +// scale_after_normalization: A bool indicating whether the resulted tensor +// needs to be multiplied with gamma. +func QuantizedBatchNormWithGlobalNormalization(scope *Scope, t tf.Output, t_min tf.Output, t_max tf.Output, m tf.Output, m_min tf.Output, m_max tf.Output, v tf.Output, v_min tf.Output, v_max tf.Output, beta tf.Output, beta_min tf.Output, beta_max tf.Output, gamma tf.Output, gamma_min tf.Output, gamma_max tf.Output, out_type tf.DataType, variance_epsilon float32, scale_after_normalization bool) (result tf.Output, result_min tf.Output, result_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type, "variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} + opspec := tf.OpSpec{ + Type: "QuantizedBatchNormWithGlobalNormalization", + Input: []tf.Input{ + t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResourceStridedSliceAssignAttr is an optional argument to ResourceStridedSliceAssign. +type ResourceStridedSliceAssignAttr func(optionalAttr) + +// ResourceStridedSliceAssignBeginMask sets the optional begin_mask attribute to value. +// If not specified, defaults to 0 +func ResourceStridedSliceAssignBeginMask(value int64) ResourceStridedSliceAssignAttr { + return func(m optionalAttr) { + m["begin_mask"] = value + } +} + +// ResourceStridedSliceAssignEndMask sets the optional end_mask attribute to value. +// If not specified, defaults to 0 +func ResourceStridedSliceAssignEndMask(value int64) ResourceStridedSliceAssignAttr { + return func(m optionalAttr) { + m["end_mask"] = value + } +} + +// ResourceStridedSliceAssignEllipsisMask sets the optional ellipsis_mask attribute to value. +// If not specified, defaults to 0 +func ResourceStridedSliceAssignEllipsisMask(value int64) ResourceStridedSliceAssignAttr { + return func(m optionalAttr) { + m["ellipsis_mask"] = value + } +} + +// ResourceStridedSliceAssignNewAxisMask sets the optional new_axis_mask attribute to value. +// If not specified, defaults to 0 +func ResourceStridedSliceAssignNewAxisMask(value int64) ResourceStridedSliceAssignAttr { + return func(m optionalAttr) { + m["new_axis_mask"] = value + } +} + +// ResourceStridedSliceAssignShrinkAxisMask sets the optional shrink_axis_mask attribute to value. +// If not specified, defaults to 0 +func ResourceStridedSliceAssignShrinkAxisMask(value int64) ResourceStridedSliceAssignAttr { + return func(m optionalAttr) { + m["shrink_axis_mask"] = value + } +} + +// Assign `value` to the sliced l-value reference of `ref`. +// +// The values of `value` are assigned to the positions in the variable +// `ref` that are selected by the slice parameters. The slice parameters +// `begin, `end`, `strides`, etc. work exactly as in `StridedSlice`. +// +// NOTE this op currently does not support broadcasting and so `value`'s +// shape must be exactly the shape produced by the slice of `ref`. +// +// Returns the created operation. +func ResourceStridedSliceAssign(scope *Scope, ref tf.Output, begin tf.Output, end tf.Output, strides tf.Output, value tf.Output, optional ...ResourceStridedSliceAssignAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceStridedSliceAssign", + Input: []tf.Input{ + ref, begin, end, strides, value, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QuantizedRelu6Attr is an optional argument to QuantizedRelu6. +type QuantizedRelu6Attr func(optionalAttr) + +// QuantizedRelu6OutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedRelu6OutType(value tf.DataType) QuantizedRelu6Attr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` +// +// Arguments: +// +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns: +// activations: Has the same output shape as "features". +// min_activations: The float value that the lowest quantized value represents. +// max_activations: The float value that the highest quantized value represents. +func QuantizedRelu6(scope *Scope, features tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedRelu6Attr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedRelu6", + Input: []tf.Input{ + features, min_features, max_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// DataFormatVecPermuteAttr is an optional argument to DataFormatVecPermute. +type DataFormatVecPermuteAttr func(optionalAttr) + +// DataFormatVecPermuteSrcFormat sets the optional src_format attribute to value. +// +// value: source data format. +// If not specified, defaults to "NHWC" +func DataFormatVecPermuteSrcFormat(value string) DataFormatVecPermuteAttr { + return func(m optionalAttr) { + m["src_format"] = value + } +} + +// DataFormatVecPermuteDstFormat sets the optional dst_format attribute to value. +// +// value: destination data format. +// If not specified, defaults to "NCHW" +func DataFormatVecPermuteDstFormat(value string) DataFormatVecPermuteAttr { + return func(m optionalAttr) { + m["dst_format"] = value + } +} + +// Returns the permuted vector/tensor in the destination data format given the +// +// one in the source data format. +// +// Arguments: +// x: Vector of size 4 or Tensor of shape (4, 2) in source data format. +// +// Returns Vector of size 4 or Tensor of shape (4, 2) in destination data format. +func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPermuteAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DataFormatVecPermute", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds Tensor 'bias' to Tensor 'input' for Quantized types. +// +// Broadcasts the values of bias on dimensions 0..N-2 of 'input'. +// +// Arguments: +// +// bias: A 1D bias Tensor with size matching the last dimension of 'input'. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// min_bias: The float value that the lowest quantized bias value represents. +// max_bias: The float value that the highest quantized bias value represents. +// +// +// Returns: +// output +// min_out: The float value that the lowest quantized output value represents. +// max_out: The float value that the highest quantized output value represents. +func QuantizedBiasAdd(scope *Scope, input tf.Output, bias tf.Output, min_input tf.Output, max_input tf.Output, min_bias tf.Output, max_bias tf.Output, out_type tf.DataType) (output tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "QuantizedBiasAdd", + Input: []tf.Input{ + input, bias, min_input, max_input, min_bias, max_bias, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// MutableDenseHashTableV2Attr is an optional argument to MutableDenseHashTableV2. +type MutableDenseHashTableV2Attr func(optionalAttr) + +// MutableDenseHashTableV2Container sets the optional container attribute to value. +// +// value: If non-empty, this table is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutableDenseHashTableV2Container(value string) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutableDenseHashTableV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this table is shared under the given name across +// multiple sessions. +// If not specified, defaults to "" +func MutableDenseHashTableV2SharedName(value string) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// MutableDenseHashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value. +// If not specified, defaults to false +func MutableDenseHashTableV2UseNodeNameSharing(value bool) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["use_node_name_sharing"] = value + } +} + +// MutableDenseHashTableV2ValueShape sets the optional value_shape attribute to value. +// +// value: The shape of each value. +// If not specified, defaults to <> +func MutableDenseHashTableV2ValueShape(value tf.Shape) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["value_shape"] = value + } +} + +// MutableDenseHashTableV2InitialNumBuckets sets the optional initial_num_buckets attribute to value. +// +// value: The initial number of hash table buckets. Must be a power +// to 2. +// If not specified, defaults to 131072 +func MutableDenseHashTableV2InitialNumBuckets(value int64) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["initial_num_buckets"] = value + } +} + +// MutableDenseHashTableV2MaxLoadFactor sets the optional max_load_factor attribute to value. +// +// value: The maximum ratio between number of entries and number of +// buckets before growing the table. Must be between 0 and 1. +// If not specified, defaults to 0.8 +func MutableDenseHashTableV2MaxLoadFactor(value float32) MutableDenseHashTableV2Attr { + return func(m optionalAttr) { + m["max_load_factor"] = value + } +} + +// Creates an empty hash table that uses tensors as the backing store. +// +// It uses "open addressing" with quadratic reprobing to resolve +// collisions. +// +// This op creates a mutable hash table, specifying the type of its keys and +// values. Each value must be a scalar. Data can be inserted into the table using +// the insert operations. It does not support the initialization operation. +// +// Arguments: +// empty_key: The key used to represent empty key buckets internally. Must not +// be used in insert or lookup operations. +// +// value_dtype: Type of the table values. +// +// Returns Handle to a table. +func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, deleted_key tf.Output, value_dtype tf.DataType, optional ...MutableDenseHashTableV2Attr) (table_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"value_dtype": value_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutableDenseHashTableV2", + Input: []tf.Input{ + empty_key, deleted_key, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FractionalAvgPoolGradAttr is an optional argument to FractionalAvgPoolGrad. +type FractionalAvgPoolGradAttr func(optionalAttr) + +// FractionalAvgPoolGradOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [41/3, 26/3] for fractional avg pooling. +// If not specified, defaults to false +func FractionalAvgPoolGradOverlapping(value bool) FractionalAvgPoolGradAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// Computes gradient of the FractionalAvgPool function. +// +// Unlike FractionalMaxPoolGrad, we don't need to find arg_max for +// FractionalAvgPoolGrad, we just need to evenly back-propagate each element of +// out_backprop to those indices that form the same pooling cell. Therefore, we +// just need to know the shape of original input tensor, instead of the whole +// tensor. +// +// Arguments: +// orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool` +// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients +// w.r.t. the output of `fractional_avg_pool`. +// row_pooling_sequence: row pooling sequence, form pooling region with +// col_pooling_sequence. +// col_pooling_sequence: column pooling sequence, form pooling region with +// row_pooling sequence. +// +// Returns 4-D. Gradients w.r.t. the input of `fractional_avg_pool`. +func FractionalAvgPoolGrad(scope *Scope, orig_input_tensor_shape tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalAvgPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalAvgPoolGrad", + Input: []tf.Input{ + orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FractionalMaxPoolGradAttr is an optional argument to FractionalMaxPoolGrad. +type FractionalMaxPoolGradAttr func(optionalAttr) + +// FractionalMaxPoolGradOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [20, 16] for fractional max pooling. +// If not specified, defaults to false +func FractionalMaxPoolGradOverlapping(value bool) FractionalMaxPoolGradAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// Computes gradient of the FractionalMaxPool function. +// +// Arguments: +// orig_input: Original input for `fractional_max_pool` +// orig_output: Original output for `fractional_max_pool` +// out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients +// w.r.t. the output of `fractional_max_pool`. +// row_pooling_sequence: row pooling sequence, form pooling region with +// col_pooling_sequence. +// col_pooling_sequence: column pooling sequence, form pooling region with +// row_pooling sequence. +// +// Returns 4-D. Gradients w.r.t. the input of `fractional_max_pool`. +func FractionalMaxPoolGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, out_backprop tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output, optional ...FractionalMaxPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalMaxPoolGrad", + Input: []tf.Input{ + orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NthElementAttr is an optional argument to NthElement. +type NthElementAttr func(optionalAttr) + +// NthElementReverse sets the optional reverse attribute to value. +// +// value: When set to True, find the nth-largest value in the vector and vice +// versa. +// If not specified, defaults to false +func NthElementReverse(value bool) NthElementAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Finds values of the `n`-th order statistic for the last dimension. +// +// If the input is a vector (rank-1), finds the entries which is the nth-smallest +// value in the vector and outputs their values as scalar tensor. +// +// For matrices (resp. higher rank input), computes the entries which is the +// nth-smallest value in each row (resp. vector along the last dimension). Thus, +// +// values.shape = input.shape[:-1] +// +// Arguments: +// input: 1-D or higher with last dimension at least `n+1`. +// n: 0-D. Position of sorted vector to select along the last dimension (along +// each row for matrices). Valid range of n is `[0, input.shape[:-1])` +// +// Returns The `n`-th order statistic along each last dimensional slice. +func NthElement(scope *Scope, input tf.Output, n tf.Output, optional ...NthElementAttr) (values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NthElement", + Input: []tf.Input{ + input, n, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Pads a tensor. +// +// This operation pads `input` according to the `paddings` and `constant_values` +// you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is +// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many padding values to add before the contents of `input` in that dimension, +// and `paddings[D, 1]` indicates how many padding values to add after the contents +// of `input` in that dimension. `constant_values` is a scalar tensor of the same +// type as `input` that indicates the value to use for padding `input`. +// +// The padded size of each dimension D of the output is: +// +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 1], [2, 2]] +// # 'paddings' is [[1, 1], [2, 2]] +// # 'constant_values' is 0 +// # rank of 't' is 2 +// pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] +// [0, 0, 1, 1, 0, 0] +// [0, 0, 2, 2, 0, 0] +// [0, 0, 0, 0, 0, 0]] +// ``` +func PadV2(scope *Scope, input tf.Output, paddings tf.Output, constant_values tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "PadV2", + Input: []tf.Input{ + input, paddings, constant_values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes cos of x element-wise. +// +// Given an input tensor, this function computes cosine of every +// element in the tensor. Input range is `(-inf, inf)` and +// output range is `[-1,1]`. If input lies outside the boundary, `nan` +// is returned. +// +// ```python +// x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")]) +// tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan] +// ``` +func Cos(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Cos", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TopKV2Attr is an optional argument to TopKV2. +type TopKV2Attr func(optionalAttr) + +// TopKV2Sorted sets the optional sorted attribute to value. +// +// value: If true the resulting `k` elements will be sorted by the values in +// descending order. +// If not specified, defaults to true +func TopKV2Sorted(value bool) TopKV2Attr { + return func(m optionalAttr) { + m["sorted"] = value + } +} + +// Finds values and indices of the `k` largest elements for the last dimension. +// +// If the input is a vector (rank-1), finds the `k` largest entries in the vector +// and outputs their values and indices as vectors. Thus `values[j]` is the +// `j`-th largest entry in `input`, and its index is `indices[j]`. +// +// For matrices (resp. higher rank input), computes the top `k` entries in each +// row (resp. vector along the last dimension). Thus, +// +// values.shape = indices.shape = input.shape[:-1] + [k] +// +// If two elements are equal, the lower-index element appears first. +// +// Arguments: +// input: 1-D or higher with last dimension at least `k`. +// k: 0-D. Number of top elements to look for along the last dimension (along each +// row for matrices). +// +// Returns: +// values: The `k` largest elements along each last dimensional slice. +// indices: The indices of `values` within the last dimension of `input`. +func TopKV2(scope *Scope, input tf.Output, k tf.Output, optional ...TopKV2Attr) (values tf.Output, indices tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TopKV2", + Input: []tf.Input{ + input, k, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// TopKAttr is an optional argument to TopK. +type TopKAttr func(optionalAttr) + +// TopKSorted sets the optional sorted attribute to value. +// +// value: If true the resulting `k` elements will be sorted by the values in +// descending order. +// If not specified, defaults to true +func TopKSorted(value bool) TopKAttr { + return func(m optionalAttr) { + m["sorted"] = value + } +} + +// Finds values and indices of the `k` largest elements for the last dimension. +// +// DEPRECATED at GraphDef version 7: Use TopKV2 instead +// +// If the input is a vector (rank-1), finds the `k` largest entries in the vector +// and outputs their values and indices as vectors. Thus `values[j]` is the +// `j`-th largest entry in `input`, and its index is `indices[j]`. +// +// For matrices (resp. higher rank input), computes the top `k` entries in each +// row (resp. vector along the last dimension). Thus, +// +// values.shape = indices.shape = input.shape[:-1] + [k] +// +// If two elements are equal, the lower-index element appears first. +// +// If `k` varies dynamically, use `TopKV2` below. +// +// Arguments: +// input: 1-D or higher with last dimension at least `k`. +// k: Number of top elements to look for along the last dimension (along each +// row for matrices). +// +// Returns: +// values: The `k` largest elements along each last dimensional slice. +// indices: The indices of `values` within the last dimension of `input`. +func TopK(scope *Scope, input tf.Output, k int64, optional ...TopKAttr) (values tf.Output, indices tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"k": k} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TopK", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Outputs the single element from the given dataset. +// +// Arguments: +// dataset: A handle to a dataset that contains a single element. +// +// +// +// Returns The components of the single element of `input`. +func DatasetToSingleElement(scope *Scope, dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "DatasetToSingleElement", + Input: []tf.Input{ + dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("DatasetToSingleElement", err) + return + } + return components +} + +// Computes softmax cross entropy cost and gradients to backpropagate. +// +// Inputs are the logits, not probabilities. +// +// Arguments: +// features: batch_size x num_classes matrix +// labels: batch_size x num_classes matrix +// The caller must ensure that each batch of labels represents a valid +// probability distribution. +// +// Returns: +// loss: Per example loss (batch_size vector). +// backprop: backpropagated gradients (batch_size x num_classes matrix). +func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SoftmaxCrossEntropyWithLogits", + Input: []tf.Input{ + features, labels, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes log softmax activations. +// +// For each batch `i` and class `j` we have +// +// logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i]))) +// +// Arguments: +// logits: 2-D with shape `[batch_size, num_classes]`. +// +// Returns Same shape as `logits`. +func LogSoftmax(scope *Scope, logits tf.Output) (logsoftmax tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogSoftmax", + Input: []tf.Input{ + logits, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softmax activations. +// +// For each batch `i` and class `j` we have +// +// $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ +// +// Arguments: +// logits: 2-D with shape `[batch_size, num_classes]`. +// +// Returns Same shape as `logits`. +func Softmax(scope *Scope, logits tf.Output) (softmax tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Softmax", + Input: []tf.Input{ + logits, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softsign gradients for a softsign operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding softsign operation. +// features: The features passed as input to the corresponding softsign operation. +// +// Returns The gradients: `gradients / (1 + abs(features)) ** 2`. +func SoftsignGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SoftsignGrad", + Input: []tf.Input{ + gradients, features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the LSTM cell backward propagation for 1 timestep. +// +// This implementation is to be used in conjunction of LSTMBlockCell. +// +// Arguments: +// x: The input to the LSTM cell, shape (batch_size, num_inputs). +// cs_prev: The previous cell state. +// h_prev: The previous h state. +// w: The weight matrix. +// wci: The weight matrix for input gate peephole connection. +// wcf: The weight matrix for forget gate peephole connection. +// wco: The weight matrix for output gate peephole connection. +// b: The bias vector. +// i: The input gate. +// cs: The cell state before the tanh. +// f: The forget gate. +// o: The output gate. +// ci: The cell input. +// co: The cell after the tanh. +// cs_grad: The current gradient of cs. +// h_grad: The gradient of h vector. +// use_peephole: Whether the cell uses peephole connections. +// +// Returns: +// cs_prev_grad: The gradient of cs to be back-propped. +// dicfo: The derivative wrt to [i, cs, f, o]. +// wci_grad: The gradient for wci to be back-propped. +// wcf_grad: The gradient for wcf to be back-propped. +// wco_grad: The gradient for wco to be back-propped. +func LSTMBlockCellGrad(scope *Scope, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, cs_grad tf.Output, h_grad tf.Output, use_peephole bool) (cs_prev_grad tf.Output, dicfo tf.Output, wci_grad tf.Output, wcf_grad tf.Output, wco_grad tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"use_peephole": use_peephole} + opspec := tf.OpSpec{ + Type: "LSTMBlockCellGrad", + Input: []tf.Input{ + x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Computes gradients for the scaled exponential linear (Selu) operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Selu operation. +// outputs: The outputs of the corresponding Selu operation. +// +// Returns The gradients: `gradients * (outputs + scale * alpha)` +// if outputs < 0, `scale * gradients` otherwise. +func SeluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SeluGrad", + Input: []tf.Input{ + gradients, outputs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes gradients for the exponential linear (Elu) operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Elu operation. +// outputs: The outputs of the corresponding Elu operation. +// +// Returns The gradients: `gradients * (outputs + 1)` if outputs < 0, +// `gradients` otherwise. +func EluGrad(scope *Scope, gradients tf.Output, outputs tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EluGrad", + Input: []tf.Input{ + gradients, outputs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LeakyReluGradAttr is an optional argument to LeakyReluGrad. +type LeakyReluGradAttr func(optionalAttr) + +// LeakyReluGradAlpha sets the optional alpha attribute to value. +// If not specified, defaults to 0.2 +func LeakyReluGradAlpha(value float32) LeakyReluGradAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// Computes rectified linear gradients for a LeakyRelu operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding LeakyRelu operation. +// features: The features passed as input to the corresponding LeakyRelu operation, +// OR the outputs of that operation (both work equivalently). +// +// Returns `gradients * (features > 0) + alpha * gradients * (features <= 0)`. +func LeakyReluGrad(scope *Scope, gradients tf.Output, features tf.Output, optional ...LeakyReluGradAttr) (backprops tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LeakyReluGrad", + Input: []tf.Input{ + gradients, features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the gradient of morphological 2-D dilation with respect to the filter. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, depth]`. +// filter: 3-D with shape `[filter_height, filter_width, depth]`. +// out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. +// strides: 1-D of length 4. The stride of the sliding window for each dimension of +// the input tensor. Must be: `[1, stride_height, stride_width, 1]`. +// rates: 1-D of length 4. The input stride for atrous morphological dilation. +// Must be: `[1, rate_height, rate_width, 1]`. +// padding: The type of padding algorithm to use. +// +// Returns 3-D with shape `[filter_height, filter_width, depth]`. +func Dilation2DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, rates []int64, padding string) (filter_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "Dilation2DBackpropFilter", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts the given variant tensor to an iterator and stores it in the given resource. +// +// Arguments: +// resource_handle: A handle to an iterator resource. +// serialized: A variant tensor storing the state of the iterator contained in the +// resource. +// +// Returns the created operation. +func DeserializeIterator(scope *Scope, resource_handle tf.Output, serialized tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeserializeIterator", + Input: []tf.Input{ + resource_handle, serialized, + }, + } + return scope.AddOperation(opspec) +} + +// Computes the gradient for the rsqrt of `x` wrt its input. +// +// Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy` +// is the corresponding input gradient. +func RsqrtGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RsqrtGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolWithArgmaxAttr is an optional argument to MaxPoolWithArgmax. +type MaxPoolWithArgmaxAttr func(optionalAttr) + +// MaxPoolWithArgmaxTargmax sets the optional Targmax attribute to value. +// If not specified, defaults to DT_INT64 +func MaxPoolWithArgmaxTargmax(value tf.DataType) MaxPoolWithArgmaxAttr { + return func(m optionalAttr) { + m["Targmax"] = value + } +} + +// MaxPoolWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. +// +// value: Whether to include batch dimension in flattened index of `argmax`. +// If not specified, defaults to false +func MaxPoolWithArgmaxIncludeBatchInIndex(value bool) MaxPoolWithArgmaxAttr { + return func(m optionalAttr) { + m["include_batch_in_index"] = value + } +} + +// Performs max pooling on the input and outputs both max values and indices. +// +// The indices in `argmax` are flattened, so that a maximum value at position +// `[b, y, x, c]` becomes flattened index: +// `(y * width + x) * channels + c` if `include_batch_in_index` is False; +// `((b * height + y) * width + x) * channels + c` if `include_batch_in_index` is True. +// +// The indices returned are always in `[0, height) x [0, width)` before flattening, +// even if padding is involved and the mathematically correct answer is outside +// (either negative or too large). This is a bug, but fixing it is difficult to do +// in a safe backwards compatible way, especially due to flattening. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns: +// output: The max pooled output tensor. +// argmax: 4-D. The flattened indices of the max values chosen for each output. +func MaxPoolWithArgmax(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolWithArgmaxAttr) (output tf.Output, argmax tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolWithArgmax", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad. +type MaxPoolGradGradAttr func(optionalAttr) + +// MaxPoolGradGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2. +type MaxPoolGradV2Attr func(optionalAttr) + +// MaxPoolGradV2DataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients w.r.t. the output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients w.r.t. the input to `max_pool`. +func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradV2", + Input: []tf.Input{ + orig_input, orig_output, grad, ksize, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concats all tensors in the list along the 0th dimension. +// +// Requires that all tensors have the same shape except the first dimension. +// +// input_handle: The input list. +// element_shape: The shape of the uninitialized elements in the list. If the first +// dimension is not -1, it is assumed that all list elements have the same +// leading dim. +// leading_dims: The list of leading dims of uninitialized list elements. Used if +// the leading dim of input_handle.element_shape or the element_shape input arg +// is not already set. +// tensor: The concated result. +// lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient. +// +func TensorListConcatV2(scope *Scope, input_handle tf.Output, element_shape tf.Output, leading_dims tf.Output, element_dtype tf.DataType) (tensor tf.Output, lengths tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "TensorListConcatV2", + Input: []tf.Input{ + input_handle, element_shape, leading_dims, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MaxPoolV2Attr is an optional argument to MaxPoolV2. +type MaxPoolV2Attr func(optionalAttr) + +// MaxPoolV2DataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolV2DataFormat(value string) MaxPoolV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs max pooling on the input. +// +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPoolV2(scope *Scope, input tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolV2", + Input: []tf.Input{ + input, ksize, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseReduceSumAttr is an optional argument to SparseReduceSum. +type SparseReduceSumAttr func(optionalAttr) + +// SparseReduceSumKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceSumKeepDims(value bool) SparseReduceSumAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the sum of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` +// instead of a sparse one. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +// +// Returns `R-K`-D. The reduced Tensor. +func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceSum", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the Hurwitz zeta function \\(\zeta(x, q)\\). +// +// The Hurwitz zeta function is defined as: +// +// +// \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\) +func Zeta(scope *Scope, x tf.Output, q tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Zeta", + Input: []tf.Input{ + x, q, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedMatMulWithBiasAndReluAndRequantizeAttr is an optional argument to QuantizedMatMulWithBiasAndReluAndRequantize. +type QuantizedMatMulWithBiasAndReluAndRequantizeAttr func(optionalAttr) + +// QuantizedMatMulWithBiasAndReluAndRequantizeToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedMatMulWithBiasAndReluAndRequantizeToutput(value tf.DataType) QuantizedMatMulWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// QuantizedMatMulWithBiasAndReluAndRequantizeTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, `a` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasAndReluAndRequantizeTransposeA(value bool) QuantizedMatMulWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// QuantizedMatMulWithBiasAndReluAndRequantizeTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, `b` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasAndReluAndRequantizeTransposeB(value bool) QuantizedMatMulWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// QuantizedMatMulWithBiasAndReluAndRequantizeInputQuantMode sets the optional input_quant_mode attribute to value. +// +// value: Input data quantization mode. Either MIN_FIRST(default) or SCALED. +// If not specified, defaults to "MIN_FIRST" +func QuantizedMatMulWithBiasAndReluAndRequantizeInputQuantMode(value string) QuantizedMatMulWithBiasAndReluAndRequantizeAttr { + return func(m optionalAttr) { + m["input_quant_mode"] = value + } +} + +// Perform a quantized matrix multiplication of `a` by the matrix `b` with bias +// add and relu and requantize fusion. +// +// The inputs must be two-dimensional matrices and 1D bias vector. And the inner +// dimension of `a` (after being transposed if `transpose_a` is non-zero) must +// match the outer dimension of `b` (after being transposed if `transposed_b` is +// non-zero). Then do broadcast add operation with bias values on the matrix +// multiplication result. The bias size must match inner dimension of `b`. Then do +// relu activation to get non-negative result. Then do requantize operation to get +// final uint8 result. +// +// Arguments: +// a: A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. +// b: A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. +// bias: A 1D bias tensor with size matching with inner dimension of `b` (after being +// transposed if `transposed_b` is non-zero). +// min_a: The float value that the lowest quantized `a` value represents. +// max_a: The float value that the highest quantized `a` value represents. +// min_b: The float value that the lowest quantized `b` value represents. +// max_b: The float value that the highest quantized `b` value represents. +// min_freezed_output: The float value that the highest quantized output value after requantize. +// +// +// Returns: +// out +// min_out: The float value that the lowest quantized output value represents. +// max_out: The float value that the highest quantized output value represents. +func QuantizedMatMulWithBiasAndReluAndRequantize(scope *Scope, a tf.Output, b tf.Output, bias tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, min_freezed_output tf.Output, max_freezed_output tf.Output, optional ...QuantizedMatMulWithBiasAndReluAndRequantizeAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMatMulWithBiasAndReluAndRequantize", + Input: []tf.Input{ + a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Conv3DBackpropInputV2Attr is an optional argument to Conv3DBackpropInputV2. +type Conv3DBackpropInputV2Attr func(optionalAttr) + +// Conv3DBackpropInputV2DataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv3DBackpropInputV2Dilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 5. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of 3-D convolution with respect to the input. +// +// Arguments: +// input_sizes: An integer vector representing the tensor shape of `input`, +// where `input` is a 5-D +// `[batch, depth, rows, cols, in_channels]` tensor. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropInputV2(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv3DBackpropInputV2", + Input: []tf.Input{ + input_sizes, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LRNAttr is an optional argument to LRN. +type LRNAttr func(optionalAttr) + +// LRNDepthRadius sets the optional depth_radius attribute to value. +// +// value: 0-D. Half-width of the 1-D normalization window. +// If not specified, defaults to 5 +func LRNDepthRadius(value int64) LRNAttr { + return func(m optionalAttr) { + m["depth_radius"] = value + } +} + +// LRNBias sets the optional bias attribute to value. +// +// value: An offset (usually positive to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNBias(value float32) LRNAttr { + return func(m optionalAttr) { + m["bias"] = value + } +} + +// LRNAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNAlpha(value float32) LRNAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// LRNBeta sets the optional beta attribute to value. +// +// value: An exponent. +// If not specified, defaults to 0.5 +func LRNBeta(value float32) LRNAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Local Response Normalization. +// +// The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last +// dimension), and each vector is normalized independently. Within a given vector, +// each component is divided by the weighted, squared sum of inputs within +// `depth_radius`. In detail, +// +// sqr_sum[a, b, c, d] = +// sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) +// output = input / (bias + alpha * sqr_sum) ** beta +// +// For details, see [Krizhevsky et al., ImageNet classification with deep +// convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). +// +// Arguments: +// input: 4-D. +func LRN(scope *Scope, input tf.Output, optional ...LRNAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LRN", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns which elements of x are Inf. +// +// @compatibility(numpy) +// Equivalent to np.isinf +// @end_compatibility +// +// Example: +// +// ```python +// x = tf.constant([5.0, np.inf, 6.8, np.inf]) +// tf.math.is_inf(x) ==> [False, True, False, True] +// ``` +func IsInf(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsInf", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPool3DAttr is an optional argument to MaxPool3D. +type MaxPool3DAttr func(optionalAttr) + +// MaxPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DDataFormat(value string) MaxPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D max pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayCloseV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayCloseV3 +// +// Returns the created operation. +func TensorArrayCloseV2(scope *Scope, handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayCloseV2", + Input: []tf.Input{ + handle, + }, + } + return scope.AddOperation(opspec) +} + +// AvgPool3DGradAttr is an optional argument to AvgPool3DGrad. +type AvgPool3DGradAttr func(optionalAttr) + +// AvgPool3DGradDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func AvgPool3DGradDataFormat(value string) AvgPool3DGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of average pooling function. +// +// Arguments: +// orig_input_shape: The original input dimensions. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The backprop for input. +func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPool3DGrad", + Input: []tf.Input{ + orig_input_shape, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv3DBackpropFilterAttr is an optional argument to Conv3DBackpropFilter. +type Conv3DBackpropFilterAttr func(optionalAttr) + +// Conv3DBackpropFilterDilations sets the optional dilations attribute to value. +// If not specified, defaults to +func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of 3-D convolution with respect to the filter. +// +// DEPRECATED at GraphDef version 10: Use Conv3DBackpropFilterV2 +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropFilter(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropFilterAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv3DBackpropFilter", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv3DAttr is an optional argument to Conv3D. +type Conv3DAttr func(optionalAttr) + +// Conv3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func Conv3DDataFormat(value string) Conv3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv3DDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 5. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func Conv3DDilations(value []int64) Conv3DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 3-D convolution given 5-D `input` and `filter` tensors. +// +// In signal processing, cross-correlation is a measure of similarity of +// two waveforms as a function of a time-lag applied to one of them. This +// is also known as a sliding dot product or sliding inner-product. +// +// Our Conv3D implements a form of cross-correlation. +// +// Arguments: +// input: Shape `[batch, in_depth, in_height, in_width, in_channels]`. +// filter: Shape `[filter_depth, filter_height, filter_width, in_channels, +// out_channels]`. `in_channels` must match between `input` and `filter`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv3D", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UniqueWithCountsAttr is an optional argument to UniqueWithCounts. +type UniqueWithCountsAttr func(optionalAttr) + +// UniqueWithCountsOutIdx sets the optional out_idx attribute to value. +// If not specified, defaults to DT_INT32 +func UniqueWithCountsOutIdx(value tf.DataType) UniqueWithCountsAttr { + return func(m optionalAttr) { + m["out_idx"] = value + } +} + +// Finds unique elements in a 1-D tensor. +// +// This operation returns a tensor `y` containing all of the unique elements of `x` +// sorted in the same order that they occur in `x`. This operation also returns a +// tensor `idx` the same size as `x` that contains the index of each value of `x` +// in the unique output `y`. Finally, it returns a third tensor `count` that +// contains the count of each element of `y` in `x`. In other words: +// +// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` +// +// For example: +// +// ``` +// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] +// y, idx, count = unique_with_counts(x) +// y ==> [1, 2, 4, 7, 8] +// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] +// count ==> [2, 1, 3, 1, 2] +// ``` +// +// Arguments: +// x: 1-D. +// +// Returns: +// y: 1-D. +// idx: 1-D. +// count: 1-D. +func UniqueWithCounts(scope *Scope, x tf.Output, optional ...UniqueWithCountsAttr) (y tf.Output, idx tf.Output, count tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UniqueWithCounts", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResizeBicubicGradAttr is an optional argument to ResizeBicubicGrad. +type ResizeBicubicGradAttr func(optionalAttr) + +// ResizeBicubicGradAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and grad tensors are +// aligned. Defaults to false. +// If not specified, defaults to false +func ResizeBicubicGradAlignCorners(value bool) ResizeBicubicGradAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeBicubicGradHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBicubicGradHalfPixelCenters(value bool) ResizeBicubicGradAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Computes the gradient of bicubic interpolation. +// +// Arguments: +// grads: 4-D with shape `[batch, height, width, channels]`. +// original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`, +// The image tensor that was resized. +// +// Returns 4-D with shape `[batch, orig_height, orig_width, channels]`. +// Gradients with respect to the input image. Input image must have been +// float or double. +func ResizeBicubicGrad(scope *Scope, grads tf.Output, original_image tf.Output, optional ...ResizeBicubicGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeBicubicGrad", + Input: []tf.Input{ + grads, original_image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`. +// +// tensor: The tensor to put on the list. +// input_handle: The old list. +// output_handle: A list with the elements of the old list followed by tensor. +// element_dtype: the type of elements in the list. +// element_shape: a shape compatible with that of elements in the list. +func TensorListPushBack(scope *Scope, input_handle tf.Output, tensor tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListPushBack", + Input: []tf.Input{ + input_handle, tensor, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns which elements of x are NaN. +// +// @compatibility(numpy) +// Equivalent to np.isnan +// @end_compatibility +// +// Example: +// +// ```python +// x = tf.constant([5.0, np.nan, 6.8, np.nan, np.inf]) +// tf.math.is_nan(x) ==> [False, True, False, True, False] +// ``` +func IsNan(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsNan", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds a value to the current value of a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to +// see the incremented value or a subsequent newer one. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value by which the variable will be incremented. +// +// Returns the created operation. +func AssignAddVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AssignAddVariableOp", + Input: []tf.Input{ + resource, value, + }, + } + return scope.AddOperation(opspec) +} + +// DepthwiseConv2dNativeBackpropInputAttr is an optional argument to DepthwiseConv2dNativeBackpropInput. +type DepthwiseConv2dNativeBackpropInputAttr func(optionalAttr) + +// DepthwiseConv2dNativeBackpropInputExplicitPaddings sets the optional explicit_paddings attribute to value. +// If not specified, defaults to <> +func DepthwiseConv2dNativeBackpropInputExplicitPaddings(value []int64) DepthwiseConv2dNativeBackpropInputAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// DepthwiseConv2dNativeBackpropInputDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dNativeBackpropInputAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeBackpropInputDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of depthwise convolution with respect to the input. +// +// Arguments: +// input_sizes: An integer vector representing the shape of `input`, based +// on `data_format`. For example, if `data_format` is 'NHWC' then +// `input` is a 4-D `[batch, height, width, channels]` tensor. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, depthwise_multiplier]`. +// out_backprop: 4-D with shape based on `data_format`. +// For example, if `data_format` is 'NHWC' then +// out_backprop shape is `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape according to `data_format`. For example, if +// `data_format` is 'NHWC', output shape is `[batch, in_height, +// in_width, in_channels]`. Gradient w.r.t. the input of the +// convolution. +func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropInputAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthwiseConv2dNativeBackpropInput", + Input: []tf.Input{ + input_sizes, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Updates the table to associates keys with values. +// +// The tensor `keys` must be of the same type as the keys of the table. +// The tensor `values` must be of the type of the table values. +// +// Arguments: +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. +// values: Values to associate with keys. +// +// Returns the created operation. +func LookupTableInsertV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableInsertV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} + +// Component-wise multiplies a SparseTensor by a dense Tensor. +// +// The output locations corresponding to the implicitly zero elements in the sparse +// tensor will be zero (i.e., will not take up storage space), regardless of the +// contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). +// +// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not +// the other direction. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseMul(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseDenseCwiseMul", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Performs a padding as a preprocess during a convolution. +// +// Similar to FusedResizeAndPadConv2d, this op allows for an optimized +// implementation where the spatial padding transformation stage is fused with the +// im2col lookup, but in this case without the bilinear filtering required for +// resizing. Fusing the padding prevents the need to write out the intermediate +// results as whole tensors, reducing memory pressure, and we can get some latency +// gains by merging the transformation calculations. +// The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' +// order is used instead. +// Internally this op uses a single per-graph scratch buffer, which means that it +// will block if multiple versions are being run in parallel. This is because this +// operator is primarily an optimization to minimize memory usage. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. +// +// strides: 1-D of length 4. The stride of the sliding window for each dimension +// of `input`. Must be in the same order as the dimension specified with format. +// padding: The type of padding algorithm to use. +func FusedPadConv2D(scope *Scope, input tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "FusedPadConv2D", + Input: []tf.Input{ + input, paddings, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// List of the given size with empty elements. +// +// element_shape: the shape of the future elements of the list +// num_elements: the number of elements to reserve +// handle: the output list +// element_dtype: the desired type of elements in the list. +func TensorListReserve(scope *Scope, element_shape tf.Output, num_elements tf.Output, element_dtype tf.DataType) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "TensorListReserve", + Input: []tf.Input{ + element_shape, num_elements, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Clips tensor values to a specified min and max. +// +// Given a tensor `t`, this operation returns a tensor of the same type and +// shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`. +// Any values less than `clip_value_min` are set to `clip_value_min`. Any values +// greater than `clip_value_max` are set to `clip_value_max`. +// +// Arguments: +// t: A `Tensor`. +// clip_value_min: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape +// as `t`. The minimum value to clip by. +// clip_value_max: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape +// as `t`. The maximum value to clip by. +// +// Returns A clipped `Tensor` with the same shape as input 't'. +func ClipByValue(scope *Scope, t tf.Output, clip_value_min tf.Output, clip_value_max tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ClipByValue", + Input: []tf.Input{ + t, clip_value_min, clip_value_max, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Conv2DBackpropFilterAttr is an optional argument to Conv2DBackpropFilter. +type Conv2DBackpropFilterAttr func(optionalAttr) + +// Conv2DBackpropFilterUseCudnnOnGpu sets the optional use_cudnn_on_gpu attribute to value. +// If not specified, defaults to true +func Conv2DBackpropFilterUseCudnnOnGpu(value bool) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["use_cudnn_on_gpu"] = value + } +} + +// Conv2DBackpropFilterExplicitPaddings sets the optional explicit_paddings attribute to value. +// +// value: If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith +// dimension, the amount of padding inserted before and after the dimension is +// `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If +// `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. +// If not specified, defaults to <> +func Conv2DBackpropFilterExplicitPaddings(value []int64) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// Conv2DBackpropFilterDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Conv2DBackpropFilterDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of convolution with respect to the filter. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 4-D +// `[filter_height, filter_width, in_channels, out_channels]` tensor. +// out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. Must be in the same order as the dimension specified with +// format. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. +// the `filter` input of the convolution. +func Conv2DBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv2DBackpropFilterAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv2DBackpropFilter", + Input: []tf.Input{ + input, filter_sizes, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of x OR y element-wise. +// +// *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func LogicalOr(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogicalOr", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds `bias` to `value`. +// +// This is a deprecated version of BiasAdd and will be soon removed. +// +// This is a special case of `tf.add` where `bias` is restricted to be 1-D. +// Broadcasting is supported, so `value` may have any number of dimensions. +// +// Arguments: +// value: Any number of dimensions. +// bias: 1-D with size the last dimension of `value`. +// +// Returns Broadcasted sum of `value` and `bias`. +func BiasAddV1(scope *Scope, value tf.Output, bias tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BiasAddV1", + Input: []tf.Input{ + value, bias, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a Dataset that returns pseudorandom numbers. +// +// Creates a Dataset that returns a stream of uniformly distributed +// pseudorandom 64-bit signed integers. +// +// In the TensorFlow Python API, you can instantiate this dataset via the +// class `tf.data.experimental.RandomDataset`. +// +// Instances of this dataset are also created as a result of the +// `hoist_random_uniform` static optimization. Whether this optimization is +// performed is determined by the `experimental_optimization.hoist_random_uniform` +// option of `tf.data.Options`. +// +// Arguments: +// seed: A scalar seed for the random number generator. If either seed or +// seed2 is set to be non-zero, the random number generator is seeded +// by the given seed. Otherwise, a random seed is used. +// seed2: A second scalar seed to avoid seed collision. +// +// +func RandomDataset(scope *Scope, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "RandomDataset", + Input: []tf.Input{ + seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FractionalAvgPoolAttr is an optional argument to FractionalAvgPool. +type FractionalAvgPoolAttr func(optionalAttr) + +// FractionalAvgPoolPseudoRandom sets the optional pseudo_random attribute to value. +// +// value: When set to True, generates the pooling sequence in a +// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin +// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for +// difference between pseudorandom and random. +// If not specified, defaults to false +func FractionalAvgPoolPseudoRandom(value bool) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["pseudo_random"] = value + } +} + +// FractionalAvgPoolOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [41/3, 26/3] for fractional avg pooling. +// If not specified, defaults to false +func FractionalAvgPoolOverlapping(value bool) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// FractionalAvgPoolDeterministic sets the optional deterministic attribute to value. +// +// value: When set to True, a fixed pooling region will be used when +// iterating over a FractionalAvgPool node in the computation graph. Mainly used +// in unit test to make FractionalAvgPool deterministic. +// If not specified, defaults to false +func FractionalAvgPoolDeterministic(value bool) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["deterministic"] = value + } +} + +// FractionalAvgPoolSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func FractionalAvgPoolSeed(value int64) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// FractionalAvgPoolSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func FractionalAvgPoolSeed2(value int64) FractionalAvgPoolAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Performs fractional average pooling on the input. +// +// Fractional average pooling is similar to Fractional max pooling in the pooling +// region generation step. The only difference is that after pooling regions are +// generated, a mean operation is performed instead of a max operation in each +// pooling region. +// +// Arguments: +// value: 4-D with shape `[batch, height, width, channels]`. +// pooling_ratio: Pooling ratio for each dimension of `value`, currently only +// supports row and col dimension and should be >= 1.0. For example, a valid +// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements +// must be 1.0 because we don't allow pooling on batch and channels +// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions +// respectively. +// +// Returns: +// output: output tensor after fractional avg pooling. +// row_pooling_sequence: row pooling sequence, needed to calculate gradient. +// col_pooling_sequence: column pooling sequence, needed to calculate gradient. +func FractionalAvgPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalAvgPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pooling_ratio": pooling_ratio} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalAvgPool", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// MapStageAttr is an optional argument to MapStage. +type MapStageAttr func(optionalAttr) + +// MapStageCapacity sets the optional capacity attribute to value. +// +// value: Maximum number of elements in the Staging Area. If > 0, inserts +// on the container will block when the capacity is reached. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapStageCapacity(value int64) MapStageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapStageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapStageMemoryLimit(value int64) MapStageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapStageContainer sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. Otherwise, +// a default container is used. +// If not specified, defaults to "" +func MapStageContainer(value string) MapStageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapStageSharedName sets the optional shared_name attribute to value. +// +// value: It is necessary to match this name to the matching Unstage Op. +// If not specified, defaults to "" +func MapStageSharedName(value string) MapStageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Stage (key, values) in the underlying container which behaves like a hashtable. +// +// Arguments: +// key: int64 +// +// values: a list of tensors +// dtypes A list of data types that inserted values should adhere to. +// +// +// Returns the created operation. +func MapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output, dtypes []tf.DataType, optional ...MapStageAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapStage", + Input: []tf.Input{ + key, indices, tf.OutputList(values), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SparseTensorDenseMatMulAttr is an optional argument to SparseTensorDenseMatMul. +type SparseTensorDenseMatMulAttr func(optionalAttr) + +// SparseTensorDenseMatMulAdjointA sets the optional adjoint_a attribute to value. +// +// value: Use the adjoint of A in the matrix multiply. If A is complex, this +// is transpose(conj(A)). Otherwise it's transpose(A). +// If not specified, defaults to false +func SparseTensorDenseMatMulAdjointA(value bool) SparseTensorDenseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_a"] = value + } +} + +// SparseTensorDenseMatMulAdjointB sets the optional adjoint_b attribute to value. +// +// value: Use the adjoint of B in the matrix multiply. If B is complex, this +// is transpose(conj(B)). Otherwise it's transpose(B). +// If not specified, defaults to false +func SparseTensorDenseMatMulAdjointB(value bool) SparseTensorDenseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_b"] = value + } +} + +// Multiply SparseTensor (of rank 2) "A" by dense matrix "B". +// +// No validity checking is performed on the indices of A. However, the following +// input format is recommended for optimal behavior: +// +// if adjoint_a == false: +// A should be sorted in lexicographically increasing order. Use SparseReorder +// if you're not sure. +// if adjoint_a == true: +// A should be sorted in order of increasing dimension 1 (i.e., "column major" +// order instead of "row major" order). +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix. +// a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector. +// b: 2-D. A dense Matrix. +func SparseTensorDenseMatMul(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output, optional ...SparseTensorDenseMatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseTensorDenseMatMul", + Input: []tf.Input{ + a_indices, a_values, a_shape, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FusedBatchNormGradV2Attr is an optional argument to FusedBatchNormGradV2. +type FusedBatchNormGradV2Attr func(optionalAttr) + +// FusedBatchNormGradV2Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormGradV2Epsilon(value float32) FusedBatchNormGradV2Attr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormGradV2DataFormat sets the optional data_format attribute to value. +// +// value: The data format for y_backprop, x, x_backprop. +// Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormGradV2DataFormat(value string) FusedBatchNormGradV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormGradV2IsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormGradV2IsTraining(value bool) FusedBatchNormGradV2Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Gradient for batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// y_backprop: A 4D Tensor for the gradient with respect to y. +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +// mean to be reused in gradient computation. When is_training is +// False, a 1D Tensor for the population mean to be reused in both +// 1st and 2nd order gradient computation. +// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch +// variance (inverted variance in the cuDNN case) to be reused in +// gradient computation. When is_training is False, a 1D Tensor +// for the population variance to be reused in both 1st and 2nd +// order gradient computation. +// +// Returns: +// x_backprop: A 4D Tensor for the gradient with respect to x. +// scale_backprop: A 1D Tensor for the gradient with respect to scale. +// offset_backprop: A 1D Tensor for the gradient with respect to offset. +// reserve_space_3: Unused placeholder to match the mean input in FusedBatchNorm. +// reserve_space_4: Unused placeholder to match the variance input +// in FusedBatchNorm. +func FusedBatchNormGradV2(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradV2Attr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormGradV2", + Input: []tf.Input{ + y_backprop, x, scale, reserve_space_1, reserve_space_2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Computes the gradient for the inverse of `x` wrt its input. +// +// Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` +// is the corresponding input gradient. +func InvGrad(scope *Scope, y tf.Output, dy tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InvGrad", + Input: []tf.Input{ + y, dy, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds up a SparseTensor and a dense Tensor, using these special rules: +// +// (1) Broadcasts the dense side to have the same shape as the sparse side, if +// eligible; +// (2) Then, only the dense values pointed to by the indices of the SparseTensor +// participate in the cwise addition. +// +// By these rules, the result is a logical SparseTensor with exactly the same +// indices and shape, but possibly with different non-zero values. The output of +// this Op is the resultant non-zero values. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseAdd(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseDenseCwiseAdd", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Counts the number of occurrences of each value in an integer array. +// +// Outputs a vector with length `size` and the same dtype as `weights`. If +// `weights` are empty, then index `i` stores the number of times the value `i` is +// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of +// the value in `weights` at each index where the corresponding value in `arr` is +// `i`. +// +// Values in `arr` outside of the range [0, size) are ignored. +// +// Arguments: +// arr: int32 `Tensor`. +// size: non-negative int32 scalar `Tensor`. +// weights: is an int32, int64, float32, or float64 `Tensor` with the same +// shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights +// equal to 1. +// +// Returns 1D `Tensor` with length equal to `size`. The counts or summed weights for +// each value in the range [0, size). +func Bincount(scope *Scope, arr tf.Output, size tf.Output, weights tf.Output) (bins tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Bincount", + Input: []tf.Input{ + arr, size, weights, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gradients for batch normalization. +// +// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization() +// +// This op is deprecated. See `tf.nn.batch_normalization`. +// +// Arguments: +// t: A 4D input Tensor. +// m: A 1D mean Tensor with size matching the last dimension of t. +// This is the first output from tf.nn.moments, +// or a saved moving average thereof. +// v: A 1D variance Tensor with size matching the last dimension of t. +// This is the second output from tf.nn.moments, +// or a saved moving average thereof. +// gamma: A 1D gamma Tensor with size matching the last dimension of t. +// If "scale_after_normalization" is true, this Tensor will be multiplied +// with the normalized Tensor. +// backprop: 4D backprop Tensor. +// variance_epsilon: A small float number to avoid dividing by 0. +// scale_after_normalization: A bool indicating whether the resulted tensor +// needs to be multiplied with gamma. +// +// Returns: +// dx: 4D backprop tensor for input. +// dm: 1D backprop tensor for mean. +// dv: 1D backprop tensor for variance. +// db: 1D backprop tensor for beta. +// dg: 1D backprop tensor for gamma. +func BatchNormWithGlobalNormalizationGrad(scope *Scope, t tf.Output, m tf.Output, v tf.Output, gamma tf.Output, backprop tf.Output, variance_epsilon float32, scale_after_normalization bool) (dx tf.Output, dm tf.Output, dv tf.Output, db tf.Output, dg tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} + opspec := tf.OpSpec{ + Type: "BatchNormWithGlobalNormalizationGrad", + Input: []tf.Input{ + t, m, v, gamma, backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// LoadAndRemapMatrixAttr is an optional argument to LoadAndRemapMatrix. +type LoadAndRemapMatrixAttr func(optionalAttr) + +// LoadAndRemapMatrixMaxRowsInMemory sets the optional max_rows_in_memory attribute to value. +// +// value: The maximum number of rows to load from the checkpoint at +// once. If less than or equal to 0, the entire matrix will be loaded into +// memory. Setting this arg trades increased disk reads for lower memory usage. +// If not specified, defaults to -1 +func LoadAndRemapMatrixMaxRowsInMemory(value int64) LoadAndRemapMatrixAttr { + return func(m optionalAttr) { + m["max_rows_in_memory"] = value + } +} + +// Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint +// +// at `ckpt_path` and potentially reorders its rows and columns using the +// specified remappings. +// +// Most users should use one of the wrapper initializers (such as +// `tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this +// function directly. +// +// The remappings are 1-D tensors with the following properties: +// +// * `row_remapping` must have exactly `num_rows` entries. Row `i` of the output +// matrix will be initialized from the row corresponding to index +// `row_remapping[i]` in the old `Tensor` from the checkpoint. +// * `col_remapping` must have either 0 entries (indicating that no column +// reordering is needed) or `num_cols` entries. If specified, column `j` of the +// output matrix will be initialized from the column corresponding to index +// `col_remapping[j]` in the old `Tensor` from the checkpoint. +// * A value of -1 in either of the remappings signifies a "missing" entry. In that +// case, values from the `initializing_values` tensor will be used to fill that +// missing row or column. If `row_remapping` has `r` missing entries and +// `col_remapping` has `c` missing entries, then the following condition must be +// true: +// +// `(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)` +// +// The remapping tensors can be generated using the GenerateVocabRemapping op. +// +// As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1], +// initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing +// the value from row i, column j of the old tensor in the checkpoint, the output +// matrix will look like the following: +// +// [[w(1, 0), w(1, 2), 0.5], +// [w(0, 0), w(0, 2), -0.5], +// [0.25, -0.25, 42]] +// +// Arguments: +// ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from +// which the old matrix `Tensor` will be loaded. +// old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint. +// row_remapping: An int `Tensor` of row remappings (generally created by +// `generate_vocab_remapping`). Even if no row remapping is needed, this must +// still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted +// index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`). +// col_remapping: An int `Tensor` of column remappings (generally created by +// `generate_vocab_remapping`). May be a size-0 `Tensor` if only row remapping +// is to be done (e.g. column ordering is the same). +// initializing_values: A float `Tensor` containing values to fill in for cells +// in the output matrix that are not loaded from the checkpoint. Length must be +// exactly the same as the number of missing / new cells. +// num_rows: Number of rows (length of the 1st dimension) in the output matrix. +// num_cols: Number of columns (length of the 2nd dimension) in the output matrix. +// +// Returns Output matrix containing existing values loaded from the +// checkpoint, and with any missing values filled in from initializing_values. +func LoadAndRemapMatrix(scope *Scope, ckpt_path tf.Output, old_tensor_name tf.Output, row_remapping tf.Output, col_remapping tf.Output, initializing_values tf.Output, num_rows int64, num_cols int64, optional ...LoadAndRemapMatrixAttr) (output_matrix tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_rows": num_rows, "num_cols": num_cols} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadAndRemapMatrix", + Input: []tf.Input{ + ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Does nothing. Only useful as a placeholder for control edges. +// +// Returns the created operation. +func NoOp(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NoOp", + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyRMSPropAttr is an optional argument to ResourceSparseApplyRMSProp. +type ResourceSparseApplyRMSPropAttr func(optionalAttr) + +// ResourceSparseApplyRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, ms, and mom tensors is protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyRMSPropUseLocking(value bool) ResourceSparseApplyRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the RMSProp algorithm. +// +// Note that in dense implementation of this algorithm, ms and mom will +// update even if the grad is zero, but in this sparse implementation, ms +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. +// +// Returns the created operation. +func ResourceSparseApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyRMSProp", + Input: []tf.Input{ + var_, ms, mom, lr, rho, momentum, epsilon, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// StringToNumberAttr is an optional argument to StringToNumber. +type StringToNumberAttr func(optionalAttr) + +// StringToNumberOutType sets the optional out_type attribute to value. +// +// value: The numeric type to interpret each string in `string_tensor` as. +// If not specified, defaults to DT_FLOAT +func StringToNumberOutType(value tf.DataType) StringToNumberAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Converts each string in the input Tensor to the specified numeric type. +// +// (Note that int32 overflow results in an error while float overflow +// results in a rounded value.) +// +// Example: +// +// >>> strings = ["5.0", "3.0", "7.0"] +// >>> tf.strings.to_number(strings) +// +// +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToNumber(scope *Scope, string_tensor tf.Output, optional ...StringToNumberAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringToNumber", + Input: []tf.Input{ + string_tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a Tensor into a serialized TensorProto proto. +// +// Arguments: +// tensor: A Tensor of type `T`. +// +// Returns A serialized TensorProto proto of the input tensor. +func SerializeTensor(scope *Scope, tensor tf.Output) (serialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SerializeTensor", + Input: []tf.Input{ + tensor, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a serialized tensorflow.TensorProto proto into a Tensor. +// +// Arguments: +// serialized: A scalar string containing a serialized TensorProto proto. +// out_type: The type of the serialized tensor. The provided type must match the +// type of the serialized tensor and no implicit conversion will take place. +// +// Returns A Tensor of type `out_type`. +func ParseTensor(scope *Scope, serialized tf.Output, out_type tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + opspec := tf.OpSpec{ + Type: "ParseTensor", + Input: []tf.Input{ + serialized, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise integer closest to x. +// +// If the result is midway between two representable values, +// the even representable is chosen. +// For example: +// +// ``` +// rint(-1.5) ==> -2.0 +// rint(0.5000001) ==> 1.0 +// rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.] +// ``` +func Rint(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Rint", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reverses specific dimensions of a tensor. +// +// Given a `tensor`, and a `bool` tensor `dims` representing the dimensions +// of `tensor`, this operation reverses each dimension i of `tensor` where +// `dims[i]` is `True`. +// +// `tensor` can have up to 8 dimensions. The number of dimensions +// of `tensor` must equal the number of elements in `dims`. In other words: +// +// `rank(tensor) = size(dims)` +// +// For example: +// +// ``` +// # tensor 't' is [[[[ 0, 1, 2, 3], +// # [ 4, 5, 6, 7], +// # [ 8, 9, 10, 11]], +// # [[12, 13, 14, 15], +// # [16, 17, 18, 19], +// # [20, 21, 22, 23]]]] +// # tensor 't' shape is [1, 2, 3, 4] +// +// # 'dims' is [False, False, False, True] +// reverse(t, dims) ==> [[[[ 3, 2, 1, 0], +// [ 7, 6, 5, 4], +// [ 11, 10, 9, 8]], +// [[15, 14, 13, 12], +// [19, 18, 17, 16], +// [23, 22, 21, 20]]]] +// +// # 'dims' is [False, True, False, False] +// reverse(t, dims) ==> [[[[12, 13, 14, 15], +// [16, 17, 18, 19], +// [20, 21, 22, 23] +// [[ 0, 1, 2, 3], +// [ 4, 5, 6, 7], +// [ 8, 9, 10, 11]]]] +// +// # 'dims' is [False, False, True, False] +// reverse(t, dims) ==> [[[[8, 9, 10, 11], +// [4, 5, 6, 7], +// [0, 1, 2, 3]] +// [[20, 21, 22, 23], +// [16, 17, 18, 19], +// [12, 13, 14, 15]]]] +// ``` +// +// Arguments: +// tensor: Up to 8-D. +// dims: 1-D. The dimensions to reverse. +// +// Returns The same shape as `tensor`. +func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Reverse", + Input: []tf.Input{ + tensor, dims, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Wraps an arbitrary MLIR computation expressed as a module with a main() function. +// +// This operation does not have an associated kernel and is not intended to be +// executed in a regular TensorFlow session. Instead it is intended to be used for +// testing or for special case where a user intends to pass custom MLIR computation +// through a TensorFlow graph with the intent of having custom tooling processing +// it downstream (when targeting a different environment, like TensorFlow lite for +// example). +// The MLIR module is expected to have a main() function that will be used as an +// entry point. The inputs to the operations will be passed as argument to the +// main() function and the returned values of the main function mapped to the +// outputs. +// Example usage: +// +// ``` +// import tensorflow as tf +// from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op +// +// mlir_module = '''python +// func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> { +// %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32> +// return %ret : tensor<10x10xf32> +// } +// ''' +// +// @tf.function +// def foo(x, y): +// return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32]) +// +// graph_def = foo.get_concrete_function(tf.TensorSpec([10], tf.float32), tf.TensorSpec([10], tf.float32)).graph.as_graph_def() +// ``` +func MlirPassthroughOp(scope *Scope, inputs []tf.Output, mlir_module string, Toutputs []tf.DataType) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mlir_module": mlir_module, "Toutputs": Toutputs} + opspec := tf.OpSpec{ + Type: "MlirPassthroughOp", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("MlirPassthroughOp", err) + return + } + return outputs +} + +// StringLowerAttr is an optional argument to StringLower. +type StringLowerAttr func(optionalAttr) + +// StringLowerEncoding sets the optional encoding attribute to value. +// If not specified, defaults to "" +func StringLowerEncoding(value string) StringLowerAttr { + return func(m optionalAttr) { + m["encoding"] = value + } +} + +// Converts all uppercase characters into their respective lowercase replacements. +// +// Example: +// +// >>> tf.strings.lower("CamelCase string and ALL CAPS") +// +// +func StringLower(scope *Scope, input tf.Output, optional ...StringLowerAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringLower", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParseSequenceExampleV2Attr is an optional argument to ParseSequenceExampleV2. +type ParseSequenceExampleV2Attr func(optionalAttr) + +// ParseSequenceExampleV2NcontextSparse sets the optional Ncontext_sparse attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleV2NcontextSparse(value int64) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["Ncontext_sparse"] = value + } +} + +// ParseSequenceExampleV2ContextSparseTypes sets the optional context_sparse_types attribute to value. +// +// value: A list of Ncontext_sparse types; the data types of data in +// each context Feature given in context_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2ContextSparseTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["context_sparse_types"] = value + } +} + +// ParseSequenceExampleV2ContextRaggedValueTypes sets the optional context_ragged_value_types attribute to value. +// +// value: RaggedTensor.value dtypes for the ragged context features. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2ContextRaggedValueTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["context_ragged_value_types"] = value + } +} + +// ParseSequenceExampleV2ContextRaggedSplitTypes sets the optional context_ragged_split_types attribute to value. +// +// value: RaggedTensor.row_split dtypes for the ragged context features. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2ContextRaggedSplitTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["context_ragged_split_types"] = value + } +} + +// ParseSequenceExampleV2ContextDenseShapes sets the optional context_dense_shapes attribute to value. +// +// value: A list of Ncontext_dense shapes; the shapes of data in +// each context Feature given in context_dense_keys. +// The number of elements in the Feature corresponding to context_dense_key[j] +// must always equal context_dense_shapes[j].NumEntries(). +// The shape of context_dense_values[j] will match context_dense_shapes[j]. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2ContextDenseShapes(value []tf.Shape) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["context_dense_shapes"] = value + } +} + +// ParseSequenceExampleV2NfeatureListSparse sets the optional Nfeature_list_sparse attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleV2NfeatureListSparse(value int64) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["Nfeature_list_sparse"] = value + } +} + +// ParseSequenceExampleV2NfeatureListDense sets the optional Nfeature_list_dense attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleV2NfeatureListDense(value int64) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["Nfeature_list_dense"] = value + } +} + +// ParseSequenceExampleV2FeatureListDenseTypes sets the optional feature_list_dense_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2FeatureListDenseTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["feature_list_dense_types"] = value + } +} + +// ParseSequenceExampleV2FeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value. +// +// value: A list of Nfeature_list_sparse types; the data types +// of data in each FeatureList given in feature_list_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2FeatureListSparseTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["feature_list_sparse_types"] = value + } +} + +// ParseSequenceExampleV2FeatureListRaggedValueTypes sets the optional feature_list_ragged_value_types attribute to value. +// +// value: RaggedTensor.value dtypes for the ragged FeatureList features. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2FeatureListRaggedValueTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["feature_list_ragged_value_types"] = value + } +} + +// ParseSequenceExampleV2FeatureListRaggedSplitTypes sets the optional feature_list_ragged_split_types attribute to value. +// +// value: RaggedTensor.row_split dtypes for the ragged FeatureList features. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2FeatureListRaggedSplitTypes(value []tf.DataType) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["feature_list_ragged_split_types"] = value + } +} + +// ParseSequenceExampleV2FeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value. +// +// value: A list of Nfeature_list_dense shapes; the shapes of +// data in each FeatureList given in feature_list_dense_keys. +// The shape of each Feature in the FeatureList corresponding to +// feature_list_dense_key[j] must always equal +// feature_list_dense_shapes[j].NumEntries(). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleV2FeatureListDenseShapes(value []tf.Shape) ParseSequenceExampleV2Attr { + return func(m optionalAttr) { + m["feature_list_dense_shapes"] = value + } +} + +// Transforms a vector of tf.io.SequenceExample protos (as strings) into +// typed tensors. +// +// Arguments: +// serialized: A scalar or vector containing binary serialized SequenceExample protos. +// debug_name: A scalar or vector containing the names of the serialized protos. +// May contain, for example, table key (descriptive) name for the +// corresponding serialized proto. This is purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty vector if no name is available. +// context_sparse_keys: The keys expected in the Examples' features associated with context_sparse +// values. +// context_dense_keys: The keys expected in the SequenceExamples' context features associated with +// dense values. +// context_ragged_keys: The keys expected in the Examples' features associated with context_ragged +// values. +// feature_list_sparse_keys: The keys expected in the FeatureLists associated with sparse values. +// feature_list_dense_keys: The keys expected in the SequenceExamples' feature_lists associated +// with lists of dense values. +// feature_list_ragged_keys: The keys expected in the FeatureLists associated with ragged values. +// feature_list_dense_missing_assumed_empty: A vector corresponding 1:1 with feature_list_dense_keys, indicating which +// features may be missing from the SequenceExamples. If the associated +// FeatureList is missing, it is treated as empty. +// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty). +// context_dense_defaults[j] provides default values +// when the SequenceExample's context map lacks context_dense_key[j]. +// If an empty Tensor is provided for context_dense_defaults[j], +// then the Feature context_dense_keys[j] is required. +// The input type is inferred from context_dense_defaults[j], even when it's +// empty. If context_dense_defaults[j] is not empty, its shape must match +// context_dense_shapes[j]. +func ParseSequenceExampleV2(scope *Scope, serialized tf.Output, debug_name tf.Output, context_sparse_keys tf.Output, context_dense_keys tf.Output, context_ragged_keys tf.Output, feature_list_sparse_keys tf.Output, feature_list_dense_keys tf.Output, feature_list_ragged_keys tf.Output, feature_list_dense_missing_assumed_empty tf.Output, context_dense_defaults []tf.Output, optional ...ParseSequenceExampleV2Attr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, context_ragged_values []tf.Output, context_ragged_row_splits []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output, feature_list_dense_lengths []tf.Output, feature_list_ragged_values []tf.Output, feature_list_ragged_outer_splits []tf.Output, feature_list_ragged_inner_splits []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParseSequenceExampleV2", + Input: []tf.Input{ + serialized, debug_name, context_sparse_keys, context_dense_keys, context_ragged_keys, feature_list_sparse_keys, feature_list_dense_keys, feature_list_ragged_keys, feature_list_dense_missing_assumed_empty, tf.OutputList(context_dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if context_ragged_values, idx, err = makeOutputList(op, idx, "context_ragged_values"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if context_ragged_row_splits, idx, err = makeOutputList(op, idx, "context_ragged_row_splits"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_dense_lengths, idx, err = makeOutputList(op, idx, "feature_list_dense_lengths"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_ragged_values, idx, err = makeOutputList(op, idx, "feature_list_ragged_values"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_ragged_outer_splits, idx, err = makeOutputList(op, idx, "feature_list_ragged_outer_splits"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + if feature_list_ragged_inner_splits, idx, err = makeOutputList(op, idx, "feature_list_ragged_inner_splits"); err != nil { + scope.UpdateErr("ParseSequenceExampleV2", err) + return + } + return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, context_ragged_values, context_ragged_row_splits, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values, feature_list_dense_lengths, feature_list_ragged_values, feature_list_ragged_outer_splits, feature_list_ragged_inner_splits +} + +// Gives a guarantee to the TF runtime that the input tensor is a constant. +// +// The runtime is then free to make optimizations based on this. +// +// Only accepts value typed tensors as inputs and rejects resource variable handles +// as input. +// +// Returns the input tensor without modification. +func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GuaranteeConst", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a tf.Example proto (as a string) into typed tensors. +// +// Arguments: +// serialized: A vector containing a batch of binary serialized Example protos. +// dense_defaults: A list of Tensors (some may be empty), whose length matches +// the length of `dense_keys`. dense_defaults[j] provides default values +// when the example's feature_map lacks dense_key[j]. If an empty Tensor is +// provided for dense_defaults[j], then the Feature dense_keys[j] is required. +// The input type is inferred from dense_defaults[j], even when it's empty. +// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, +// then the shape of dense_defaults[j] must match that of dense_shapes[j]. +// If dense_shapes[j] has an undefined major dimension (variable strides dense +// feature), dense_defaults[j] must contain a single element: +// the padding element. +// num_sparse: The number of sparse features to be parsed from the example. This +// must match the lengths of `sparse_keys` and `sparse_types`. +// sparse_keys: A list of `num_sparse` strings. +// The keys expected in the Examples' features associated with sparse values. +// dense_keys: The keys expected in the Examples' features associated with dense +// values. +// sparse_types: A list of `num_sparse` types; the data types of data in each +// Feature given in sparse_keys. +// Currently the ParseSingleExample op supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// dense_shapes: The shapes of data in each Feature given in dense_keys. +// The length of this list must match the length of `dense_keys`. The +// number of elements in the Feature corresponding to dense_key[j] must +// always equal dense_shapes[j].NumEntries(). If dense_shapes[j] == +// (D0, D1, ..., DN) then the shape of output Tensor dense_values[j] +// will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1, +// ..., DN), the shape of the output Tensor dense_values[j] will be (M, +// D1, .., DN), where M is the number of blocks of elements of length +// D1 * .... * DN, in the input. +func ParseSingleExample(scope *Scope, serialized tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_keys []string, dense_keys []string, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_keys": sparse_keys, "dense_keys": dense_keys, "sparse_types": sparse_types, "dense_shapes": dense_shapes} + opspec := tf.OpSpec{ + Type: "ParseSingleExample", + Input: []tf.Input{ + serialized, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { + scope.UpdateErr("ParseSingleExample", err) + return + } + return sparse_indices, sparse_values, sparse_shapes, dense_values +} + +// Scatter `updates` into a new tensor according to `indices`. +// +// Creates a new tensor by applying sparse `updates` to individual values or +// slices within a tensor (initially zero for numeric, empty for string) of +// the given `shape` according to indices. This operator is the inverse of the +// `tf.gather_nd` operator which extracts values or slices from a given tensor. +// +// This operation is similar to tensor_scatter_add, except that the tensor is +// zero-initialized. Calling `tf.scatter_nd(indices, values, shape)` is identical +// to `tensor_scatter_add(tf.zeros(shape, values.dtype), indices, values)` +// +// If `indices` contains duplicates, then their updates are accumulated (summed). +// +// **WARNING**: The order in which updates are applied is nondeterministic, so the +// output will be nondeterministic if `indices` contains duplicates -- because +// of some numerical approximation issues, numbers summed in different order +// may yield different results. +// +// `indices` is an integer tensor containing indices into a new tensor of shape +// `shape`. The last dimension of `indices` can be at most the rank of `shape`: +// +// indices.shape[-1] <= shape.rank +// +// The last dimension of `indices` corresponds to indices into elements +// (if `indices.shape[-1] = shape.rank`) or slices +// (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of +// `shape`. `updates` is a tensor with shape +// +// indices.shape[:-1] + shape[indices.shape[-1]:] +// +// The simplest form of scatter is to insert individual elements in a tensor by +// index. For example, say we want to insert 4 scattered elements in a rank-1 +// tensor with 8 elements. +// +//
+// +//
+// +// In Python, this scatter operation would look like this: +// +// ```python +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// shape = tf.constant([8]) +// scatter = tf.scatter_nd(indices, updates, shape) +// print(scatter) +// ``` +// +// The resulting tensor would look like this: +// +// [0, 11, 0, 10, 9, 0, 0, 12] +// +// We can also, insert entire slices of a higher rank tensor all at once. For +// example, if we wanted to insert two slices in the first dimension of a +// rank-3 tensor with two matrices of new values. +// +//
+// +//
+// +// In Python, this scatter operation would look like this: +// +// ```python +// indices = tf.constant([[0], [2]]) +// updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]], +// [[5, 5, 5, 5], [6, 6, 6, 6], +// [7, 7, 7, 7], [8, 8, 8, 8]]]) +// shape = tf.constant([4, 4, 4]) +// scatter = tf.scatter_nd(indices, updates, shape) +// print(scatter) +// ``` +// +// The resulting tensor would look like this: +// +// [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], +// [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], +// [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], +// [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]] +// +// Note that on CPU, if an out of bound index is found, an error is returned. +// On GPU, if an out of bound index is found, the index is ignored. +// +// Arguments: +// indices: Index tensor. +// updates: Updates to scatter into output. +// shape: 1-D. The shape of the resulting tensor. +// +// Returns A new tensor with the given shape and updates applied according +// to the indices. +func ScatterNd(scope *Scope, indices tf.Output, updates tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ScatterNd", + Input: []tf.Input{ + indices, updates, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UniqueAttr is an optional argument to Unique. +type UniqueAttr func(optionalAttr) + +// UniqueOutIdx sets the optional out_idx attribute to value. +// If not specified, defaults to DT_INT32 +func UniqueOutIdx(value tf.DataType) UniqueAttr { + return func(m optionalAttr) { + m["out_idx"] = value + } +} + +// Finds unique elements in a 1-D tensor. +// +// This operation returns a tensor `y` containing all of the unique elements of `x` +// sorted in the same order that they occur in `x`; `x` does not need to be sorted. +// This operation also returns a tensor `idx` the same size as `x` that contains +// the index of each value of `x` in the unique output `y`. In other words: +// +// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` +// +// Examples: +// +// ``` +// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] +// y, idx = unique(x) +// y ==> [1, 2, 4, 7, 8] +// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] +// ``` +// +// ``` +// # tensor 'x' is [4, 5, 1, 2, 3, 3, 4, 5] +// y, idx = unique(x) +// y ==> [4, 5, 1, 2, 3] +// idx ==> [0, 1, 2, 3, 4, 4, 0, 1] +// ``` +// +// Arguments: +// x: 1-D. +// +// Returns: +// y: 1-D. +// idx: 1-D. +func Unique(scope *Scope, x tf.Output, optional ...UniqueAttr) (y tf.Output, idx tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Unique", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Converts a `RaggedTensor` into a `SparseTensor` with the same values. +// +// input=ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits) +// output=SparseTensor(indices=sparse_indices, values=sparse_values, +// dense_shape=sparse_dense_shape) +// +// Arguments: +// rt_nested_splits: The `row_splits` for the `RaggedTensor`. +// rt_dense_values: The `flat_values` for the `RaggedTensor`. +// +// Returns: +// sparse_indices: The indices for the `SparseTensor`. +// sparse_values: The values of the `SparseTensor`. +// sparse_dense_shape: `sparse_dense_shape` is a tight bounding box of the input `RaggedTensor`. +func RaggedTensorToSparse(scope *Scope, rt_nested_splits []tf.Output, rt_dense_values tf.Output) (sparse_indices tf.Output, sparse_values tf.Output, sparse_dense_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RaggedTensorToSparse", + Input: []tf.Input{ + tf.OutputList(rt_nested_splits), rt_dense_values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the name of the device on which `resource` has been placed. +func ExperimentalIteratorGetDevice(scope *Scope, resource tf.Output) (device tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExperimentalIteratorGetDevice", + Input: []tf.Input{ + resource, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Records the bytes size of each element of `input_dataset` in a StatsAggregator. +func ExperimentalBytesProducedStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalBytesProducedStatsDataset", + Input: []tf.Input{ + input_dataset, tag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise. +// +// See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) +// ](http://arxiv.org/abs/1511.07289) +func Elu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Elu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AddSparseToTensorsMapAttr is an optional argument to AddSparseToTensorsMap. +type AddSparseToTensorsMapAttr func(optionalAttr) + +// AddSparseToTensorsMapContainer sets the optional container attribute to value. +// +// value: The container name for the `SparseTensorsMap` created by this op. +// If not specified, defaults to "" +func AddSparseToTensorsMapContainer(value string) AddSparseToTensorsMapAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// AddSparseToTensorsMapSharedName sets the optional shared_name attribute to value. +// +// value: The shared name for the `SparseTensorsMap` created by this op. +// If blank, the new Operation's unique name is used. +// If not specified, defaults to "" +func AddSparseToTensorsMapSharedName(value string) AddSparseToTensorsMapAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Add a `SparseTensor` to a `SparseTensorsMap` return its handle. +// +// A `SparseTensor` is represented by three tensors: `sparse_indices`, +// `sparse_values`, and `sparse_shape`. +// +// This operator takes the given `SparseTensor` and adds it to a container +// object (a `SparseTensorsMap`). A unique key within this container is generated +// in the form of an `int64`, and this is the value that is returned. +// +// The `SparseTensor` can then be read out as part of a minibatch by passing +// the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure +// the correct `SparseTensorsMap` is accessed, ensure that the same +// `container` and `shared_name` are passed to that Op. If no `shared_name` +// is provided here, instead use the *name* of the Operation created by calling +// `AddSparseToTensorsMap` as the `shared_name` passed to +// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the `SparseTensor`. +// sparse_values: 1-D. The `values` of the `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the `SparseTensor`. +// +// Returns 0-D. The handle of the `SparseTensor` now stored in the +// `SparseTensorsMap`. +func AddSparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddSparseToTensorsMapAttr) (sparse_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AddSparseToTensorsMap", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Transforms a vector of tf.Example protos (as strings) into typed tensors. +// +// Arguments: +// serialized: A scalar or vector containing binary serialized Example protos. +// names: A tensor containing the names of the serialized protos. +// Corresponds 1:1 with the `serialized` tensor. +// May contain, for example, table key (descriptive) names for the +// corresponding serialized protos. These are purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty vector if no names are available. +// If non-empty, this tensor must have the same shape as "serialized". +// sparse_keys: Vector of strings. +// The keys expected in the Examples' features associated with sparse values. +// dense_keys: Vector of strings. +// The keys expected in the Examples' features associated with dense values. +// ragged_keys: Vector of strings. +// The keys expected in the Examples' features associated with ragged values. +// dense_defaults: A list of Tensors (some may be empty). Corresponds 1:1 with `dense_keys`. +// dense_defaults[j] provides default values +// when the example's feature_map lacks dense_key[j]. If an empty Tensor is +// provided for dense_defaults[j], then the Feature dense_keys[j] is required. +// The input type is inferred from dense_defaults[j], even when it's empty. +// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, +// then the shape of dense_defaults[j] must match that of dense_shapes[j]. +// If dense_shapes[j] has an undefined major dimension (variable strides dense +// feature), dense_defaults[j] must contain a single element: +// the padding element. +// num_sparse: The number of sparse keys. +// sparse_types: A list of `num_sparse` types; the data types of data in each Feature +// given in sparse_keys. +// Currently the ParseExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// ragged_value_types: A list of `num_ragged` types; the data types of data in each Feature +// given in ragged_keys (where `num_ragged = sparse_keys.size()`). +// Currently the ParseExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// ragged_split_types: A list of `num_ragged` types; the data types of row_splits in each Feature +// given in ragged_keys (where `num_ragged = sparse_keys.size()`). +// May be DT_INT32 or DT_INT64. +// dense_shapes: A list of `num_dense` shapes; the shapes of data in each Feature +// given in dense_keys (where `num_dense = dense_keys.size()`). +// The number of elements in the Feature corresponding to dense_key[j] +// must always equal dense_shapes[j].NumEntries(). +// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output +// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN): +// The dense outputs are just the inputs row-stacked by batch. +// This works for dense_shapes[j] = (-1, D1, ..., DN). In this case +// the shape of the output Tensor dense_values[j] will be +// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks +// of elements of length D1 * .... * DN, across all minibatch entries +// in the input. Any minibatch entry with less than M blocks of elements of +// length D1 * ... * DN will be padded with the corresponding default_value +// scalar element along the second dimension. +func ParseExampleV2(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys tf.Output, dense_keys tf.Output, ragged_keys tf.Output, dense_defaults []tf.Output, num_sparse int64, sparse_types []tf.DataType, ragged_value_types []tf.DataType, ragged_split_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output, ragged_values []tf.Output, ragged_row_splits []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_sparse": num_sparse, "sparse_types": sparse_types, "ragged_value_types": ragged_value_types, "ragged_split_types": ragged_split_types, "dense_shapes": dense_shapes} + opspec := tf.OpSpec{ + Type: "ParseExampleV2", + Input: []tf.Input{ + serialized, names, sparse_keys, dense_keys, ragged_keys, tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { + scope.UpdateErr("ParseExampleV2", err) + return + } + if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { + scope.UpdateErr("ParseExampleV2", err) + return + } + if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { + scope.UpdateErr("ParseExampleV2", err) + return + } + if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { + scope.UpdateErr("ParseExampleV2", err) + return + } + if ragged_values, idx, err = makeOutputList(op, idx, "ragged_values"); err != nil { + scope.UpdateErr("ParseExampleV2", err) + return + } + if ragged_row_splits, idx, err = makeOutputList(op, idx, "ragged_row_splits"); err != nil { + scope.UpdateErr("ParseExampleV2", err) + return + } + return sparse_indices, sparse_values, sparse_shapes, dense_values, ragged_values, ragged_row_splits +} + +// Saves input tensors slices to disk. +// +// This is like `Save` except that tensors can be listed in the saved file as being +// a slice of a larger tensor. `shapes_and_slices` specifies the shape of the +// larger tensor and the slice that this tensor covers. `shapes_and_slices` must +// have as many elements as `tensor_names`. +// +// Elements of the `shapes_and_slices` input must either be: +// +// * The empty string, in which case the corresponding tensor is +// saved normally. +// * A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the +// `dimI` are the dimensions of the larger tensor and `slice-spec` +// specifies what part is covered by the tensor to save. +// +// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1` +// where each `sliceI` is either: +// +// * The string `-` meaning that the slice covers all indices of this dimension +// * `start,length` where `start` and `length` are integers. In that +// case the slice covers `length` indices starting at `start`. +// +// See also `Save`. +// +// Arguments: +// filename: Must have a single element. The name of the file to which we write the +// tensor. +// tensor_names: Shape `[N]`. The names of the tensors to be saved. +// shapes_and_slices: Shape `[N]`. The shapes and slice specifications to use when +// saving the tensors. +// data: `N` tensors to save. +// +// Returns the created operation. +func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SaveSlices", + Input: []tf.Input{ + filename, tensor_names, shapes_and_slices, tf.OutputList(data), + }, + } + return scope.AddOperation(opspec) +} + +// FusedBatchNormGradV3Attr is an optional argument to FusedBatchNormGradV3. +type FusedBatchNormGradV3Attr func(optionalAttr) + +// FusedBatchNormGradV3Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormGradV3Epsilon(value float32) FusedBatchNormGradV3Attr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormGradV3DataFormat sets the optional data_format attribute to value. +// +// value: The data format for y_backprop, x, x_backprop. +// Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormGradV3DataFormat(value string) FusedBatchNormGradV3Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormGradV3IsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormGradV3IsTraining(value bool) FusedBatchNormGradV3Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Gradient for batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// y_backprop: A 4D Tensor for the gradient with respect to y. +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +// mean to be reused in gradient computation. When is_training is +// False, a 1D Tensor for the population mean to be reused in both +// 1st and 2nd order gradient computation. +// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch +// variance (inverted variance in the cuDNN case) to be reused in +// gradient computation. When is_training is False, a 1D Tensor +// for the population variance to be reused in both 1st and 2nd +// order gradient computation. +// reserve_space_3: When is_training is True, a 1D Tensor for some intermediate results to be reused +// in gradient computation. When is_training is False, a dummy empty Tensor will be +// created. +// +// Returns: +// x_backprop: A 4D Tensor for the gradient with respect to x. +// scale_backprop: A 1D Tensor for the gradient with respect to scale. +// offset_backprop: A 1D Tensor for the gradient with respect to offset. +// reserve_space_4: Unused placeholder to match the mean input in FusedBatchNorm. +// reserve_space_5: Unused placeholder to match the variance input +// in FusedBatchNorm. +func FusedBatchNormGradV3(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, reserve_space_3 tf.Output, optional ...FusedBatchNormGradV3Attr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_4 tf.Output, reserve_space_5 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormGradV3", + Input: []tf.Input{ + y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// AvgPool3DAttr is an optional argument to AvgPool3D. +type AvgPool3DAttr func(optionalAttr) + +// AvgPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func AvgPool3DDataFormat(value string) AvgPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D average pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The average pooled output tensor. +func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the number of records this Reader has produced. +// +// This is the same as the number of ReaderRead executions that have +// succeeded. +// +// Arguments: +// reader_handle: Handle to a Reader. +func ReaderNumRecordsProducedV2(scope *Scope, reader_handle tf.Output) (records_produced tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderNumRecordsProducedV2", + Input: []tf.Input{ + reader_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeRawAttr is an optional argument to DecodeRaw. +type DecodeRawAttr func(optionalAttr) + +// DecodeRawLittleEndian sets the optional little_endian attribute to value. +// +// value: Whether the input `bytes` are in little-endian order. +// Ignored for `out_type` values that are stored in a single byte like +// `uint8`. +// If not specified, defaults to true +func DecodeRawLittleEndian(value bool) DecodeRawAttr { + return func(m optionalAttr) { + m["little_endian"] = value + } +} + +// Reinterpret the bytes of a string as a vector of numbers. +// +// Arguments: +// bytes: All the elements must have the same length. +// +// +// Returns A Tensor with one more dimension than the input `bytes`. The +// added dimension will have size equal to the length of the elements +// of `bytes` divided by the number of bytes to represent `out_type`. +func DecodeRaw(scope *Scope, bytes tf.Output, out_type tf.DataType, optional ...DecodeRawAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeRaw", + Input: []tf.Input{ + bytes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gather ragged slices from `params` axis `0` according to `indices`. +// +// Outputs a `RaggedTensor` output composed from `output_dense_values` and +// `output_nested_splits`, such that: +// +// ```python +// output.shape = indices.shape + params.shape[1:] +// output.ragged_rank = indices.shape.ndims + params.ragged_rank +// output[i...j, d0...dn] = params[indices[i...j], d0...dn] +// ``` +// +// where +// +// * `params = +// ragged.from_nested_row_splits(params_dense_values, params_nested_splits)` +// provides the values that should be gathered. +// * `indices` ia a dense tensor with dtype `int32` or `int64`, indicating which +// values should be gathered. +// * `output = +// ragged.from_nested_row_splits(output_dense_values, output_nested_splits)` +// is the output tensor. +// +// (Note: This c++ op is used to implement the higher-level python +// `tf.ragged.gather` op, which also supports ragged indices.) +// +// +// Arguments: +// params_nested_splits: The `nested_row_splits` tensors that define the row-partitioning for the +// `params` RaggedTensor input. +// params_dense_values: The `flat_values` for the `params` RaggedTensor. There was a terminology change +// at the python level from dense_values to flat_values, so dense_values is the +// deprecated name. +// indices: Indices in the outermost dimension of `params` of the values that should be +// gathered. +// OUTPUT_RAGGED_RANK: The ragged rank of the output RaggedTensor. `output_nested_splits` will contain +// this number of `row_splits` tensors. This value should equal +// `indices.shape.ndims + params.ragged_rank - 1`. +// +// Returns: +// output_nested_splits: The `nested_row_splits` tensors that define the row-partitioning for the +// returned RaggedTensor. +// output_dense_values: The `flat_values` for the returned RaggedTensor. +func RaggedGather(scope *Scope, params_nested_splits []tf.Output, params_dense_values tf.Output, indices tf.Output, OUTPUT_RAGGED_RANK int64) (output_nested_splits []tf.Output, output_dense_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"OUTPUT_RAGGED_RANK": OUTPUT_RAGGED_RANK} + opspec := tf.OpSpec{ + Type: "RaggedGather", + Input: []tf.Input{ + tf.OutputList(params_nested_splits), params_dense_values, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output_nested_splits, idx, err = makeOutputList(op, idx, "output_nested_splits"); err != nil { + scope.UpdateErr("RaggedGather", err) + return + } + output_dense_values = op.Output(idx) + return output_nested_splits, output_dense_values +} + +// QuantizeV2Attr is an optional argument to QuantizeV2. +type QuantizeV2Attr func(optionalAttr) + +// QuantizeV2Mode sets the optional mode attribute to value. +// If not specified, defaults to "MIN_COMBINED" +func QuantizeV2Mode(value string) QuantizeV2Attr { + return func(m optionalAttr) { + m["mode"] = value + } +} + +// QuantizeV2RoundMode sets the optional round_mode attribute to value. +// If not specified, defaults to "HALF_AWAY_FROM_ZERO" +func QuantizeV2RoundMode(value string) QuantizeV2Attr { + return func(m optionalAttr) { + m["round_mode"] = value + } +} + +// QuantizeV2NarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func QuantizeV2NarrowRange(value bool) QuantizeV2Attr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// QuantizeV2Axis sets the optional axis attribute to value. +// If not specified, defaults to -1 +func QuantizeV2Axis(value int64) QuantizeV2Attr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// QuantizeV2EnsureMinimumRange sets the optional ensure_minimum_range attribute to value. +// If not specified, defaults to 0.01 +func QuantizeV2EnsureMinimumRange(value float32) QuantizeV2Attr { + return func(m optionalAttr) { + m["ensure_minimum_range"] = value + } +} + +// Quantize the 'input' tensor of type float to 'output' tensor of type 'T'. +// +// [min_range, max_range] are scalar floats that specify the range for +// the 'input' data. The 'mode' attribute controls exactly which calculations are +// used to convert the float values to their quantized equivalents. The +// 'round_mode' attribute controls which rounding tie-breaking algorithm is used +// when rounding float values to their quantized equivalents. +// +// In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: +// +// ``` +// out[i] = (in[i] - min_range) * range(T) / (max_range - min_range) +// if T == qint8: out[i] -= (range(T) + 1) / 2.0 +// ``` +// +// here `range(T) = numeric_limits::max() - numeric_limits::min()` +// +// *MIN_COMBINED Mode Example* +// +// Assume the input is type float and has a possible range of [0.0, 6.0] and the +// output type is quint8 ([0, 255]). The min_range and max_range values should be +// specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each +// value of the input by 255/6 and cast to quint8. +// +// If the output type was qint8 ([-128, 127]), the operation will additionally +// subtract each value by 128 prior to casting, so that the range of values aligns +// with the range of qint8. +// +// If the mode is 'MIN_FIRST', then this approach is used: +// +// ``` +// num_discrete_values = 1 << (# of bits in T) +// range_adjust = num_discrete_values / (num_discrete_values - 1) +// range = (range_max - range_min) * range_adjust +// range_scale = num_discrete_values / range +// quantized = round(input * range_scale) - round(range_min * range_scale) + +// numeric_limits::min() +// quantized = max(quantized, numeric_limits::min()) +// quantized = min(quantized, numeric_limits::max()) +// ``` +// +// The biggest difference between this and MIN_COMBINED is that the minimum range +// is rounded first, before it's subtracted from the rounded value. With +// MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing +// and dequantizing will introduce a larger and larger error. +// +// *SCALED mode Example* +// +// `SCALED` mode matches the quantization approach used in +// `QuantizeAndDequantize{V2|V3}`. +// +// If the mode is `SCALED`, the quantization is performed by multiplying each +// input value by a scaling_factor. +// The scaling_factor is determined from `min_range` and `max_range` to be as large +// as possible such that the range from `min_range` to `max_range` is representable +// within values of type T. +// +// ```c++ +// +// const int min_T = std::numeric_limits::min(); +// const int max_T = std::numeric_limits::max(); +// const float max_float = std::numeric_limits::max(); +// +// const float scale_factor_from_min_side = +// (min_T * min_range > 0) ? min_T / min_range : max_float; +// const float scale_factor_from_max_side = +// (max_T * max_range > 0) ? max_T / max_range : max_float; +// +// const float scale_factor = std::min(scale_factor_from_min_side, +// scale_factor_from_max_side); +// ``` +// +// We next use the scale_factor to adjust min_range and max_range as follows: +// +// ```c++ +// min_range = min_T / scale_factor; +// max_range = max_T / scale_factor; +// ``` +// +// +// e.g. if T = qint8, and initially min_range = -10, and max_range = 9, we would +// compare -128/-10.0 = 12.8 to 127/9.0 = 14.11, and set scaling_factor = 12.8 +// In this case, min_range would remain -10, but max_range would be adjusted to +// 127 / 12.8 = 9.921875 +// +// So we will quantize input values in the range (-10, 9.921875) to (-128, 127). +// +// The input tensor can now be quantized by clipping values to the range +// `min_range` to `max_range`, then multiplying by scale_factor as follows: +// +// ```c++ +// result = round(min(max_range, max(min_range, input)) * scale_factor) +// ``` +// +// The adjusted `min_range` and `max_range` are returned as outputs 2 and 3 of +// this operation. These outputs should be used as the range for any further +// calculations. +// +// +// *narrow_range (bool) attribute* +// +// If true, we do not use the minimum quantized value. +// i.e. for int8 the quantized output, it would be restricted to the range +// -127..127 instead of the full -128..127 range. +// This is provided for compatibility with certain inference backends. +// (Only applies to SCALED mode) +// +// +// *axis (int) attribute* +// +// An optional `axis` attribute can specify a dimension index of the input tensor, +// such that quantization ranges will be calculated and applied separately for each +// slice of the tensor along that dimension. This is useful for per-channel +// quantization. +// +// If axis is specified, min_range and max_range +// +// if `axis`=None, per-tensor quantization is performed as normal. +// +// +// *ensure_minimum_range (float) attribute* +// +// Ensures the minimum quantization range is at least this value. +// The legacy default value for this is 0.01, but it is strongly suggested to +// set it to 0 for new uses. +// +// +// Arguments: +// +// min_range: The minimum value of the quantization range. This value may be adjusted by the +// op depending on other parameters. The adjusted value is written to `output_min`. +// If the `axis` attribute is specified, this must be a 1-D tensor whose size +// matches the `axis` dimension of the input and output tensors. +// max_range: The maximum value of the quantization range. This value may be adjusted by the +// op depending on other parameters. The adjusted value is written to `output_max`. +// If the `axis` attribute is specified, this must be a 1-D tensor whose size +// matches the `axis` dimension of the input and output tensors. +// +// +// Returns: +// output: The quantized data produced from the float input. +// output_min: The final quantization range minimum, used to clip input values before scaling +// and rounding them to quantized values. +// If the `axis` attribute is specified, this will be a 1-D tensor whose size +// matches the `axis` dimension of the input and output tensors. +// output_max: The final quantization range maximum, used to clip input values before scaling +// and rounding them to quantized values. +// If the `axis` attribute is specified, this will be a 1-D tensor whose size +// matches the `axis` dimension of the input and output tensors. +func QuantizeV2(scope *Scope, input tf.Output, min_range tf.Output, max_range tf.Output, T tf.DataType, optional ...QuantizeV2Attr) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizeV2", + Input: []tf.Input{ + input, min_range, max_range, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the truth value of (x >= y) element-wise. +// +// *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// Example: +// +// ```python +// x = tf.constant([5, 4, 6, 7]) +// y = tf.constant([5, 2, 5, 10]) +// tf.math.greater_equal(x, y) ==> [True, True, True, False] +// +// x = tf.constant([5, 4, 6, 7]) +// y = tf.constant([5]) +// tf.math.greater_equal(x, y) ==> [True, False, True, True] +// ``` +func GreaterEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GreaterEqual", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BatchAttr is an optional argument to Batch. +type BatchAttr func(optionalAttr) + +// BatchMaxEnqueuedBatches sets the optional max_enqueued_batches attribute to value. +// If not specified, defaults to 10 +func BatchMaxEnqueuedBatches(value int64) BatchAttr { + return func(m optionalAttr) { + m["max_enqueued_batches"] = value + } +} + +// BatchAllowedBatchSizes sets the optional allowed_batch_sizes attribute to value. +// If not specified, defaults to <> +func BatchAllowedBatchSizes(value []int64) BatchAttr { + return func(m optionalAttr) { + m["allowed_batch_sizes"] = value + } +} + +// BatchContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func BatchContainer(value string) BatchAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// BatchSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func BatchSharedName(value string) BatchAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// BatchBatchingQueue sets the optional batching_queue attribute to value. +// If not specified, defaults to "" +func BatchBatchingQueue(value string) BatchAttr { + return func(m optionalAttr) { + m["batching_queue"] = value + } +} + +// Batches all input tensors nondeterministically. +// +// When many instances of this Op are being run concurrently with the same +// container/shared_name in the same device, some will output zero-shaped Tensors +// and others will output Tensors of size up to max_batch_size. +// +// All Tensors in in_tensors are batched together (so, for example, labels and +// features should be batched with a single instance of this operation. +// +// Each invocation of batch emits an `id` scalar which will be used to identify +// this particular invocation when doing unbatch or its gradient. +// +// Each op which emits a non-empty batch will also emit a non-empty batch_index +// Tensor, which, is a [K, 3] matrix where each row contains the invocation's id, +// start, and length of elements of each set of Tensors present in batched_tensors. +// +// Batched tensors are concatenated along the first dimension, and all tensors in +// in_tensors must have the first dimension of the same size. +// +// in_tensors: The tensors to be batched. +// num_batch_threads: Number of scheduling threads for processing batches of work. +// Determines the number of batches processed in parallel. +// max_batch_size: Batch sizes will never be bigger than this. +// batch_timeout_micros: Maximum number of microseconds to wait before outputting +// an incomplete batch. +// allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does +// nothing. Otherwise, supplies a list of batch sizes, causing the op to pad +// batches up to one of those sizes. The entries must increase monotonically, and +// the final entry must equal max_batch_size. +// grad_timeout_micros: The timeout to use for the gradient. See Unbatch. +// batched_tensors: Either empty tensors or a batch of concatenated Tensors. +// batch_index: If out_tensors is non-empty, has information to invert it. +// container: Controls the scope of sharing of this batch. +// id: always contains a scalar with a unique ID for this invocation of Batch. +// shared_name: Concurrently running instances of batch in the same device with the +// same container and shared_name will batch their elements together. If left +// empty, the op name will be used as the shared name. +// T: the types of tensors to be batched. +func Batch(scope *Scope, in_tensors []tf.Output, num_batch_threads int64, max_batch_size int64, batch_timeout_micros int64, grad_timeout_micros int64, optional ...BatchAttr) (batched_tensors []tf.Output, batch_index tf.Output, id tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_batch_threads": num_batch_threads, "max_batch_size": max_batch_size, "batch_timeout_micros": batch_timeout_micros, "grad_timeout_micros": grad_timeout_micros} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Batch", + Input: []tf.Input{ + tf.OutputList(in_tensors), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if batched_tensors, idx, err = makeOutputList(op, idx, "batched_tensors"); err != nil { + scope.UpdateErr("Batch", err) + return + } + batch_index = op.Output(idx) + id = op.Output(idx) + return batched_tensors, batch_index, id +} + +// UnicodeDecodeAttr is an optional argument to UnicodeDecode. +type UnicodeDecodeAttr func(optionalAttr) + +// UnicodeDecodeErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeDecodeErrors(value string) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeDecodeReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD or U+65533.) +// If not specified, defaults to 65533 +func UnicodeDecodeReplacementChar(value int64) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// UnicodeDecodeReplaceControlCharacters sets the optional replace_control_characters attribute to value. +// +// value: Whether to replace the C0 control characters (00-1F) with the +// `replacement_char`. Default is false. +// If not specified, defaults to false +func UnicodeDecodeReplaceControlCharacters(value bool) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["replace_control_characters"] = value + } +} + +// UnicodeDecodeTsplits sets the optional Tsplits attribute to value. +// If not specified, defaults to DT_INT64 +func UnicodeDecodeTsplits(value tf.DataType) UnicodeDecodeAttr { + return func(m optionalAttr) { + m["Tsplits"] = value + } +} + +// Decodes each string in `input` into a sequence of Unicode code points. +// +// The character codepoints for all strings are returned using a single vector +// `char_values`, with strings expanded to characters in row-major order. +// +// The `row_splits` tensor indicates where the codepoints for +// each input string begin and end within the `char_values` tensor. +// In particular, the values for the `i`th +// string (in row-major order) are stored in the slice +// `[row_splits[i]:row_splits[i+1]]`. Thus: +// +// * `char_values[row_splits[i]+j]` is the Unicode codepoint for the `j`th +// character in the `i`th string (in row-major order). +// * `row_splits[i+1] - row_splits[i]` is the number of characters in the `i`th +// string (in row-major order). +// +// Arguments: +// input: The text to be decoded. Can have any shape. Note that the output is flattened +// to a vector of char values. +// input_encoding: Text encoding of the input strings. This is any of the encodings supported +// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. +// +// Returns: +// row_splits: A 1D int32 tensor containing the row splits. +// char_values: A 1D int32 Tensor containing the decoded codepoints. +func UnicodeDecode(scope *Scope, input tf.Output, input_encoding string, optional ...UnicodeDecodeAttr) (row_splits tf.Output, char_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_encoding": input_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeDecode", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Create a dense tensor from a ragged tensor, possibly altering its shape. +// +// The `ragged_to_dense` op creates a dense tensor from a list of row partition +// tensors, a value vector, and default values. If the shape is unspecified, the +// minimal shape required to contain all the elements in the ragged tensor (the +// natural shape) will be used. If some dimensions are left unspecified, then the +// size of the natural shape is used in that dimension. +// +// The default_value will be broadcast to the output shape. After that, the values +// from the ragged tensor overwrite the default values. Note that the default_value +// must have less dimensions than the value. +// +// The row partition tensors are in the order of the dimensions. +// At present, the types can be: +// * "ROW_SPLITS": the row_splits tensor from the ragged tensor. +// * "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor. +// * "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it +// is preceded by "FIRST_DIM_SIZE". +// +// Arguments: +// shape: The desired shape of the the output tensor. If left unspecified (empty), +// the minimal shape required to contain all the elements in the ragged tensor +// (the natural shape) will be used. If some dimensions are left unspecified, then +// the size of the natural shape is used in that dimension. +// +// Note that dense dimensions cannot be modified by the shape argument. Trying to +// change the size of a dense dimension will cause the op to fail. +// Examples: +// natural shape: [4, 5, 6] +// shape: -1 +// output shape: [4, 5, 6] +// +// natural shape: [4, 5, 6] +// shape: [3, -1, 2] +// output shape: [3, 5, 2] +// +// natural shape: [4, 5, 6] +// shape: [3, 7, 2] +// output shape: [3, 7, 2] +// +// values: A 1D tensor representing the values of the ragged tensor. +// default_value: The default_value when the shape is larger than the ragged tensor. The +// default_value is broadcast until it is the shape of the output tensor, and +// then overwritten by values in the ragged tensor. The default value must be +// compatible with this broadcast operation, and must have fewer dimensions than +// the value tensor. +// +// row_partition_types: The types of the row partition tensors. At present, these can be: +// * "ROW_SPLITS": the row_splits tensor from the ragged tensor. +// * "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor. +// * "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it +// is preceeded by "FIRST_DIM_SIZE". +// The tensors are in the order of the dimensions. +// +// Returns The resulting dense tensor. +func RaggedTensorToTensor(scope *Scope, shape tf.Output, values tf.Output, default_value tf.Output, row_partition_tensors []tf.Output, row_partition_types []string) (result tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"row_partition_types": row_partition_types} + opspec := tf.OpSpec{ + Type: "RaggedTensorToTensor", + Input: []tf.Input{ + shape, values, default_value, tf.OutputList(row_partition_tensors), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BatchMatMulAttr is an optional argument to BatchMatMul. +type BatchMatMulAttr func(optionalAttr) + +// BatchMatMulAdjX sets the optional adj_x attribute to value. +// +// value: If `True`, adjoint the slices of `x`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulAdjX(value bool) BatchMatMulAttr { + return func(m optionalAttr) { + m["adj_x"] = value + } +} + +// BatchMatMulAdjY sets the optional adj_y attribute to value. +// +// value: If `True`, adjoint the slices of `y`. Defaults to `False`. +// If not specified, defaults to false +func BatchMatMulAdjY(value bool) BatchMatMulAttr { + return func(m optionalAttr) { + m["adj_y"] = value + } +} + +// Multiplies slices of two tensors in batches. +// +// Multiplies all slices of `Tensor` `x` and `y` (each slice can be +// viewed as an element of a batch), and arranges the individual results +// in a single output tensor of the same batch size. Each of the +// individual slices can optionally be adjointed (to adjoint a matrix +// means to transpose and conjugate it) before multiplication by setting +// the `adj_x` or `adj_y` flag to `True`, which are by default `False`. +// +// The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` +// and `[..., r_y, c_y]`. +// +// The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: +// +// r_o = c_x if adj_x else r_x +// c_o = r_y if adj_y else c_y +// +// It is computed as: +// +// output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) +// +// Arguments: +// x: 2-D or higher with shape `[..., r_x, c_x]`. +// y: 2-D or higher with shape `[..., r_y, c_y]`. +// +// Returns 3-D or higher with shape `[..., r_o, c_o]` +func BatchMatMul(scope *Scope, x tf.Output, y tf.Output, optional ...BatchMatMulAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BatchMatMul", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RaggedTensorFromVariantAttr is an optional argument to RaggedTensorFromVariant. +type RaggedTensorFromVariantAttr func(optionalAttr) + +// RaggedTensorFromVariantTsplits sets the optional Tsplits attribute to value. +// If not specified, defaults to DT_INT64 +func RaggedTensorFromVariantTsplits(value tf.DataType) RaggedTensorFromVariantAttr { + return func(m optionalAttr) { + m["Tsplits"] = value + } +} + +// Decodes a `variant` Tensor into a `RaggedTensor`. +// +// Decodes the given `variant` Tensor and returns a `RaggedTensor`. The input +// could be a scalar, meaning it encodes a single `RaggedTensor` with ragged_rank +// `output_ragged_rank`. It could also have an arbitrary rank, in which case each +// element is decoded into a `RaggedTensor` with ragged_rank `input_ragged_rank` +// and these are then stacked according to the input shape to output a single +// `RaggedTensor` with ragged_rank `output_ragged_rank`. Each `variant` element in +// the input Tensor is decoded by retrieving from the element a 1-D `variant` +// Tensor with `input_ragged_rank + 1` Tensors, corresponding to the splits and +// values of the decoded `RaggedTensor`. If `input_ragged_rank` is -1, then it is +// inferred as `output_ragged_rank` - `rank(encoded_ragged)`. See +// `RaggedTensorToVariant` for the corresponding encoding logic. +// +// +// Arguments: +// encoded_ragged: A `variant` Tensor containing encoded `RaggedTensor`s. +// input_ragged_rank: The ragged rank of each encoded `RaggedTensor` component in the input. If set to +// -1, this is inferred as `output_ragged_rank` - `rank(encoded_ragged)` +// output_ragged_rank: The expected ragged rank of the output `RaggedTensor`. The following must hold: +// `output_ragged_rank = rank(encoded_ragged) + input_ragged_rank`. +// +// +// Returns: +// output_nested_splits: A list of one or more Tensors representing the splits of the output +// `RaggedTensor`. +// output_dense_values: A Tensor representing the values of the output `RaggedTensor`. +func RaggedTensorFromVariant(scope *Scope, encoded_ragged tf.Output, input_ragged_rank int64, output_ragged_rank int64, Tvalues tf.DataType, optional ...RaggedTensorFromVariantAttr) (output_nested_splits []tf.Output, output_dense_values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_ragged_rank": input_ragged_rank, "output_ragged_rank": output_ragged_rank, "Tvalues": Tvalues} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RaggedTensorFromVariant", + Input: []tf.Input{ + encoded_ragged, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output_nested_splits, idx, err = makeOutputList(op, idx, "output_nested_splits"); err != nil { + scope.UpdateErr("RaggedTensorFromVariant", err) + return + } + output_dense_values = op.Output(idx) + return output_nested_splits, output_dense_values +} + +// RandomPoissonV2Attr is an optional argument to RandomPoissonV2. +type RandomPoissonV2Attr func(optionalAttr) + +// RandomPoissonV2Seed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomPoissonV2Seed(value int64) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomPoissonV2Seed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomPoissonV2Seed2(value int64) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// RandomPoissonV2Dtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_INT64 +func RandomPoissonV2Dtype(value tf.DataType) RandomPoissonV2Attr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from the Poisson distribution(s) described by rate. +// +// This op uses two algorithms, depending on rate. If rate >= 10, then +// the algorithm by Hormann is used to acquire samples via +// transformation-rejection. +// See http://www.sciencedirect.com/science/article/pii/0167668793909974. +// +// Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform +// random variables. +// See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer +// Programming, Volume 2. Addison Wesley +// +// Arguments: +// shape: 1-D integer tensor. Shape of independent samples to draw from each +// distribution described by the shape parameters given in rate. +// rate: A tensor in which each scalar is a "rate" parameter describing the +// associated poisson distribution. +// +// Returns A tensor with shape `shape + shape(rate)`. Each slice +// `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for +// `rate[i0, i1, ...iN]`. +func RandomPoissonV2(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomPoissonV2", + Input: []tf.Input{ + shape, rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that takes a Bernoulli sample of the contents of another dataset. +// +// There is no transformation in the `tf.data` Python API for creating this dataset. +// Instead, it is created as a result of the `filter_with_random_uniform_fusion` +// static optimization. Whether this optimization is performed is determined by the +// `experimental_optimization.filter_with_random_uniform_fusion` option of +// `tf.data.Options`. +// +// Arguments: +// +// rate: A scalar representing the sample rate. Each element of `input_dataset` is +// retained with this probability, independent of all other elements. +// seed: A scalar representing seed of random number generator. +// seed2: A scalar representing seed2 of random number generator. +// +// +func SamplingDataset(scope *Scope, input_dataset tf.Output, rate tf.Output, seed tf.Output, seed2 tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "SamplingDataset", + Input: []tf.Input{ + input_dataset, rate, seed, seed2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reads and outputs the entire contents of the input filename. +func ReadFile(scope *Scope, filename tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReadFile", + Input: []tf.Input{ + filename, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes requantization range per channel. +// +// Arguments: +// input: The original input tensor. +// input_min: The minimum value of the input tensor +// input_max: The maximum value of the input tensor. +// clip_value_max: The maximum value of the output that needs to be clipped. +// Example: set this to 6 for Relu6. +// +// Returns: +// output_min: The minimum value of the final output tensor +// output_max: The maximum value of the final output tensor. +func RequantizationRangePerChannel(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, clip_value_max float32) (output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"clip_value_max": clip_value_max} + opspec := tf.OpSpec{ + Type: "RequantizationRangePerChannel", + Input: []tf.Input{ + input, input_min, input_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// TruncatedNormalAttr is an optional argument to TruncatedNormal. +type TruncatedNormalAttr func(optionalAttr) + +// TruncatedNormalSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func TruncatedNormalSeed(value int64) TruncatedNormalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// TruncatedNormalSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func TruncatedNormalSeed2(value int64) TruncatedNormalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a truncated normal distribution. +// +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. +// +// Arguments: +// shape: The shape of the output tensor. +// dtype: The type of the output. +// +// Returns A tensor of the specified shape filled with random truncated normal +// values. +func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...TruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TruncatedNormal", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParameterizedTruncatedNormalAttr is an optional argument to ParameterizedTruncatedNormal. +type ParameterizedTruncatedNormalAttr func(optionalAttr) + +// ParameterizedTruncatedNormalSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ParameterizedTruncatedNormalSeed(value int64) ParameterizedTruncatedNormalAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// ParameterizedTruncatedNormalSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func ParameterizedTruncatedNormalSeed2(value int64) ParameterizedTruncatedNormalAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a normal distribution. The parameters may each be a +// +// scalar which applies to the entire output, or a vector of length shape[0] which +// stores the parameters for each batch. +// +// Arguments: +// shape: The shape of the output tensor. Batches are indexed by the 0th dimension. +// means: The mean parameter of each batch. +// stdevs: The standard deviation parameter of each batch. Must be greater than 0. +// minvals: The minimum cutoff. May be -infinity. +// maxvals: The maximum cutoff. May be +infinity, and must be more than the minval +// for each batch. +// +// Returns A matrix of shape num_batches x samples_per_batch, filled with random +// truncated normal values using the parameters for each row. +func ParameterizedTruncatedNormal(scope *Scope, shape tf.Output, means tf.Output, stdevs tf.Output, minvals tf.Output, maxvals tf.Output, optional ...ParameterizedTruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParameterizedTruncatedNormal", + Input: []tf.Input{ + shape, means, stdevs, minvals, maxvals, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedMatMulAttr is an optional argument to QuantizedMatMul. +type QuantizedMatMulAttr func(optionalAttr) + +// QuantizedMatMulToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedMatMulToutput(value tf.DataType) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// QuantizedMatMulTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, `a` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulTransposeA(value bool) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// QuantizedMatMulTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, `b` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulTransposeB(value bool) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// QuantizedMatMulTactivation sets the optional Tactivation attribute to value. +// +// value: The type of output produced by activation function +// following this operation. +// If not specified, defaults to DT_QUINT8 +func QuantizedMatMulTactivation(value tf.DataType) QuantizedMatMulAttr { + return func(m optionalAttr) { + m["Tactivation"] = value + } +} + +// Perform a quantized matrix multiplication of `a` by the matrix `b`. +// +// The inputs must be two-dimensional matrices and the inner dimension of +// `a` (after being transposed if `transpose_a` is non-zero) must match the +// outer dimension of `b` (after being transposed if `transposed_b` is +// non-zero). +// +// Arguments: +// a: Must be a two-dimensional tensor. +// b: Must be a two-dimensional tensor. +// min_a: The float value that the lowest quantized `a` value represents. +// max_a: The float value that the highest quantized `a` value represents. +// min_b: The float value that the lowest quantized `b` value represents. +// max_b: The float value that the highest quantized `b` value represents. +// +// Returns: +// out +// min_out: The float value that the lowest quantized output value represents. +// max_out: The float value that the highest quantized output value represents. +func QuantizedMatMul(scope *Scope, a tf.Output, b tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMatMul", + Input: []tf.Input{ + a, b, min_a, max_a, min_b, max_b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Extract `patches` from `images` and put them in the "depth" output dimension. +// +// Arguments: +// images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`. +// ksizes: The size of the sliding window for each dimension of `images`. +// strides: How far the centers of two consecutive patches are in +// the images. Must be: `[1, stride_rows, stride_cols, 1]`. +// rates: Must be: `[1, rate_rows, rate_cols, 1]`. This is the +// input stride, specifying how far two consecutive patch samples are in the +// input. Equivalent to extracting patches with +// `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by +// subsampling them spatially by a factor of `rates`. This is equivalent to +// `rate` in dilated (a.k.a. Atrous) convolutions. +// padding: The type of padding algorithm to use. +// +// Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows * +// ksize_cols * depth]` containing image patches with size +// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note +// `out_rows` and `out_cols` are the dimensions of the output patches. +func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "rates": rates, "padding": padding} + opspec := tf.OpSpec{ + Type: "ExtractImagePatches", + Input: []tf.Input{ + images, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Forwards the value of an available tensor from `inputs` to `output`. +// +// `Merge` waits for at least one of the tensors in `inputs` to become available. +// It is usually combined with `Switch` to implement branching. +// +// `Merge` forwards the first tensor to become available to `output`, and sets +// `value_index` to its index in `inputs`. +// +// Arguments: +// inputs: The input tensors, exactly one of which will become available. +// +// Returns: +// output: Will be set to the available input tensor. +// value_index: The index of the chosen input tensor in `inputs`. +func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Merge", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// PaddedBatchDatasetV2Attr is an optional argument to PaddedBatchDatasetV2. +type PaddedBatchDatasetV2Attr func(optionalAttr) + +// PaddedBatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value. +// If not specified, defaults to false +func PaddedBatchDatasetV2ParallelCopy(value bool) PaddedBatchDatasetV2Attr { + return func(m optionalAttr) { + m["parallel_copy"] = value + } +} + +// Creates a dataset that batches and pads `batch_size` elements from the input. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// padded_shapes: A list of int64 tensors representing the desired padded shapes +// of the corresponding output components. These shapes may be partially +// specified, using `-1` to indicate that a particular dimension should be +// padded to the maximum size of all batch elements. +// padding_values: A list of scalars containing the padding value to use for +// each of the outputs. +// drop_remainder: A scalar representing whether the last batch should be dropped in case its size +// is smaller than desired. +// +func PaddedBatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, drop_remainder tf.Output, output_shapes []tf.Shape, optional ...PaddedBatchDatasetV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PaddedBatchDatasetV2", + Input: []tf.Input{ + input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BlockLSTMV2Attr is an optional argument to BlockLSTMV2. +type BlockLSTMV2Attr func(optionalAttr) + +// BlockLSTMV2CellClip sets the optional cell_clip attribute to value. +// +// value: Value to clip the 'cs' value to. +// If not specified, defaults to 0 +func BlockLSTMV2CellClip(value float32) BlockLSTMV2Attr { + return func(m optionalAttr) { + m["cell_clip"] = value + } +} + +// BlockLSTMV2UsePeephole sets the optional use_peephole attribute to value. +// +// value: Whether to use peephole weights. +// If not specified, defaults to false +func BlockLSTMV2UsePeephole(value bool) BlockLSTMV2Attr { + return func(m optionalAttr) { + m["use_peephole"] = value + } +} + +// Computes the LSTM cell forward propagation for all the time steps. +// +// This is equivalent to applying LSTMBlockCell in a loop, like so: +// +// ```python +// for x1 in unpack(x): +// i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock( +// x1, cs_prev, h_prev, w, wci, wcf, wco, b) +// cs_prev = cs1 +// h_prev = h1 +// i.append(i1) +// cs.append(cs1) +// f.append(f1) +// o.append(o1) +// ci.append(ci1) +// co.append(co1) +// h.append(h1) +// return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h) +// +// Note that unlike LSTMBlockCell (and BlockLSTM) which uses ICFO gate layout, +// this op uses IFCO. So in order for the following snippet to be equivalent +// all gate-related outputs should be reordered. +// ``` +// +// Arguments: +// seq_len_max: Maximum time length actually used by this input. Outputs are padded +// with zeros beyond this length. +// x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). +// cs_prev: Value of the initial cell state. +// h_prev: Initial output of cell (to be used for peephole). +// w: The weight matrix. +// wci: The weight matrix for input gate peephole connection. +// wcf: The weight matrix for forget gate peephole connection. +// wco: The weight matrix for output gate peephole connection. +// b: The bias vector. +// +// Returns: +// i: The input gate over the whole time sequence. +// cs: The cell state before the tanh over the whole time sequence. +// f: The forget gate over the whole time sequence. +// o: The output gate over the whole time sequence. +// ci: The cell input over the whole time sequence. +// co: The cell after the tanh over the whole time sequence. +// h: The output h vector over the whole time sequence. +func BlockLSTMV2(scope *Scope, seq_len_max tf.Output, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, optional ...BlockLSTMV2Attr) (i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BlockLSTMV2", + Input: []tf.Input{ + seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// Return a tensor with the same shape and contents as the input tensor or value. +func Identity(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Identity", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs a `Summary` protocol buffer with scalar values. +// +// The input `tags` and `values` must have the same shape. The generated summary +// has a summary value for each tag-value pair in `tags` and `values`. +// +// Arguments: +// tags: Tags for the summary. +// values: Same shape as `tags. Values for the summary. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func ScalarSummary(scope *Scope, tags tf.Output, values tf.Output) (summary tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ScalarSummary", + Input: []tf.Input{ + tags, values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyProximalAdagradAttr is an optional argument to ResourceSparseApplyProximalAdagrad. +type ResourceSparseApplyProximalAdagradAttr func(optionalAttr) + +// ResourceSparseApplyProximalAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyProximalAdagradUseLocking(value bool) ResourceSparseApplyProximalAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. +// +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// prox_v = var +// prox_v -= lr * grad * (1 / sqrt(accum)) +// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyProximalAdagrad", + Input: []tf.Input{ + var_, accum, lr, l1, l2, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes numerical negative value element-wise. +// +// I.e., \\(y = -x\\). +func Neg(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Neg", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates tensors along one dimension. +// +// Arguments: +// values: List of `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// axis: 0-D. The dimension along which to concatenate. Must be in the +// range [-rank(values), rank(values)). +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func ConcatV2(scope *Scope, values []tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConcatV2", + Input: []tf.Input{ + tf.OutputList(values), axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Elementwise computes the bitwise right-shift of `x` and `y`. +// +// Performs a logical shift for unsigned integer types, and an arithmetic shift +// for signed integer types. +// +// If `y` is negative, or greater than or equal to than the width of `x` in bits +// the result is implementation defined. +// +// Example: +// +// ```python +// import tensorflow as tf +// from tensorflow.python.ops import bitwise_ops +// import numpy as np +// dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64] +// +// for dtype in dtype_list: +// lhs = tf.constant([-1, -5, -3, -14], dtype=dtype) +// rhs = tf.constant([5, 0, 7, 11], dtype=dtype) +// +// right_shift_result = bitwise_ops.right_shift(lhs, rhs) +// +// print(right_shift_result) +// +// # This will print: +// # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int8) +// # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int16) +// # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int32) +// # tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int64) +// +// lhs = np.array([-2, 64, 101, 32], dtype=np.int8) +// rhs = np.array([-1, -5, -3, -14], dtype=np.int8) +// bitwise_ops.right_shift(lhs, rhs) +// # +// ``` +// +func RightShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RightShift", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// IteratorFromStringHandleAttr is an optional argument to IteratorFromStringHandle. +type IteratorFromStringHandleAttr func(optionalAttr) + +// IteratorFromStringHandleOutputTypes sets the optional output_types attribute to value. +// +// value: If specified, defines the type of each tuple component in an +// element produced by the resulting iterator. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func IteratorFromStringHandleOutputTypes(value []tf.DataType) IteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_types"] = value + } +} + +// IteratorFromStringHandleOutputShapes sets the optional output_shapes attribute to value. +// +// value: If specified, defines the shape of each tuple component in an +// element produced by the resulting iterator. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func IteratorFromStringHandleOutputShapes(value []tf.Shape) IteratorFromStringHandleAttr { + return func(m optionalAttr) { + m["output_shapes"] = value + } +} + +// Converts the given string representing a handle to an iterator to a resource. +// +// Arguments: +// string_handle: A string representation of the given handle. +// +// Returns A handle to an iterator resource. +func IteratorFromStringHandle(scope *Scope, string_handle tf.Output, optional ...IteratorFromStringHandleAttr) (resource_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IteratorFromStringHandle", + Input: []tf.Input{ + string_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Performs gradient updates of embedding tables. +// +// Arguments: +// inputs: A TensorList of gradients with which to update embedding tables. +// This argument has the same length and shapes as the return value of +// RecvTPUEmbeddingActivations, but contains gradients of the model's loss +// with respect to the embedding activations. The embedding tables are updated +// from these gradients via the optimizer specified in the TPU embedding +// configuration given to tpu.initialize_system. +// learning_rates: A TensorList of float32 scalars, one for each dynamic learning +// rate tag: see the comments in +// //third_party/tensorflow/core/protobuf/tpu/optimization_parameters.proto. +// Multiple tables can share the same dynamic learning rate tag as specified +// in the configuration. If the learning rates for all tables are constant, +// this list should be empty. +// config: Serialized TPUEmbeddingConfiguration proto. +// +// Returns the created operation. +func SendTPUEmbeddingGradients(scope *Scope, inputs []tf.Output, learning_rates []tf.Output, config string) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"config": config} + opspec := tf.OpSpec{ + Type: "SendTPUEmbeddingGradients", + Input: []tf.Input{ + tf.OutputList(inputs), tf.OutputList(learning_rates), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// CumsumAttr is an optional argument to Cumsum. +type CumsumAttr func(optionalAttr) + +// CumsumExclusive sets the optional exclusive attribute to value. +// +// value: If `True`, perform exclusive cumsum. +// If not specified, defaults to false +func CumsumExclusive(value bool) CumsumAttr { + return func(m optionalAttr) { + m["exclusive"] = value + } +} + +// CumsumReverse sets the optional reverse attribute to value. +// +// value: A `bool` (default: False). +// If not specified, defaults to false +func CumsumReverse(value bool) CumsumAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Compute the cumulative sum of the tensor `x` along `axis`. +// +// By default, this op performs an inclusive cumsum, which means that the first +// element of the input is identical to the first element of the output: +// +// ```python +// tf.cumsum([a, b, c]) # => [a, a + b, a + b + c] +// ``` +// +// By setting the `exclusive` kwarg to `True`, an exclusive cumsum is +// performed instead: +// +// ```python +// tf.cumsum([a, b, c], exclusive=True) # => [0, a, a + b] +// ``` +// +// By setting the `reverse` kwarg to `True`, the cumsum is performed in the +// opposite direction: +// +// ```python +// tf.cumsum([a, b, c], reverse=True) # => [a + b + c, b + c, c] +// ``` +// +// This is more efficient than using separate `tf.reverse` ops. +// +// The `reverse` and `exclusive` kwargs can also be combined: +// +// ```python +// tf.cumsum([a, b, c], exclusive=True, reverse=True) # => [b + c, c, 0] +// ``` +// +// Arguments: +// x: A `Tensor`. Must be one of the following types: `float32`, `float64`, +// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, +// `complex128`, `qint8`, `quint8`, `qint32`, `half`. +// axis: A `Tensor` of type `int32` (default: 0). Must be in the range +// `[-rank(x), rank(x))`. +func Cumsum(scope *Scope, x tf.Output, axis tf.Output, optional ...CumsumAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Cumsum", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Calculates the gradient of the SparseMatrixSoftmax op. +// +// Arguments: +// softmax: A CSRSparseMatrix. +// grad_softmax: The gradient of `softmax`. +// +// +// Returns The output gradient. +func SparseMatrixSoftmaxGrad(scope *Scope, softmax tf.Output, grad_softmax tf.Output, type_ tf.DataType) (gradient tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "SparseMatrixSoftmaxGrad", + Input: []tf.Input{ + softmax, grad_softmax, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the GRU cell back-propagation for 1 time step. +// +// Args +// x: Input to the GRU cell. +// h_prev: State input from the previous GRU cell. +// w_ru: Weight matrix for the reset and update gate. +// w_c: Weight matrix for the cell connection gate. +// b_ru: Bias vector for the reset and update gate. +// b_c: Bias vector for the cell connection gate. +// r: Output of the reset gate. +// u: Output of the update gate. +// c: Output of the cell connection gate. +// d_h: Gradients of the h_new wrt to objective function. +// +// Returns +// d_x: Gradients of the x wrt to objective function. +// d_h_prev: Gradients of the h wrt to objective function. +// d_c_bar Gradients of the c_bar wrt to objective function. +// d_r_bar_u_bar Gradients of the r_bar & u_bar wrt to objective function. +// +// This kernel op implements the following mathematical equations: +// +// Note on notation of the variables: +// +// Concatenation of a and b is represented by a_b +// Element-wise dot product of a and b is represented by ab +// Element-wise dot product is represented by \circ +// Matrix multiplication is represented by * +// +// Additional notes for clarity: +// +// `w_ru` can be segmented into 4 different matrices. +// ``` +// w_ru = [w_r_x w_u_x +// w_r_h_prev w_u_h_prev] +// ``` +// Similarly, `w_c` can be segmented into 2 different matrices. +// ``` +// w_c = [w_c_x w_c_h_prevr] +// ``` +// Same goes for biases. +// ``` +// b_ru = [b_ru_x b_ru_h] +// b_c = [b_c_x b_c_h] +// ``` +// Another note on notation: +// ``` +// d_x = d_x_component_1 + d_x_component_2 +// +// where d_x_component_1 = d_r_bar * w_r_x^T + d_u_bar * w_r_x^T +// and d_x_component_2 = d_c_bar * w_c_x^T +// +// d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + d_h \circ u +// where d_h_prev_componenet_1 = d_r_bar * w_r_h_prev^T + d_u_bar * w_r_h_prev^T +// ``` +// +// Mathematics behind the Gradients below: +// ``` +// d_c_bar = d_h \circ (1-u) \circ (1-c \circ c) +// d_u_bar = d_h \circ (h-c) \circ u \circ (1-u) +// +// d_r_bar_u_bar = [d_r_bar d_u_bar] +// +// [d_x_component_1 d_h_prev_component_1] = d_r_bar_u_bar * w_ru^T +// +// [d_x_component_2 d_h_prevr] = d_c_bar * w_c^T +// +// d_x = d_x_component_1 + d_x_component_2 +// +// d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + u +// ``` +// Below calculation is performed in the python wrapper for the Gradients +// (not in the gradient kernel.) +// ``` +// d_w_ru = x_h_prevr^T * d_c_bar +// +// d_w_c = x_h_prev^T * d_r_bar_u_bar +// +// d_b_ru = sum of d_r_bar_u_bar along axis = 0 +// +// d_b_c = sum of d_c_bar along axis = 0 +// ``` +func GRUBlockCellGrad(scope *Scope, x tf.Output, h_prev tf.Output, w_ru tf.Output, w_c tf.Output, b_ru tf.Output, b_c tf.Output, r tf.Output, u tf.Output, c tf.Output, d_h tf.Output) (d_x tf.Output, d_h_prev tf.Output, d_c_bar tf.Output, d_r_bar_u_bar tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GRUBlockCellGrad", + Input: []tf.Input{ + x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// TextLineReaderV2Attr is an optional argument to TextLineReaderV2. +type TextLineReaderV2Attr func(optionalAttr) + +// TextLineReaderV2SkipHeaderLines sets the optional skip_header_lines attribute to value. +// +// value: Number of lines to skip from the beginning of every file. +// If not specified, defaults to 0 +func TextLineReaderV2SkipHeaderLines(value int64) TextLineReaderV2Attr { + return func(m optionalAttr) { + m["skip_header_lines"] = value + } +} + +// TextLineReaderV2Container sets the optional container attribute to value. +// +// value: If non-empty, this reader is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func TextLineReaderV2Container(value string) TextLineReaderV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// TextLineReaderV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this reader is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func TextLineReaderV2SharedName(value string) TextLineReaderV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A Reader that outputs the lines of a file delimited by '\n'. +// +// Returns The handle to reference the Reader. +func TextLineReaderV2(scope *Scope, optional ...TextLineReaderV2Attr) (reader_handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TextLineReaderV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Encode audio data using the WAV file format. +// +// This operation will generate a string suitable to be saved out to create a .wav +// audio file. It will be encoded in the 16-bit PCM format. It takes in float +// values in the range -1.0f to 1.0f, and any outside that value will be clamped to +// that range. +// +// `audio` is a 2-D float Tensor of shape `[length, channels]`. +// `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). +// +// Arguments: +// audio: 2-D with shape `[length, channels]`. +// sample_rate: Scalar containing the sample frequency. +// +// Returns 0-D. WAV-encoded file contents. +func EncodeWav(scope *Scope, audio tf.Output, sample_rate tf.Output) (contents tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "EncodeWav", + Input: []tf.Input{ + audio, sample_rate, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EuclideanNormAttr is an optional argument to EuclideanNorm. +type EuclideanNormAttr func(optionalAttr) + +// EuclideanNormKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func EuclideanNormKeepDims(value bool) EuclideanNormAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the euclidean norm of elements across dimensions of a tensor. +// +// Reduces `input` along the dimensions given in `axis`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `axis`. If `keep_dims` is true, the reduced dimensions are +// retained with length 1. +// +// Arguments: +// input: The tensor to reduce. +// axis: The dimensions to reduce. Must be in the range +// `[-rank(input), rank(input))`. +// +// Returns The reduced tensor. +func EuclideanNorm(scope *Scope, input tf.Output, axis tf.Output, optional ...EuclideanNormAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EuclideanNorm", + Input: []tf.Input{ + input, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// IRFFT3DAttr is an optional argument to IRFFT3D. +type IRFFT3DAttr func(optionalAttr) + +// IRFFT3DTreal sets the optional Treal attribute to value. +// If not specified, defaults to DT_FLOAT +func IRFFT3DTreal(value tf.DataType) IRFFT3DAttr { + return func(m optionalAttr) { + m["Treal"] = value + } +} + +// Inverse 3D real-valued fast Fourier transform. +// +// Computes the inverse 3-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most 3 dimensions of `input`. +// +// The inner-most 3 dimensions of `input` are assumed to be the result of `RFFT3D`: +// The inner-most dimension contains the `fft_length / 2 + 1` unique components of +// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed +// from the size of the inner-most 3 dimensions of `input`. If the FFT length used +// to compute `input` is odd, it should be provided since it cannot be inferred +// properly. +// +// Along each axis `IRFFT3D` is computed on, if `fft_length` (or +// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A complex tensor. +// fft_length: An int32 tensor of shape [3]. The FFT length for each dimension. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most 3 +// dimensions of `input` are replaced with the `fft_length` samples of their +// inverse 3D real Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.irfftn with 3 dimensions. +// @end_compatibility +func IRFFT3D(scope *Scope, input tf.Output, fft_length tf.Output, optional ...IRFFT3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IRFFT3D", + Input: []tf.Input{ + input, fft_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the element-wise min of two SparseTensors. +// +// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. +// +// Arguments: +// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, in the canonical lexicographic ordering. +// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. +// a_shape: 1-D. Shape of the input SparseTensor. +// b_indices: counterpart to `a_indices` for the other operand. +// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. +// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// +// Returns: +// output_indices: 2-D. The indices of the output SparseTensor. +// output_values: 1-D. The values of the output SparseTensor. +func SparseSparseMinimum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSparseMinimum", + Input: []tf.Input{ + a_indices, a_values, a_shape, b_indices, b_values, b_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// StatefulStandardNormalV2Attr is an optional argument to StatefulStandardNormalV2. +type StatefulStandardNormalV2Attr func(optionalAttr) + +// StatefulStandardNormalV2Dtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulStandardNormalV2Dtype(value tf.DataType) StatefulStandardNormalV2Attr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns A tensor of the specified shape filled with random normal values. +func StatefulStandardNormalV2(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulStandardNormalV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulStandardNormalV2", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyFtrlAttr is an optional argument to ResourceSparseApplyFtrl. +type ResourceSparseApplyFtrlAttr func(optionalAttr) + +// ResourceSparseApplyFtrlUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlUseLocking(value bool) ResourceSparseApplyFtrlAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyFtrlMultiplyLinearByLr sets the optional multiply_linear_by_lr attribute to value. +// If not specified, defaults to false +func ResourceSparseApplyFtrlMultiplyLinearByLr(value bool) ResourceSparseApplyFtrlAttr { + return func(m optionalAttr) { + m["multiply_linear_by_lr"] = value + } +} + +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. +// +// That is for rows we have grad for, we update var, accum and linear as follows: +// accum_new = accum + grad * grad +// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyFtrl", + Input: []tf.Input{ + var_, accum, linear, grad, indices, lr, l1, l2, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Debugging/model interpretability outputs for each example. +// +// It traverses all the trees and computes debug metrics for individual examples, +// such as getting split feature ids and logits after each split along the decision +// path used to compute directional feature contributions. +// +// Arguments: +// +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for constructing the protos in +// examples_debug_outputs_serialized. +// +// Returns Output rank 1 Tensor containing a proto serialized as a string for each example. +func BoostedTreesExampleDebugOutputs(scope *Scope, tree_ensemble_handle tf.Output, bucketized_features []tf.Output, logits_dimension int64) (examples_debug_outputs_serialized tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesExampleDebugOutputs", + Input: []tf.Input{ + tree_ensemble_handle, tf.OutputList(bucketized_features), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatefulUniformAttr is an optional argument to StatefulUniform. +type StatefulUniformAttr func(optionalAttr) + +// StatefulUniformDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulUniformDtype(value tf.DataType) StatefulUniformAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns Random values with specified shape. +func StatefulUniform(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulUniformAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulUniform", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedConv2DAttr is an optional argument to QuantizedConv2D. +type QuantizedConv2DAttr func(optionalAttr) + +// QuantizedConv2DOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// QuantizedConv2DDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each +// filter element on that dimension. The dimension order is determined by the +// value of `data_format`, see above for details. Dilations in the batch and +// depth dimensions must be 1. +// If not specified, defaults to +func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 2D convolution given quantized 4D input and filter tensors. +// +// The inputs are quantized tensors where the lowest value represents the real +// number of the associated minimum, and the highest represents the maximum. +// This means that you can only interpret the quantized output in the same way, by +// taking the returned minimum and maximum values into account. +// +// Arguments: +// +// filter: filter's input_depth dimension must match input's depth dimensions. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// min_filter: The float value that the lowest quantized filter value represents. +// max_filter: The float value that the highest quantized filter value represents. +// strides: The stride of the sliding window for each dimension of the input +// tensor. +// padding: The type of padding algorithm to use. +// +// Returns: +// output +// min_output: The float value that the lowest quantized output value represents. +// max_output: The float value that the highest quantized output value represents. +func QuantizedConv2D(scope *Scope, input tf.Output, filter tf.Output, min_input tf.Output, max_input tf.Output, min_filter tf.Output, max_filter tf.Output, strides []int64, padding string, optional ...QuantizedConv2DAttr) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedConv2D", + Input: []tf.Input{ + input, filter, min_input, max_input, min_filter, max_filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes rectified linear 6 gradients for a Relu6 operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Relu6 operation. +// features: The features passed as input to the corresponding Relu6 operation, or +// its output; using either one produces the same result. +// +// Returns The gradients: +// `gradients * (features > 0) * (features < 6)`. +func Relu6Grad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu6Grad", + Input: []tf.Input{ + gradients, features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EditDistanceAttr is an optional argument to EditDistance. +type EditDistanceAttr func(optionalAttr) + +// EditDistanceNormalize sets the optional normalize attribute to value. +// +// value: boolean (if true, edit distances are normalized by length of truth). +// +// The output is: +// If not specified, defaults to true +func EditDistanceNormalize(value bool) EditDistanceAttr { + return func(m optionalAttr) { + m["normalize"] = value + } +} + +// Computes the (possibly normalized) Levenshtein Edit Distance. +// +// The inputs are variable-length sequences provided by SparseTensors +// (hypothesis_indices, hypothesis_values, hypothesis_shape) +// and +// (truth_indices, truth_values, truth_shape). +// +// The inputs are: +// +// Arguments: +// hypothesis_indices: The indices of the hypothesis list SparseTensor. +// This is an N x R int64 matrix. +// hypothesis_values: The values of the hypothesis list SparseTensor. +// This is an N-length vector. +// hypothesis_shape: The shape of the hypothesis list SparseTensor. +// This is an R-length vector. +// truth_indices: The indices of the truth list SparseTensor. +// This is an M x R int64 matrix. +// truth_values: The values of the truth list SparseTensor. +// This is an M-length vector. +// truth_shape: truth indices, vector. +// +// Returns A dense float tensor with rank R - 1. +// +// For the example input: +// +// // hypothesis represents a 2x1 matrix with variable-length values: +// // (0,0) = ["a"] +// // (1,0) = ["b"] +// hypothesis_indices = [[0, 0, 0], +// [1, 0, 0]] +// hypothesis_values = ["a", "b"] +// hypothesis_shape = [2, 1, 1] +// +// // truth represents a 2x2 matrix with variable-length values: +// // (0,0) = [] +// // (0,1) = ["a"] +// // (1,0) = ["b", "c"] +// // (1,1) = ["a"] +// truth_indices = [[0, 1, 0], +// [1, 0, 0], +// [1, 0, 1], +// [1, 1, 0]] +// truth_values = ["a", "b", "c", "a"] +// truth_shape = [2, 2, 2] +// normalize = true +// +// The output will be: +// +// // output is a 2x2 matrix with edit distances normalized by truth lengths. +// output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis +// [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis +func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EditDistance", + Input: []tf.Input{ + hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates a list of `N` tensors along the first dimension. +// +// The input tensors are all required to have size 1 in the first dimension. +// +// For example: +// +// ``` +// # 'x' is [[1, 4]] +// # 'y' is [[2, 5]] +// # 'z' is [[3, 6]] +// parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. +// ``` +// +// The difference between concat and parallel_concat is that concat requires all +// of the inputs be computed before the operation will begin but doesn't require +// that the input shapes be known during graph construction. Parallel concat +// will copy pieces of the input into the output as they become available, in +// some situations this can provide a performance benefit. +// +// Arguments: +// values: Tensors to be concatenated. All must have size 1 in the first dimension +// and same shape. +// shape: the final shape of the result; should be equal to the shapes of any input +// but with the number of input values in the first dimension. +// +// Returns The concatenated tensor. +func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "ParallelConcat", + Input: []tf.Input{ + tf.OutputList(values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AvgPoolGradAttr is an optional argument to AvgPoolGrad. +type AvgPoolGradAttr func(optionalAttr) + +// AvgPoolGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func AvgPoolGradDataFormat(value string) AvgPoolGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of the average pooling function. +// +// Arguments: +// orig_input_shape: 1-D. Shape of the original input to `avg_pool`. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. +// the output of `avg_pool`. +// ksize: The size of the sliding window for each dimension of the input. +// strides: The stride of the sliding window for each dimension of the input. +// padding: The type of padding algorithm to use. +// +// Returns 4-D. Gradients w.r.t. the input of `avg_pool`. +func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPoolGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPoolGrad", + Input: []tf.Input{ + orig_input_shape, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringSplitAttr is an optional argument to StringSplit. +type StringSplitAttr func(optionalAttr) + +// StringSplitSkipEmpty sets the optional skip_empty attribute to value. +// +// value: A `bool`. If `True`, skip the empty strings from the result. +// If not specified, defaults to true +func StringSplitSkipEmpty(value bool) StringSplitAttr { + return func(m optionalAttr) { + m["skip_empty"] = value + } +} + +// Split elements of `input` based on `delimiter` into a `SparseTensor`. +// +// Let N be the size of source (typically N will be the batch size). Split each +// element of `input` based on `delimiter` and return a `SparseTensor` +// containing the splitted tokens. Empty tokens are ignored. +// +// `delimiter` can be empty, or a string of split characters. If `delimiter` is an +// empty string, each element of `input` is split into individual single-byte +// character strings, including splitting of UTF-8 multibyte sequences. Otherwise +// every character of `delimiter` is a potential split point. +// +// For example: +// N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output +// will be +// +// indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// shape = [2, 3] +// values = ['hello', 'world', 'a', 'b', 'c'] +// +// Arguments: +// input: 1-D. Strings to split. +// delimiter: 0-D. Delimiter characters (bytes), or empty string. +// +// Returns: +// indices: A dense matrix of int64 representing the indices of the sparse tensor. +// values: A vector of strings corresponding to the splited values. +// shape: a length-2 vector of int64 representing the shape of the sparse +// tensor, where the first value is N and the second value is the maximum number +// of tokens in a single input entry. +func StringSplit(scope *Scope, input tf.Output, delimiter tf.Output, optional ...StringSplitAttr) (indices tf.Output, values tf.Output, shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringSplit", + Input: []tf.Input{ + input, delimiter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Assigns sparse updates to the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] = updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] +// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterUpdate(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterUpdate", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Creates ngrams from ragged string data. +// +// This op accepts a ragged tensor with 1 ragged dimension containing only +// strings and outputs a ragged tensor with 1 ragged dimension containing ngrams +// of that string, joined along the innermost axis. +// +// Arguments: +// data: The values tensor of the ragged string tensor to make ngrams out of. Must be a +// 1D string tensor. +// data_splits: The splits tensor of the ragged string tensor to make ngrams out of. +// separator: The string to append between elements of the token. Use "" for no separator. +// ngram_widths: The sizes of the ngrams to create. +// left_pad: The string to use to pad the left side of the ngram sequence. Only used if +// pad_width != 0. +// right_pad: The string to use to pad the right side of the ngram sequence. Only used if +// pad_width != 0. +// pad_width: The number of padding elements to add to each side of each +// sequence. Note that padding will never be greater than 'ngram_widths'-1 +// regardless of this value. If `pad_width=-1`, then add `max(ngram_widths)-1` +// elements. +// +// +// Returns: +// ngrams: The values tensor of the output ngrams ragged tensor. +// ngrams_splits: The splits tensor of the output ngrams ragged tensor. +func StringNGrams(scope *Scope, data tf.Output, data_splits tf.Output, separator string, ngram_widths []int64, left_pad string, right_pad string, pad_width int64, preserve_short_sequences bool) (ngrams tf.Output, ngrams_splits tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"separator": separator, "ngram_widths": ngram_widths, "left_pad": left_pad, "right_pad": right_pad, "pad_width": pad_width, "preserve_short_sequences": preserve_short_sequences} + opspec := tf.OpSpec{ + Type: "StringNGrams", + Input: []tf.Input{ + data, data_splits, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Reduces sparse updates into the variable referenced by `resource` using the `min` operation. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = min(ref[indices, ...], updates[...]) +// +// # Vector indices (for each i) +// ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...]) +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions are combined. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterMin(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterMin", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Multiplies sparse updates into the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] *= updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] *= updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions multiply. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterMul(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterMul", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Compresses a dataset element. +func CompressElement(scope *Scope, components []tf.Output) (compressed tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CompressElement", + Input: []tf.Input{ + tf.OutputList(components), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MatMulAttr is an optional argument to MatMul. +type MatMulAttr func(optionalAttr) + +// MatMulTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, "a" is transposed before multiplication. +// If not specified, defaults to false +func MatMulTransposeA(value bool) MatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// MatMulTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, "b" is transposed before multiplication. +// If not specified, defaults to false +func MatMulTransposeB(value bool) MatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// Multiply the matrix "a" by the matrix "b". +// +// The inputs must be two-dimensional matrices and the inner dimension of +// "a" (after being transposed if transpose_a is true) must match the +// outer dimension of "b" (after being transposed if transposed_b is +// true). +// +// *Note*: The default kernel implementation for MatMul on GPUs uses +// cublas. +func MatMul(scope *Scope, a tf.Output, b tf.Output, optional ...MatMulAttr) (product tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseReduceSumSparseAttr is an optional argument to SparseReduceSumSparse. +type SparseReduceSumSparseAttr func(optionalAttr) + +// SparseReduceSumSparseKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceSumSparseKeepDims(value bool) SparseReduceSumSparseAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the sum of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a +// SparseTensor. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +func SparseReduceSumSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceSumSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceSumSparse", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes rectified linear: `max(features, 0)`. +// +// See: https://en.wikipedia.org/wiki/Rectifier_(neural_networks) +// Example usage: +// >>> tf.nn.relu([-2., 0., -0., 3.]).numpy() +// array([ 0., 0., -0., 3.], dtype=float32) +func Relu(scope *Scope, features tf.Output) (activations tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Relu", + Input: []tf.Input{ + features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Get the number of nodes in a tree +// +// Arguments: +// tree_handle: Handle to the tree resource. +// +// Returns The size of the tree. +func TensorForestTreeSize(scope *Scope, tree_handle tf.Output) (tree_size tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorForestTreeSize", + Input: []tf.Input{ + tree_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Subtracts sparse updates from the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] -= updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] -= updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions add. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterSub(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterSub", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// Returns the cardinality of `input_dataset`. +// +// Returns the cardinality of `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to return cardinality for. +// +// Returns The cardinality of `input_dataset`. Named constants are used to represent +// infinite and unknown cardinality. +func DatasetCardinality(scope *Scope, input_dataset tf.Output) (cardinality tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DatasetCardinality", + Input: []tf.Input{ + input_dataset, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits each dim-0 slice of `components` once. +func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "TensorSliceDataset", + Input: []tf.Input{ + tf.OutputList(components), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to RetrieveTPUEmbeddingMDLAdagradLightParameters. +type RetrieveTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingMDLAdagradLightParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingMDLAdagradLightParametersTableId(value int64) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingMDLAdagradLightParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMDLAdagradLightParametersTableName(value string) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingMDLAdagradLightParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMDLAdagradLightParametersConfig(value string) RetrieveTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve MDL Adagrad Light embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the MDL Adagrad Light optimization algorithm. +// accumulators: Parameter accumulators updated by the MDL Adagrad Light optimization algorithm. +// weights: Parameter weights updated by the MDL Adagrad Light optimization algorithm. +// benefits: Parameter benefits updated by the MDL Adagrad Light optimization algorithm. +func RetrieveTPUEmbeddingMDLAdagradLightParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMDLAdagradLightParametersAttr) (parameters tf.Output, accumulators tf.Output, weights tf.Output, benefits tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingMDLAdagradLightParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Adds sparse updates to the variable referenced by `resource`. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] += updates[...] +// +// # Vector indices (for each i) +// ref[indices[i], ...] += updates[i, ...] +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] += updates[i, ..., j, ...] +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions add. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterAdd(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterAdd", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// This op consumes a lock created by `MutexLock`. +// +// This op exists to consume a tensor created by `MutexLock` (other than +// direct control dependencies). It should be the only that consumes the tensor, +// and will raise an error if it is not. Its only purpose is to keep the +// mutex lock tensor alive until it is consumed by this op. +// +// **NOTE**: This operation must run on the same device as its input. This may +// be enforced via the `colocate_with` mechanism. +// +// Arguments: +// mutex_lock: A tensor returned by `MutexLock`. +// +// Returns the created operation. +func ConsumeMutexLock(scope *Scope, mutex_lock tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ConsumeMutexLock", + Input: []tf.Input{ + mutex_lock, + }, + } + return scope.AddOperation(opspec) +} + +// BiasAddAttr is an optional argument to BiasAdd. +type BiasAddAttr func(optionalAttr) + +// BiasAddDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the bias tensor will be added to the last dimension +// of the value tensor. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// The tensor will be added to "in_channels", the third-to-the-last +// dimension. +// If not specified, defaults to "NHWC" +func BiasAddDataFormat(value string) BiasAddAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Adds `bias` to `value`. +// +// This is a special case of `tf.add` where `bias` is restricted to be 1-D. +// Broadcasting is supported, so `value` may have any number of dimensions. +// +// Arguments: +// value: Any number of dimensions. +// bias: 1-D with size the last dimension of `value`. +// +// Returns Broadcasted sum of `value` and `bias`. +func BiasAdd(scope *Scope, value tf.Output, bias tf.Output, optional ...BiasAddAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BiasAdd", + Input: []tf.Input{ + value, bias, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces the average pool of the input tensor for quantized types. +// +// Arguments: +// input: 4-D with shape `[batch, height, width, channels]`. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// ksize: The size of the window for each dimension of the input tensor. +// The length must be 4 to match the number of dimensions of the input. +// strides: The stride of the sliding window for each dimension of the input +// tensor. The length must be 4 to match the number of dimensions of the input. +// padding: The type of padding algorithm to use. +// +// Returns: +// output +// min_output: The float value that the lowest quantized output value represents. +// max_output: The float value that the highest quantized output value represents. +func QuantizedAvgPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "QuantizedAvgPool", + Input: []tf.Input{ + input, min_input, max_input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// VariableShapeAttr is an optional argument to VariableShape. +type VariableShapeAttr func(optionalAttr) + +// VariableShapeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func VariableShapeOutType(value tf.DataType) VariableShapeAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns the shape of the variable pointed to by `resource`. +// +// This operation returns a 1-D integer tensor representing the shape of `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] +// shape(t) ==> [2, 2, 3] +// ``` +func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "VariableShape", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the minimum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// This operator is similar to the unsorted segment sum operator found +// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). +// Instead of computing the sum over segments, it computes the minimum such that: +// +// \\(output_i = \min_{j...} data_[j...]\\) where min is over tuples `j...` such +// that `segment_ids[j...] == i`. +// +// If the minimum is empty for a given segment ID `i`, it outputs the largest +// possible value for the specific numeric type, +// `output[i] = numeric_limits::max()`. +// +// For example: +// +// ``` python +// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) +// tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 1, 2, 2, 1], +// # [5, 6, 7, 8]] +// ``` +// +// If the given segment ID `i` is negative, then the corresponding value is +// dropped, and will not be included in the result. +// +// Arguments: +// +// segment_ids: A tensor whose shape is a prefix of `data.shape`. +// +// +// Returns Has same shape as data, except for the first `segment_ids.rank` +// dimensions, which are replaced with a single dimension which has size +// `num_segments`. +func UnsortedSegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnsortedSegmentMin", + Input: []tf.Input{ + data, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceScatterNdSubAttr is an optional argument to ResourceScatterNdSub. +type ResourceScatterNdSubAttr func(optionalAttr) + +// ResourceScatterNdSubUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdSubUseLocking(value bool) ResourceScatterNdSubAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Applies sparse subtraction to individual values or slices in a Variable. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] +// ``` +// +// For example, say we want to subtract 4 scattered elements from a rank-1 tensor +// with 8 elements. In Python, that subtraction would look like this: +// +// ```python +// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// sub = tf.scatter_nd_sub(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(sub) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, -9, 3, -6, -4, 6, 7, -4] +// +// See `tf.scatter_nd` for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdSub(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdSubAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdSub", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// DataFormatDimMapAttr is an optional argument to DataFormatDimMap. +type DataFormatDimMapAttr func(optionalAttr) + +// DataFormatDimMapSrcFormat sets the optional src_format attribute to value. +// +// value: source data format. +// If not specified, defaults to "NHWC" +func DataFormatDimMapSrcFormat(value string) DataFormatDimMapAttr { + return func(m optionalAttr) { + m["src_format"] = value + } +} + +// DataFormatDimMapDstFormat sets the optional dst_format attribute to value. +// +// value: destination data format. +// If not specified, defaults to "NCHW" +func DataFormatDimMapDstFormat(value string) DataFormatDimMapAttr { + return func(m optionalAttr) { + m["dst_format"] = value + } +} + +// Returns the dimension index in the destination data format given the one in +// +// the source data format. +// +// Arguments: +// x: A Tensor with each element as a dimension index in source data format. +// Must be in the range [-4, 4). +// +// Returns A Tensor with each element as a dimension index in destination data format. +func DataFormatDimMap(scope *Scope, x tf.Output, optional ...DataFormatDimMapAttr) (y tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DataFormatDimMap", + Input: []tf.Input{ + x, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Assigns a new value to a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to return +// this value or a subsequent newer value of the variable. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value to set the new tensor to use. +// +// Returns the created operation. +func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AssignVariableOp", + Input: []tf.Input{ + resource, value, + }, + } + return scope.AddOperation(opspec) +} + +// UpperBoundAttr is an optional argument to UpperBound. +type UpperBoundAttr func(optionalAttr) + +// UpperBoundOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func UpperBoundOutType(value tf.DataType) UpperBoundAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Applies upper_bound(sorted_search_values, values) along each row. +// +// Each set of rows with the same index in (sorted_inputs, values) is treated +// independently. The resulting row is the equivalent of calling +// `np.searchsorted(sorted_inputs, values, side='right')`. +// +// The result is not a global index to the entire +// `Tensor`, but rather just the index in the last dimension. +// +// A 2-D example: +// sorted_sequence = [[0, 3, 9, 9, 10], +// [1, 2, 3, 4, 5]] +// values = [[2, 4, 9], +// [0, 2, 6]] +// +// result = UpperBound(sorted_sequence, values) +// +// result == [[1, 2, 4], +// [0, 2, 5]] +// +// Arguments: +// sorted_inputs: 2-D Tensor where each row is ordered. +// values: 2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains +// the values that will be searched for in `sorted_search_values`. +// +// Returns A `Tensor` with the same shape as `values`. It contains the last scalar index +// into the last dimension where values can be inserted without changing the +// ordered property. +func UpperBound(scope *Scope, sorted_inputs tf.Output, values tf.Output, optional ...UpperBoundAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UpperBound", + Input: []tf.Input{ + sorted_inputs, values, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyFtrlV2Attr is an optional argument to ResourceApplyFtrlV2. +type ResourceApplyFtrlV2Attr func(optionalAttr) + +// ResourceApplyFtrlV2UseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyFtrlV2MultiplyLinearByLr sets the optional multiply_linear_by_lr attribute to value. +// If not specified, defaults to false +func ResourceApplyFtrlV2MultiplyLinearByLr(value bool) ResourceApplyFtrlV2Attr { + return func(m optionalAttr) { + m["multiply_linear_by_lr"] = value + } +} + +// Update '*var' according to the Ftrl-proximal scheme. +// +// grad_with_shrinkage = grad + 2 * l2_shrinkage * var +// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage +// linear += grad_with_shrinkage + +// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 shrinkage regularization. Must be a scalar. +// +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyFtrlV2", + Input: []tf.Input{ + var_, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Deprecated. Use TensorArraySplitV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArraySplitV3 +func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArraySplitV2", + Input: []tf.Input{ + handle, value, lengths, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ComputeAccidentalHitsAttr is an optional argument to ComputeAccidentalHits. +type ComputeAccidentalHitsAttr func(optionalAttr) + +// ComputeAccidentalHitsSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed(value int64) ComputeAccidentalHitsAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// ComputeAccidentalHitsSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func ComputeAccidentalHitsSeed2(value int64) ComputeAccidentalHitsAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Computes the ids of the positions in sampled_candidates that match true_labels. +// +// When doing log-odds NCE, the result of this op should be passed through a +// SparseToDense op, then added to the logits of the sampled candidates. This has +// the effect of 'removing' the sampled labels that match the true labels by +// making the classifier sure that they are sampled labels. +// +// Arguments: +// true_classes: The true_classes output of UnpackSparseLabels. +// sampled_candidates: The sampled_candidates output of CandidateSampler. +// num_true: Number of true labels per context. +// +// Returns: +// indices: A vector of indices corresponding to rows of true_candidates. +// ids: A vector of IDs of positions in sampled_candidates that match a true_label +// for the row with the corresponding index in indices. +// weights: A vector of the same length as indices and ids, in which each element +// is -FLOAT_MAX. +func ComputeAccidentalHits(scope *Scope, true_classes tf.Output, sampled_candidates tf.Output, num_true int64, optional ...ComputeAccidentalHitsAttr) (indices tf.Output, ids tf.Output, weights tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_true": num_true} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ComputeAccidentalHits", + Input: []tf.Input{ + true_classes, sampled_candidates, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// VarHandleOpAttr is an optional argument to VarHandleOp. +type VarHandleOpAttr func(optionalAttr) + +// VarHandleOpContainer sets the optional container attribute to value. +// +// value: the container this variable is placed in. +// If not specified, defaults to "" +func VarHandleOpContainer(value string) VarHandleOpAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// VarHandleOpSharedName sets the optional shared_name attribute to value. +// +// value: the name by which this variable is referred to. +// If not specified, defaults to "" +func VarHandleOpSharedName(value string) VarHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// VarHandleOpAllowedDevices sets the optional allowed_devices attribute to value. +// +// value: DEPRECATED. The allowed devices containing the resource variable. Set when the +// output ResourceHandle represents a per-replica/partitioned resource variable. +// If not specified, defaults to <> +func VarHandleOpAllowedDevices(value []string) VarHandleOpAttr { + return func(m optionalAttr) { + m["allowed_devices"] = value + } +} + +// Creates a handle to a Variable resource. +// +// Arguments: +// dtype: the type of this variable. Must agree with the dtypes +// of all ops using this variable. +// shape: The (possibly partially specified) shape of this variable. +func VarHandleOp(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...VarHandleOpAttr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "VarHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns (x - y)(x - y) element-wise. +// +// *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func SquaredDifference(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SquaredDifference", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that emits the records from one or more TFRecord files. +// +// Arguments: +// filenames: A scalar or vector containing the name(s) of the file(s) to be +// read. +// compression_type: A scalar containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// buffer_size: A scalar representing the number of bytes to buffer. A value of +// 0 means no buffering will be performed. +func TFRecordDataset(scope *Scope, filenames tf.Output, compression_type tf.Output, buffer_size tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TFRecordDataset", + Input: []tf.Input{ + filenames, compression_type, buffer_size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RpcAttr is an optional argument to Rpc. +type RpcAttr func(optionalAttr) + +// RpcProtocol sets the optional protocol attribute to value. +// +// value: RPC protocol to use. Empty string means use the default protocol. +// Options include 'grpc'. +// If not specified, defaults to "" +func RpcProtocol(value string) RpcAttr { + return func(m optionalAttr) { + m["protocol"] = value + } +} + +// RpcFailFast sets the optional fail_fast attribute to value. +// +// value: `boolean`. If `true` (default), then failures to connect +// (i.e., the server does not immediately respond) cause an RPC failure. +// If not specified, defaults to true +func RpcFailFast(value bool) RpcAttr { + return func(m optionalAttr) { + m["fail_fast"] = value + } +} + +// RpcTimeoutInMs sets the optional timeout_in_ms attribute to value. +// +// value: `int`. If `0` (default), then the kernel will run the RPC +// request and only time out if the RPC deadline passes or the session times out. +// If this value is greater than `0`, then the op will raise an exception if +// the RPC takes longer than `timeout_in_ms`. +// If not specified, defaults to 0 +func RpcTimeoutInMs(value int64) RpcAttr { + return func(m optionalAttr) { + m["timeout_in_ms"] = value + } +} + +// Perform batches of RPC requests. +// +// This op asynchronously performs either a single RPC request, or a batch +// of requests. RPC requests are defined by three main parameters: +// +// - `address` (the host+port or BNS address of the request) +// - `method` (the RPC method name for the request) +// - `request` (the serialized proto string, or vector of strings, +// of the RPC request argument). +// +// For example, if you have an RPC service running on port localhost:2345, +// and its interface is configured with the following proto declaration: +// +// ``` +// service MyService { +// rpc MyMethod(MyRequestProto) returns (MyResponseProto) { +// } +// }; +// ``` +// +// then call this op with arguments: +// +// ``` +// address = "localhost:2345" +// method = "MyService/MyMethod" +// ``` +// +// The `request` tensor is a string tensor representing serialized `MyRequestProto` +// strings; and the output string tensor `response` will have the same shape +// and contain (upon successful completion) corresponding serialized +// `MyResponseProto` strings. +// +// For example, to send a single, empty, `MyRequestProto`, call +// this op with `request = ""`. To send 5 **parallel** empty requests, +// call this op with `request = ["", "", "", "", ""]`. +// +// More generally, one can create a batch of `MyRequestProto` serialized protos +// from regular batched tensors using the `encode_proto` op, and convert +// the response `MyResponseProto` serialized protos to batched tensors +// using the `decode_proto` op. +// +// **NOTE** Working with serialized proto strings is faster than instantiating +// actual proto objects in memory, so no performance degradation is expected +// compared to writing custom kernels for this workflow. +// +// If the connection fails or the remote worker returns an error +// status, the op reraises this exception locally. +// +// See the `TryRpc` op if you prefer to handle RPC failures manually in the graph. +// +// Arguments: +// address: `0-D` or `1-D`. The address (i.e. host_name:port) of the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `method` and `request`. +// method: `0-D` or `1-D`. The method address on the RPC server. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `request`. +// request: `0-D` or `1-D`. Serialized proto strings: the rpc request argument. +// If this tensor has more than 1 element, then multiple parallel rpc requests +// are sent. This argument broadcasts with `address` and `method`. +// +// Returns Same shape as `request`. Serialized proto strings: the rpc responses. +func Rpc(scope *Scope, address tf.Output, method tf.Output, request tf.Output, optional ...RpcAttr) (response tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Rpc", + Input: []tf.Input{ + address, method, request, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BoostedTreesQuantileStreamResourceHandleOpAttr is an optional argument to BoostedTreesQuantileStreamResourceHandleOp. +type BoostedTreesQuantileStreamResourceHandleOpAttr func(optionalAttr) + +// BoostedTreesQuantileStreamResourceHandleOpContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func BoostedTreesQuantileStreamResourceHandleOpContainer(value string) BoostedTreesQuantileStreamResourceHandleOpAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// BoostedTreesQuantileStreamResourceHandleOpSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func BoostedTreesQuantileStreamResourceHandleOpSharedName(value string) BoostedTreesQuantileStreamResourceHandleOpAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a handle to a BoostedTreesQuantileStreamResource. +func BoostedTreesQuantileStreamResourceHandleOp(scope *Scope, optional ...BoostedTreesQuantileStreamResourceHandleOpAttr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BoostedTreesQuantileStreamResourceHandleOp", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyAdagradAttr is an optional argument to ResourceSparseApplyAdagrad. +type ResourceSparseApplyAdagradAttr func(optionalAttr) + +// ResourceSparseApplyAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradUseLocking(value bool) ResourceSparseApplyAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceSparseApplyAdagradUpdateSlots(value bool) ResourceSparseApplyAdagradAttr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. +// +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdagrad", + Input: []tf.Input{ + var_, accum, lr, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// EagerPyFuncAttr is an optional argument to EagerPyFunc. +type EagerPyFuncAttr func(optionalAttr) + +// EagerPyFuncIsAsync sets the optional is_async attribute to value. +// If not specified, defaults to false +func EagerPyFuncIsAsync(value bool) EagerPyFuncAttr { + return func(m optionalAttr) { + m["is_async"] = value + } +} + +// Eagerly executes a python function to compute func(input)->output. The +// +// semantics of the input, output, and attributes are the same as those for +// PyFunc. +func EagerPyFunc(scope *Scope, input []tf.Output, token string, Tout []tf.DataType, optional ...EagerPyFuncAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"token": token, "Tout": Tout} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EagerPyFunc", + Input: []tf.Input{ + tf.OutputList(input), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("EagerPyFunc", err) + return + } + return output +} + +// SdcaOptimizerV2Attr is an optional argument to SdcaOptimizerV2. +type SdcaOptimizerV2Attr func(optionalAttr) + +// SdcaOptimizerV2Adaptive sets the optional adaptive attribute to value. +// +// value: Whether to use Adaptive SDCA for the inner loop. +// If not specified, defaults to true +func SdcaOptimizerV2Adaptive(value bool) SdcaOptimizerV2Attr { + return func(m optionalAttr) { + m["adaptive"] = value + } +} + +// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for +// +// linear models with L1 + L2 regularization. As global optimization objective is +// strongly-convex, the optimizer optimizes the dual objective at each step. The +// optimizer applies each update one example at a time. Examples are sampled +// uniformly, and the optimizer is learning rate free and enjoys linear convergence +// rate. +// +// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
+// Shai Shalev-Shwartz, Tong Zhang. 2012 +// +// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ +// +// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
+// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, +// Peter Richtarik, Martin Takac. 2015 +// +// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
+// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 +// +// Arguments: +// sparse_example_indices: a list of vectors which contain example indices. +// sparse_feature_indices: a list of vectors which contain feature indices. +// sparse_feature_values: a list of vectors which contains feature value +// associated with each feature group. +// dense_features: a list of matrices which contains the dense feature values. +// example_weights: a vector which contains the weight associated with each +// example. +// example_labels: a vector which contains the label/target associated with each +// example. +// sparse_indices: a list of vectors where each value is the indices which has +// corresponding weights in sparse_weights. This field maybe omitted for the +// dense approach. +// sparse_weights: a list of vectors where each value is the weight associated with +// a sparse feature group. +// dense_weights: a list of vectors where the values are the weights associated +// with a dense feature group. +// example_state_data: a list of vectors containing the example state data. +// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, +// squared and hinge losses. +// l1: Symmetric l1 regularization strength. +// l2: Symmetric l2 regularization strength. +// num_loss_partitions: Number of partitions of the global loss function. +// num_inner_iterations: Number of iterations per mini-batch. +// +// Returns: +// out_example_state_data: a list of vectors containing the updated example state +// data. +// out_delta_sparse_weights: a list of vectors where each value is the delta +// weights associated with a sparse feature group. +// out_delta_dense_weights: a list of vectors where the values are the delta +// weights associated with a dense feature group. +func SdcaOptimizerV2(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerV2Attr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SdcaOptimizerV2", + Input: []tf.Input{ + tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + out_example_state_data = op.Output(idx) + if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { + scope.UpdateErr("SdcaOptimizerV2", err) + return + } + if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { + scope.UpdateErr("SdcaOptimizerV2", err) + return + } + return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights +} + +// MaxPool3DGradGradAttr is an optional argument to MaxPool3DGradGrad. +type MaxPool3DGradGradAttr func(optionalAttr) + +// MaxPool3DGradGradDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradGradDataFormat(value string) MaxPool3DGradGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPool3DGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3DGradGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that contains the elements of `input_dataset` ignoring errors. +func IgnoreErrorsDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "IgnoreErrorsDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deprecated. Use TensorArrayGradV3 +// +// DEPRECATED at GraphDef version 26: Use TensorArrayWriteV3 +func TensorArrayWriteV2(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayWriteV2", + Input: []tf.Input{ + handle, index, value, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DenseToSparseSetOperationAttr is an optional argument to DenseToSparseSetOperation. +type DenseToSparseSetOperationAttr func(optionalAttr) + +// DenseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func DenseToSparseSetOperationValidateIndices(value bool) DenseToSparseSetOperationAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Applies set operation along last dimension of `Tensor` and `SparseTensor`. +// +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set2` +// indices. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the +// max set size across `n-1` dimensions. +// +// +// Returns: +// result_indices: 2D indices of a `SparseTensor`. +// result_values: 1D values of a `SparseTensor`. +// result_shape: 1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func DenseToSparseSetOperation(scope *Scope, set1 tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...DenseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DenseToSparseSetOperation", + Input: []tf.Input{ + set1, set2_indices, set2_values, set2_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// CollectiveBcastRecvAttr is an optional argument to CollectiveBcastRecv. +type CollectiveBcastRecvAttr func(optionalAttr) + +// CollectiveBcastRecvCommunicationHint sets the optional communication_hint attribute to value. +// If not specified, defaults to "auto" +func CollectiveBcastRecvCommunicationHint(value string) CollectiveBcastRecvAttr { + return func(m optionalAttr) { + m["communication_hint"] = value + } +} + +// CollectiveBcastRecvTimeoutSeconds sets the optional timeout_seconds attribute to value. +// If not specified, defaults to 0 +func CollectiveBcastRecvTimeoutSeconds(value float32) CollectiveBcastRecvAttr { + return func(m optionalAttr) { + m["timeout_seconds"] = value + } +} + +// Receives a tensor value broadcast from another device. +func CollectiveBcastRecv(scope *Scope, T tf.DataType, group_size int64, group_key int64, instance_key int64, shape tf.Shape, optional ...CollectiveBcastRecvAttr) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T, "group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CollectiveBcastRecv", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Scatter the data from the input value into specific TensorArray elements. +// +// `indices` must be a vector, its length must match the first dim of `value`. +// +// Arguments: +// handle: The handle to a TensorArray. +// indices: The locations at which to write the tensor elements. +// value: The concatenated tensor to write to the TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// +// Returns A float scalar that enforces proper chaining of operations. +func TensorArrayScatterV3(scope *Scope, handle tf.Output, indices tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorArrayScatterV3", + Input: []tf.Input{ + handle, indices, value, flow_in, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the matrix square root of one or more square matrices: +// +// matmul(sqrtm(A), sqrtm(A)) = A +// +// The input matrix should be invertible. If the input matrix is real, it should +// have no eigenvalues which are real and negative (pairs of complex conjugate +// eigenvalues are allowed). +// +// The matrix square root is computed by first reducing the matrix to +// quasi-triangular form with the real Schur decomposition. The square root +// of the quasi-triangular matrix is then computed directly. Details of +// the algorithm can be found in: Nicholas J. Higham, "Computing real +// square roots of a real matrix", Linear Algebra Appl., 1987. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor of the same shape as the input +// containing the matrix square root for all input submatrices `[..., :, :]`. +// +// Arguments: +// input: Shape is `[..., M, M]`. +// +// Returns Shape is `[..., M, M]`. +// +// @compatibility(scipy) +// Equivalent to scipy.linalg.sqrtm +// @end_compatibility +func MatrixSquareRoot(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixSquareRoot", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MutexV2Attr is an optional argument to MutexV2. +type MutexV2Attr func(optionalAttr) + +// MutexV2Container sets the optional container attribute to value. +// +// value: If non-empty, this variable is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func MutexV2Container(value string) MutexV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MutexV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this variable is named in the given bucket +// with this shared_name. Otherwise, the node name is used instead. +// If not specified, defaults to "" +func MutexV2SharedName(value string) MutexV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a Mutex resource that can be locked by `MutexLock`. +// +// Returns The mutex resource. +func MutexV2(scope *Scope, optional ...MutexV2Attr) (resource tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MutexV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of (x < y) element-wise. +// +// *NOTE*: `Less` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// Example: +// +// ```python +// x = tf.constant([5, 4, 6]) +// y = tf.constant([5]) +// tf.math.less(x, y) ==> [False, True, False] +// +// x = tf.constant([5, 4, 6]) +// y = tf.constant([5, 6, 7]) +// tf.math.less(x, y) ==> [False, True, True] +// ``` +func Less(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Less", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolGradGradWithArgmaxAttr is an optional argument to MaxPoolGradGradWithArgmax. +type MaxPoolGradGradWithArgmaxAttr func(optionalAttr) + +// MaxPoolGradGradWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. +// +// value: Whether to include batch dimension in flattened index of `argmax`. +// If not specified, defaults to false +func MaxPoolGradGradWithArgmaxIncludeBatchInIndex(value bool) MaxPoolGradGradWithArgmaxAttr { + return func(m optionalAttr) { + m["include_batch_in_index"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// input: The original input. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the +// input of `max_pool`. +// argmax: The indices of the maximum values chosen for each output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input of `max_pool`. +func MaxPoolGradGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradWithArgmaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradGradWithArgmax", + Input: []tf.Input{ + input, grad, argmax, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StridedSliceAttr is an optional argument to StridedSlice. +type StridedSliceAttr func(optionalAttr) + +// StridedSliceBeginMask sets the optional begin_mask attribute to value. +// +// value: a bitmask where a bit i being 1 means to ignore the begin +// value and instead use the largest interval possible. At runtime +// begin[i] will be replaced with `[0, n-1)` if `stride[i] > 0` or +// `[-1, n-1]` if `stride[i] < 0` +// If not specified, defaults to 0 +func StridedSliceBeginMask(value int64) StridedSliceAttr { + return func(m optionalAttr) { + m["begin_mask"] = value + } +} + +// StridedSliceEndMask sets the optional end_mask attribute to value. +// +// value: analogous to `begin_mask` +// If not specified, defaults to 0 +func StridedSliceEndMask(value int64) StridedSliceAttr { + return func(m optionalAttr) { + m["end_mask"] = value + } +} + +// StridedSliceEllipsisMask sets the optional ellipsis_mask attribute to value. +// +// value: a bitmask where bit `i` being 1 means the `i`th +// position is actually an ellipsis. One bit at most can be 1. +// If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)` +// is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis +// implicitly creates as many range specifications as necessary to fully +// specify the sliced range for every dimension. For example for a 4-dimensional +// tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`. +// If not specified, defaults to 0 +func StridedSliceEllipsisMask(value int64) StridedSliceAttr { + return func(m optionalAttr) { + m["ellipsis_mask"] = value + } +} + +// StridedSliceNewAxisMask sets the optional new_axis_mask attribute to value. +// +// value: a bitmask where bit `i` being 1 means the `i`th +// specification creates a new shape 1 dimension. For example +// `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor. +// If not specified, defaults to 0 +func StridedSliceNewAxisMask(value int64) StridedSliceAttr { + return func(m optionalAttr) { + m["new_axis_mask"] = value + } +} + +// StridedSliceShrinkAxisMask sets the optional shrink_axis_mask attribute to value. +// +// value: a bitmask where bit `i` implies that the `i`th +// specification should shrink the dimensionality. begin and end +// must imply a slice of size 1 in the dimension. For example in +// python one might do `foo[:, 3, :]` which would result in +// `shrink_axis_mask` being 2. +// If not specified, defaults to 0 +func StridedSliceShrinkAxisMask(value int64) StridedSliceAttr { + return func(m optionalAttr) { + m["shrink_axis_mask"] = value + } +} + +// Return a strided slice from `input`. +// +// Note, most python users will want to use the Python `Tensor.__getitem__` +// or `Variable.__getitem__` rather than this op directly. +// +// The goal of this op is to produce a new tensor with a subset of +// the elements from the `n` dimensional `input` tensor. The subset is chosen using +// a sequence of `m` sparse range specifications encoded into the arguments +// of this function. Note, in some cases +// `m` could be equal to `n`, but this need not be the case. Each +// range specification entry can be one of the following: +// +// - An ellipsis (...). Ellipses are used to imply zero or more +// dimensions of full-dimension selection and are produced using +// `ellipsis_mask`. For example, `foo[...]` is the identity slice. +// +// - A new axis. This is used to insert a new shape=1 dimension and is +// produced using `new_axis_mask`. For example, `foo[:, ...]` where +// `foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor. +// +// +// - A range `begin:end:stride`. This is used to specify how much to choose from +// a given dimension. `stride` can be any integer but 0. `begin` is an integer +// which represents the index of the first value to select while `end` represents +// the index of the last value to select. The number of values selected in each +// dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`. +// `begin` and `end` can be negative where `-1` is the last element, `-2` is +// the second to last. `begin_mask` controls whether to replace the explicitly +// given `begin` with an implicit effective value of `0` if `stride > 0` and +// `-1` if `stride < 0`. `end_mask` is analogous but produces the number +// required to create the largest open interval. For example, given a shape +// `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do +// not assume this is equivalent to `foo[0:-1]` which has an effective `begin` +// and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the +// first dimension of a tensor while dropping the last two (in the original +// order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`. +// +// - A single index. This is used to keep only elements that have a given +// index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a +// shape `(6,)` tensor. This is encoded in `begin` and `end` and +// `shrink_axis_mask`. +// +// Each conceptual range specification is encoded in the op's argument. This +// encoding is best understand by considering a non-trivial example. In +// particular, +// `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as +// +// ``` +// begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0) +// end = [2, 4, x, x, -3, x] +// strides = [1, 1, x, x, -1, 1] +// begin_mask = 1<<4 | 1<<5 = 48 +// end_mask = 1<<5 = 32 +// ellipsis_mask = 1<<3 = 8 +// new_axis_mask = 1<<2 = 4 +// shrink_axis_mask = 1<<0 = 1 +// ``` +// +// In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of +// the slice becomes (2, 1, 5, 5, 2, 5). +// Let us walk step by step through each argument specification. +// +// 1. The first argument in the example slice is turned into `begin = 1` and +// `end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we +// also set the appropriate bit in `shrink_axis_mask`. +// +// 2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have +// zero bits contributed. +// +// 3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1 +// dimension in the final shape. Dummy values are contributed to begin, +// end and stride, while the new_axis_mask bit is set. +// +// 4. `...` grab the full ranges from as many dimensions as needed to +// fully specify a slice for every dimension of the input shape. +// +// 5. `:-3:-1` shows the use of negative indices. A negative index `i` associated +// with a dimension that has shape `s` is converted to a positive index +// `s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion +// is done internally so begin, end and strides receive x, -3, and -1. +// The appropriate begin_mask bit is set to indicate the start range is the +// full range (ignoring the x). +// +// 6. `:` indicates that the entire contents of the corresponding dimension +// is selected. This is equivalent to `::` or `0::1`. begin, end, and strides +// receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and +// `end_mask` are also set. +// +// *Requirements*: +// `0 != strides[i] for i in [0, m)` +// `ellipsis_mask must be a power of two (only one ellipsis)` +// +// Arguments: +// +// begin: `begin[k]` specifies the offset into the `k`th range specification. +// The exact dimension this corresponds to will be determined by context. +// Out-of-bounds values will be silently clamped. If the `k`th bit of +// `begin_mask` then `begin[k]` is ignored and the full range of the +// appropriate dimension is used instead. Negative values causes indexing +// to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`. +// end: `end[i]` is like `begin` with the exception that `end_mask` is +// used to determine full ranges. +// strides: `strides[i]` specifies the increment in the `i`th specification +// after extracting a given element. Negative indices will reverse +// the original order. Out or range values are +// clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0` +func StridedSlice(scope *Scope, input tf.Output, begin tf.Output, end tf.Output, strides tf.Output, optional ...StridedSliceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StridedSlice", + Input: []tf.Input{ + input, begin, end, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug. +type RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve RMSProp embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the RMSProp optimization algorithm. +// ms: Parameter ms updated by the RMSProp optimization algorithm. +// mom: Parameter mom updated by the RMSProp optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the RMSProp optimization algorithm. +func RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingRMSPropParametersGradAccumDebugAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// DenseToDenseSetOperationAttr is an optional argument to DenseToDenseSetOperation. +type DenseToDenseSetOperationAttr func(optionalAttr) + +// DenseToDenseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func DenseToDenseSetOperationValidateIndices(value bool) DenseToDenseSetOperationAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Applies set operation along last dimension of 2 `Tensor` inputs. +// +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// set2: `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`. +// Dimension `n` contains values in a set, duplicates are allowed but ignored. +// +// +// Returns: +// result_indices: 2D indices of a `SparseTensor`. +// result_values: 1D values of a `SparseTensor`. +// result_shape: 1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func DenseToDenseSetOperation(scope *Scope, set1 tf.Output, set2 tf.Output, set_operation string, optional ...DenseToDenseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DenseToDenseSetOperation", + Input: []tf.Input{ + set1, set2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the set of files matching one or more glob patterns. +// +// Note that this routine only supports wildcard characters in the +// basename portion of the pattern, not in the directory portion. +// Note also that the order of filenames returned is deterministic. +// +// Arguments: +// pattern: Shell wildcard pattern(s). Scalar or vector of type string. +// +// Returns A vector of matching filenames. +func MatchingFiles(scope *Scope, pattern tf.Output) (filenames tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatchingFiles", + Input: []tf.Input{ + pattern, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns element-wise remainder of division. When `x < 0` xor `y < 0` is +// +// true, this follows Python semantics in that the result here is consistent +// with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. +// +// *NOTE*: `FloorMod` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func FloorMod(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FloorMod", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Slice a `SparseTensor` based on the `start` and `size`. +// +// For example, if the input is +// +// input_tensor = shape = [2, 7] +// [ a d e ] +// [b c ] +// +// Graphically the output tensors are: +// +// sparse_slice([0, 0], [2, 4]) = shape = [2, 4] +// [ a ] +// [b c ] +// +// sparse_slice([0, 4], [2, 3]) = shape = [2, 3] +// [ d e ] +// [ ] +// +// Arguments: +// indices: 2-D tensor represents the indices of the sparse tensor. +// values: 1-D tensor represents the values of the sparse tensor. +// shape: 1-D. tensor represents the shape of the sparse tensor. +// start: 1-D. tensor represents the start of the slice. +// size: 1-D. tensor represents the size of the slice. +// output indices: A list of 1-D tensors represents the indices of the output +// sparse tensors. +// +// Returns: +// output_indices +// output_values: A list of 1-D tensors represents the values of the output sparse +// tensors. +// output_shape: A list of 1-D tensors represents the shape of the output sparse +// tensors. +func SparseSlice(scope *Scope, indices tf.Output, values tf.Output, shape tf.Output, start tf.Output, size tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSlice", + Input: []tf.Input{ + indices, values, shape, start, size, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// SparseMatrixSparseMatMulAttr is an optional argument to SparseMatrixSparseMatMul. +type SparseMatrixSparseMatMulAttr func(optionalAttr) + +// SparseMatrixSparseMatMulTransposeA sets the optional transpose_a attribute to value. +// +// value: Indicates whether `a` should be transposed. +// If not specified, defaults to false +func SparseMatrixSparseMatMulTransposeA(value bool) SparseMatrixSparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatrixSparseMatMulTransposeB sets the optional transpose_b attribute to value. +// +// value: Indicates whether `b` should be transposed. +// If not specified, defaults to false +func SparseMatrixSparseMatMulTransposeB(value bool) SparseMatrixSparseMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatrixSparseMatMulAdjointA sets the optional adjoint_a attribute to value. +// +// value: Indicates whether `a` should be conjugate-transposed. +// If not specified, defaults to false +func SparseMatrixSparseMatMulAdjointA(value bool) SparseMatrixSparseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_a"] = value + } +} + +// SparseMatrixSparseMatMulAdjointB sets the optional adjoint_b attribute to value. +// +// value: Indicates whether `b` should be conjugate-transposed. +// If not specified, defaults to false +func SparseMatrixSparseMatMulAdjointB(value bool) SparseMatrixSparseMatMulAttr { + return func(m optionalAttr) { + m["adjoint_b"] = value + } +} + +// Sparse-matrix-multiplies two CSR matrices `a` and `b`. +// +// Performs a matrix multiplication of a sparse matrix `a` with a sparse matrix +// `b`; returns a sparse matrix `a * b`, unless either `a` or `b` is transposed or +// adjointed. +// +// Each matrix may be transposed or adjointed (conjugated and transposed) +// according to the Boolean parameters `transpose_a`, `adjoint_a`, `transpose_b` +// and `adjoint_b`. At most one of `transpose_a` or `adjoint_a` may be True. +// Similarly, at most one of `transpose_b` or `adjoint_b` may be True. +// +// The inputs must have compatible shapes. That is, the inner dimension of `a` +// must be equal to the outer dimension of `b`. This requirement is adjusted +// according to whether either `a` or `b` is transposed or adjointed. +// +// The `type` parameter denotes the type of the matrix elements. Both `a` and `b` +// must have the same type. The supported types are: `float32`, `float64`, +// `complex64` and `complex128`. +// +// Both `a` and `b` must have the same rank. Broadcasting is not supported. If they +// have rank 3, each batch of 2D CSRSparseMatrices within `a` and `b` must have the +// same dense shape. +// +// The sparse matrix product may have numeric (non-structural) zeros. +// TODO(anudhyan): Consider adding a boolean attribute to control whether to prune +// zeros. +// +// Usage example: +// +// ```python +// from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops +// +// a_indices = np.array([[0, 0], [2, 3], [2, 4], [3, 0]]) +// a_values = np.array([1.0, 5.0, -1.0, -2.0], np.float32) +// a_dense_shape = [4, 5] +// +// b_indices = np.array([[0, 0], [3, 0], [3, 1]]) +// b_values = np.array([2.0, 7.0, 8.0], np.float32) +// b_dense_shape = [5, 3] +// +// with tf.Session() as sess: +// # Define (COO format) Sparse Tensors over Numpy arrays +// a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape) +// b_st = tf.sparse.SparseTensor(b_indices, b_values, b_dense_shape) +// +// # Convert SparseTensors to CSR SparseMatrix +// a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix( +// a_st.indices, a_st.values, a_st.dense_shape) +// b_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix( +// b_st.indices, b_st.values, b_st.dense_shape) +// +// # Compute the CSR SparseMatrix matrix multiplication +// c_sm = sparse_csr_matrix_ops.sparse_matrix_sparse_mat_mul( +// a=a_sm, b=b_sm, type=tf.float32) +// +// # Convert the CSR SparseMatrix product to a dense Tensor +// c_sm_dense = sparse_csr_matrix_ops.csr_sparse_matrix_to_dense( +// c_sm, tf.float32) +// # Evaluate the dense Tensor value +// c_sm_dense_value = sess.run(c_sm_dense) +// ``` +// +// `c_sm_dense_value` stores the dense matrix product: +// +// ``` +// [[ 2. 0. 0.] +// [ 0. 0. 0.] +// [ 35. 40. 0.] +// [ -4. 0. 0.]] +// ``` +// +// a: A `CSRSparseMatrix`. +// b: A `CSRSparseMatrix` with the same type and rank as `a`. +// type: The type of both `a` and `b`. +// transpose_a: If True, `a` transposed before multiplication. +// transpose_b: If True, `b` transposed before multiplication. +// adjoint_a: If True, `a` adjointed before multiplication. +// adjoint_b: If True, `b` adjointed before multiplication. +// +// Arguments: +// a: A CSRSparseMatrix. +// b: A CSRSparseMatrix. +// +// +// Returns A CSRSparseMatrix. +func SparseMatrixSparseMatMul(scope *Scope, a tf.Output, b tf.Output, type_ tf.DataType, optional ...SparseMatrixSparseMatMulAttr) (c tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatrixSparseMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CopyHostAttr is an optional argument to CopyHost. +type CopyHostAttr func(optionalAttr) + +// CopyHostTensorName sets the optional tensor_name attribute to value. +// +// value: The name of the input tensor. +// If not specified, defaults to "" +func CopyHostTensorName(value string) CopyHostAttr { + return func(m optionalAttr) { + m["tensor_name"] = value + } +} + +// CopyHostDebugOpsSpec sets the optional debug_ops_spec attribute to value. +// +// value: A list of debug op spec (op, url, gated_grpc) for attached debug +// ops. Each element of the list has the format +// ;;, wherein gated_grpc is boolean represented +// as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1", +// "DebugIdentity;file:///tmp/tfdbg_1;0". +// If not specified, defaults to <> +func CopyHostDebugOpsSpec(value []string) CopyHostAttr { + return func(m optionalAttr) { + m["debug_ops_spec"] = value + } +} + +// Copy a tensor to host. +// +// Performs CPU-to-CPU deep-copying of tensor. +// N.B.: If the all downstream attached debug ops are disabled given the current +// gRPC gating status, the output will simply forward the input tensor without +// deep-copying. See the documentation of Debug* ops for more details. +// +// Unlike the Copy Op, this op has HostMemory constraint on its input or output. +// +// Arguments: +// input: Input tensor. +func CopyHost(scope *Scope, input tf.Output, optional ...CopyHostAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CopyHost", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Sparse addition of two CSR matrices, C = alpha * A + beta * B. +// +// The gradients of SparseMatrixAdd outputs with respect to alpha and beta are not +// currently defined (TensorFlow will return zeros for these entries). +// +// Arguments: +// a: A CSRSparseMatrix. +// b: A CSRSparseMatrix. +// alpha: A constant scalar. +// beta: A constant scalar. +// +// Returns A CSRSparseMatrix. +func SparseMatrixAdd(scope *Scope, a tf.Output, b tf.Output, alpha tf.Output, beta tf.Output) (c tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseMatrixAdd", + Input: []tf.Input{ + a, b, alpha, beta, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseMatrixMatMulAttr is an optional argument to SparseMatrixMatMul. +type SparseMatrixMatMulAttr func(optionalAttr) + +// SparseMatrixMatMulTransposeA sets the optional transpose_a attribute to value. +// +// value: Indicates whether `a` should be transposed. +// If not specified, defaults to false +func SparseMatrixMatMulTransposeA(value bool) SparseMatrixMatMulAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// SparseMatrixMatMulTransposeB sets the optional transpose_b attribute to value. +// +// value: Indicates whether `b` should be transposed. +// If not specified, defaults to false +func SparseMatrixMatMulTransposeB(value bool) SparseMatrixMatMulAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// SparseMatrixMatMulAdjointA sets the optional adjoint_a attribute to value. +// +// value: Indicates whether `a` should be conjugate-transposed. +// If not specified, defaults to false +func SparseMatrixMatMulAdjointA(value bool) SparseMatrixMatMulAttr { + return func(m optionalAttr) { + m["adjoint_a"] = value + } +} + +// SparseMatrixMatMulAdjointB sets the optional adjoint_b attribute to value. +// +// value: Indicates whether `b` should be conjugate-transposed. +// If not specified, defaults to false +func SparseMatrixMatMulAdjointB(value bool) SparseMatrixMatMulAttr { + return func(m optionalAttr) { + m["adjoint_b"] = value + } +} + +// SparseMatrixMatMulTransposeOutput sets the optional transpose_output attribute to value. +// +// value: Transposes the product of `a` and `b`. +// If not specified, defaults to false +func SparseMatrixMatMulTransposeOutput(value bool) SparseMatrixMatMulAttr { + return func(m optionalAttr) { + m["transpose_output"] = value + } +} + +// SparseMatrixMatMulConjugateOutput sets the optional conjugate_output attribute to value. +// +// value: Conjugates the product of `a` and `b`. +// If not specified, defaults to false +func SparseMatrixMatMulConjugateOutput(value bool) SparseMatrixMatMulAttr { + return func(m optionalAttr) { + m["conjugate_output"] = value + } +} + +// Matrix-multiplies a sparse matrix with a dense matrix. +// +// Returns a dense matrix. +// For inputs A and B, where A is CSR and B is dense; this op returns a dense C; +// +// If transpose_output is false, returns: +// ``` +// C = A . B +// ``` +// +// If transpose_output is `true`, returns: +// ``` +// C = transpose(A . B) = transpose(B) . transpose(A) +// ``` +// where the transposition is performed along the two innermost (matrix) +// dimensions. +// +// If conjugate_output is `true`, returns: +// ``` +// C = conjugate(A . B) = conjugate(A) . conjugate(B) +// ``` +// +// If both conjugate_output and transpose_output are `true`, returns: +// ``` +// C = conjugate(transpose(A . B)) = conjugate(transpose(B)) . +// conjugate(transpose(A)) +// ``` +// +// Arguments: +// a: A CSRSparseMatrix. +// b: A dense tensor. +// +// Returns A dense output tensor. +func SparseMatrixMatMul(scope *Scope, a tf.Output, b tf.Output, optional ...SparseMatrixMatMulAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatrixMatMul", + Input: []tf.Input{ + a, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reads out the CSR components at batch `index`. +// +// This op is meant only for debugging / testing, and its interface is not expected +// to be stable. +// +// Arguments: +// csr_sparse_matrix: A batched CSRSparseMatrix. +// index: The index in `csr_sparse_matrix`'s batch. +// +// +// Returns: +// row_ptrs: An array containing CSR matrix row pointers. +// col_inds: An array containing CSR matrix column indices. +// values: An array containing CSR matrix nonzero values. +func CSRSparseMatrixComponents(scope *Scope, csr_sparse_matrix tf.Output, index tf.Output, type_ tf.DataType) (row_ptrs tf.Output, col_inds tf.Output, values tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "CSRSparseMatrixComponents", + Input: []tf.Input{ + csr_sparse_matrix, index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StringSplitV2Attr is an optional argument to StringSplitV2. +type StringSplitV2Attr func(optionalAttr) + +// StringSplitV2Maxsplit sets the optional maxsplit attribute to value. +// +// value: An `int`. If `maxsplit > 0`, limit of the split of the result. +// If not specified, defaults to -1 +func StringSplitV2Maxsplit(value int64) StringSplitV2Attr { + return func(m optionalAttr) { + m["maxsplit"] = value + } +} + +// Split elements of `source` based on `sep` into a `SparseTensor`. +// +// Let N be the size of source (typically N will be the batch size). Split each +// element of `source` based on `sep` and return a `SparseTensor` +// containing the split tokens. Empty tokens are ignored. +// +// For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', +// then the output will be +// ``` +// st.indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// st.shape = [2, 3] +// st.values = ['hello', 'world', 'a', 'b', 'c'] +// ``` +// +// If `sep` is given, consecutive delimiters are not grouped together and are +// deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and +// sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty +// string, consecutive whitespace are regarded as a single separator, and the +// result will contain no empty strings at the startor end if the string has +// leading or trailing whitespace. +// +// Note that the above mentioned behavior matches python's str.split. +// +// Arguments: +// input: `1-D` string `Tensor`, the strings to split. +// sep: `0-D` string `Tensor`, the delimiter character. +func StringSplitV2(scope *Scope, input tf.Output, sep tf.Output, optional ...StringSplitV2Attr) (indices tf.Output, values tf.Output, shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringSplitV2", + Input: []tf.Input{ + input, sep, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Compute the lower regularized incomplete Gamma function `P(a, x)`. +// +// The lower regularized incomplete Gamma function is defined as: +// +// +// \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\) +// +// where +// +// \\(gamma(a, x) = \\int_{0}^{x} t^{a-1} exp(-t) dt\\) +// +// is the lower incomplete Gamma function. +// +// Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete +// Gamma function. +func Igamma(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Igamma", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Convert a (possibly batched) CSRSparseMatrix to dense. +// +// Arguments: +// sparse_input: A batched CSRSparseMatrix. +// +// +// Returns A dense tensor. +func CSRSparseMatrixToDense(scope *Scope, sparse_input tf.Output, type_ tf.DataType) (dense_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "CSRSparseMatrixToDense", + Input: []tf.Input{ + sparse_input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Add all input tensors element wise. +// +// Inputs must be of same size and shape. +// +// ```python +// x = [9, 7, 10] +// tf.math.add_n(x) ==> 26 +// ``` +func AddN(scope *Scope, inputs []tf.Output) (sum tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AddN", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts a (possibly batched) CSRSparesMatrix to a SparseTensor. +// +// Arguments: +// sparse_matrix: A (possibly batched) CSRSparseMatrix. +// +// +// Returns: +// indices: SparseTensor indices. +// values: SparseTensor values. +// dense_shape: SparseTensor dense shape. +func CSRSparseMatrixToSparseTensor(scope *Scope, sparse_matrix tf.Output, type_ tf.DataType) (indices tf.Output, values tf.Output, dense_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "CSRSparseMatrixToSparseTensor", + Input: []tf.Input{ + sparse_matrix, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// SizeAttr is an optional argument to Size. +type SizeAttr func(optionalAttr) + +// SizeOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_INT32 +func SizeOutType(value tf.DataType) SizeAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Returns the size of a tensor. +// +// This operation returns an integer representing the number of elements in +// `input`. +// +// For example: +// +// ``` +// # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]] +// size(t) ==> 12 +// ``` +func Size(scope *Scope, input tf.Output, optional ...SizeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Size", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TPUReplicatedInputAttr is an optional argument to TPUReplicatedInput. +type TPUReplicatedInputAttr func(optionalAttr) + +// TPUReplicatedInputIsMirroredVariable sets the optional is_mirrored_variable attribute to value. +// If not specified, defaults to false +func TPUReplicatedInputIsMirroredVariable(value bool) TPUReplicatedInputAttr { + return func(m optionalAttr) { + m["is_mirrored_variable"] = value + } +} + +// TPUReplicatedInputIndex sets the optional index attribute to value. +// If not specified, defaults to -1 +func TPUReplicatedInputIndex(value int64) TPUReplicatedInputAttr { + return func(m optionalAttr) { + m["index"] = value + } +} + +// TPUReplicatedInputIsPacked sets the optional is_packed attribute to value. +// If not specified, defaults to false +func TPUReplicatedInputIsPacked(value bool) TPUReplicatedInputAttr { + return func(m optionalAttr) { + m["is_packed"] = value + } +} + +// Connects N inputs to an N-way replicated TPU computation. +// +// This operation holds a replicated input to a `tpu.replicate()` computation subgraph. +// Each replicated input has the same shape and type alongside the output. +// +// For example: +// ``` +// %a = "tf.opA"() +// %b = "tf.opB"() +// %replicated_input = "tf.TPUReplicatedInput"(%a, %b) +// %computation = "tf.Computation"(%replicated_input) +// ``` +// The above computation has a replicated input of two replicas. +func TPUReplicatedInput(scope *Scope, inputs []tf.Output, optional ...TPUReplicatedInputAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TPUReplicatedInput", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a tensor filled with a scalar value. +// +// This operation creates a tensor of shape `dims` and fills it with `value`. +// +// For example: +// +// ``` +// # Output tensor has shape [2, 3]. +// fill([2, 3], 9) ==> [[9, 9, 9] +// [9, 9, 9]] +// ``` +// +// `tf.fill` differs from `tf.constant` in a few ways: +// +// * `tf.fill` only supports scalar contents, whereas `tf.constant` supports +// Tensor values. +// * `tf.fill` creates an Op in the computation graph that constructs the actual +// Tensor value at runtime. This is in contrast to `tf.constant` which embeds +// the entire Tensor into the graph with a `Const` node. +// * Because `tf.fill` evaluates at graph runtime, it supports dynamic shapes +// based on other runtime Tensors, unlike `tf.constant`. +// +// Arguments: +// dims: 1-D. Represents the shape of the output tensor. +// value: 0-D (scalar). Value to fill the returned tensor. +// +// @compatibility(numpy) +// Equivalent to np.full +// @end_compatibility +func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Fill", + Input: []tf.Input{ + dims, value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts a dense tensor to a (possibly batched) CSRSparseMatrix. +// +// Arguments: +// dense_input: A Dense tensor. +// indices: Indices of nonzero elements. +// +// Returns A (possibly batched) CSRSparseMatrix. +func DenseToCSRSparseMatrix(scope *Scope, dense_input tf.Output, indices tf.Output) (sparse_output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DenseToCSRSparseMatrix", + Input: []tf.Input{ + dense_input, indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Fills empty rows in the input 2-D `SparseTensor` with a default value. +// +// The input `SparseTensor` is represented via the tuple of inputs +// (`indices`, `values`, `dense_shape`). The output `SparseTensor` has the +// same `dense_shape` but with indices `output_indices` and values +// `output_values`. +// +// This op inserts a single entry for every row that doesn't have any values. +// The index is created as `[row, 0, ..., 0]` and the inserted value +// is `default_value`. +// +// For example, suppose `sp_input` has shape `[5, 6]` and non-empty values: +// +// [0, 1]: a +// [0, 3]: b +// [2, 0]: c +// [3, 1]: d +// +// Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values: +// +// [0, 1]: a +// [0, 3]: b +// [1, 0]: default_value +// [2, 0]: c +// [3, 1]: d +// [4, 0]: default_value +// +// The output `SparseTensor` will be in row-major order and will have the +// same shape as the input. +// +// This op also returns an indicator vector shaped `[dense_shape[0]]` such that +// +// empty_row_indicator[i] = True iff row i was an empty row. +// +// And a reverse index map vector shaped `[indices.shape[0]]` that is used during +// backpropagation, +// +// reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :] +// +// Arguments: +// indices: 2-D. the indices of the sparse tensor. +// values: 1-D. the values of the sparse tensor. +// dense_shape: 1-D. the shape of the sparse tensor. +// default_value: 0-D. default value to insert into location `[row, 0, ..., 0]` +// for rows missing from the input sparse tensor. +// output indices: 2-D. the indices of the filled sparse tensor. +// +// Returns: +// output_indices +// output_values: 1-D. the values of the filled sparse tensor. +// empty_row_indicator: 1-D. whether the dense row was missing in the +// input sparse tensor. +// reverse_index_map: 1-D. a map from the input indices to the output indices. +func SparseFillEmptyRows(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output, default_value tf.Output) (output_indices tf.Output, output_values tf.Output, empty_row_indicator tf.Output, reverse_index_map tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseFillEmptyRows", + Input: []tf.Input{ + indices, values, dense_shape, default_value, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Makes the summary of quantiles for the batch. +// +// An op that takes a list of tensors (one tensor per feature) and outputs the +// quantile summaries for each tensor. +// +// Arguments: +// float_values: float; List of Rank 1 Tensors each containing values for a single feature. +// example_weights: float; Rank 1 Tensor with weights per instance. +// epsilon: float; The required maximum approximation error. +// +// Returns float; List of Rank 2 Tensors each containing the quantile summary +// (value, weight, min_rank, max_rank) of a single feature. +func BoostedTreesMakeQuantileSummaries(scope *Scope, float_values []tf.Output, example_weights tf.Output, epsilon tf.Output) (summaries []tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesMakeQuantileSummaries", + Input: []tf.Input{ + tf.OutputList(float_values), example_weights, epsilon, + }, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if summaries, idx, err = makeOutputList(op, idx, "summaries"); err != nil { + scope.UpdateErr("BoostedTreesMakeQuantileSummaries", err) + return + } + return summaries +} + +// TakeManySparseFromTensorsMapAttr is an optional argument to TakeManySparseFromTensorsMap. +type TakeManySparseFromTensorsMapAttr func(optionalAttr) + +// TakeManySparseFromTensorsMapContainer sets the optional container attribute to value. +// +// value: The container name for the `SparseTensorsMap` read by this op. +// If not specified, defaults to "" +func TakeManySparseFromTensorsMapContainer(value string) TakeManySparseFromTensorsMapAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// TakeManySparseFromTensorsMapSharedName sets the optional shared_name attribute to value. +// +// value: The shared name for the `SparseTensorsMap` read by this op. +// It should not be blank; rather the `shared_name` or unique Operation name +// of the Op that created the original `SparseTensorsMap` should be used. +// If not specified, defaults to "" +func TakeManySparseFromTensorsMapSharedName(value string) TakeManySparseFromTensorsMapAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. +// +// The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where +// `N` is the minibatch size and the rows correspond to the output handles of +// `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the +// original `SparseTensor` objects that went into the given input ops must all +// match. When the final `SparseTensor` is created, it has rank one +// higher than the ranks of the incoming `SparseTensor` objects +// (they have been concatenated along a new row dimension on the left). +// +// The output `SparseTensor` object's shape values for all dimensions but the +// first are the max across the input `SparseTensor` objects' shape values +// for the corresponding dimensions. Its first shape value is `N`, the minibatch +// size. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the handles represent an input, which is a `[2, 3]` matrix +// representing two original `SparseTensor` objects: +// +// ``` +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// ``` +// +// and +// +// ``` +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// ``` +// +// then the final `SparseTensor` will be: +// +// ``` +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// ``` +// +// Arguments: +// sparse_handles: 1-D, The `N` serialized `SparseTensor` objects. +// Shape: `[N]`. +// dtype: The `dtype` of the `SparseTensor` objects stored in the +// `SparseTensorsMap`. +// +// Returns: +// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. +// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. +func TakeManySparseFromTensorsMap(scope *Scope, sparse_handles tf.Output, dtype tf.DataType, optional ...TakeManySparseFromTensorsMapAttr) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TakeManySparseFromTensorsMap", + Input: []tf.Input{ + sparse_handles, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// LoadTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to LoadTPUEmbeddingCenteredRMSPropParameters. +type LoadTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingCenteredRMSPropParametersTableId(value int64) LoadTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingCenteredRMSPropParametersTableName(value string) LoadTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingCenteredRMSPropParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingCenteredRMSPropParametersConfig(value string) LoadTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load centered RMSProp embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the centered RMSProp optimization algorithm. +// ms: Value of ms used in the centered RMSProp optimization algorithm. +// mom: Value of mom used in the centered RMSProp optimization algorithm. +// mg: Value of mg used in the centered RMSProp optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingCenteredRMSPropParameters(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingCenteredRMSPropParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingCenteredRMSPropParameters", + Input: []tf.Input{ + parameters, ms, mom, mg, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// RandomPoissonAttr is an optional argument to RandomPoisson. +type RandomPoissonAttr func(optionalAttr) + +// RandomPoissonSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func RandomPoissonSeed(value int64) RandomPoissonAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomPoissonSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func RandomPoissonSeed2(value int64) RandomPoissonAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Use RandomPoissonV2 instead. +// +// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2 +func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomPoisson", + Input: []tf.Input{ + shape, rate, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Compute the regularized incomplete beta integral \\(I_x(a, b)\\). +// +// The regularized incomplete beta integral is defined as: +// +// +// \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\) +// +// where +// +// +// \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\) +// +// +// is the incomplete beta function and \\(B(a, b)\\) is the *complete* +// beta function. +func Betainc(scope *Scope, a tf.Output, b tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Betainc", + Input: []tf.Input{ + a, b, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Reduces sparse updates into the variable referenced by `resource` using the `max` operation. +// +// This operation computes +// +// # Scalar indices +// ref[indices, ...] = max(ref[indices, ...], updates[...]) +// +// # Vector indices (for each i) +// ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...]) +// +// # High rank indices (for each i, ..., j) +// ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...]) +// +// Duplicate entries are handled correctly: if multiple `indices` reference +// the same location, their contributions are combined. +// +// Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`. +// +//
+// +//
+// +// Arguments: +// resource: Should be from a `Variable` node. +// indices: A tensor of indices into the first dimension of `ref`. +// updates: A tensor of updated values to add to `ref`. +// +// Returns the created operation. +func ResourceScatterMax(scope *Scope, resource tf.Output, indices tf.Output, updates tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceScatterMax", + Input: []tf.Input{ + resource, indices, updates, + }, + } + return scope.AddOperation(opspec) +} + +// AddManySparseToTensorsMapAttr is an optional argument to AddManySparseToTensorsMap. +type AddManySparseToTensorsMapAttr func(optionalAttr) + +// AddManySparseToTensorsMapContainer sets the optional container attribute to value. +// +// value: The container name for the `SparseTensorsMap` created by this op. +// If not specified, defaults to "" +func AddManySparseToTensorsMapContainer(value string) AddManySparseToTensorsMapAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// AddManySparseToTensorsMapSharedName sets the optional shared_name attribute to value. +// +// value: The shared name for the `SparseTensorsMap` created by this op. +// If blank, the new Operation's unique name is used. +// If not specified, defaults to "" +func AddManySparseToTensorsMapSharedName(value string) AddManySparseToTensorsMapAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles. +// +// A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`, +// `sparse_values`, and `sparse_shape`, where +// +// ```sparse_indices.shape[1] == sparse_shape.shape[0] == R``` +// +// An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor` +// having a first `sparse_indices` column taking values between `[0, N)`, where +// the minibatch size `N == sparse_shape[0]`. +// +// The input `SparseTensor` must have rank `R` greater than 1, and the first +// dimension is treated as the minibatch dimension. Elements of the `SparseTensor` +// must be sorted in increasing order of this first dimension. The stored +// `SparseTensor` objects pointed to by each row of the output `sparse_handles` +// will have rank `R-1`. +// +// The `SparseTensor` values can then be read out as part of a minibatch by passing +// the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure +// the correct `SparseTensorsMap` is accessed, ensure that the same +// `container` and `shared_name` are passed to that Op. If no `shared_name` +// is provided here, instead use the *name* of the Operation created by calling +// `AddManySparseToTensorsMap` as the `shared_name` passed to +// `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. +// `sparse_indices[:, 0]` must be ordered values in `[0, N)`. +// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. +// The minibatch size `N == sparse_shape[0]`. +// +// Returns 1-D. The handles of the `SparseTensor` now stored in the +// `SparseTensorsMap`. Shape: `[N]`. +func AddManySparseToTensorsMap(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...AddManySparseToTensorsMapAttr) (sparse_handles tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AddManySparseToTensorsMap", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes tan of x element-wise. +// +// Given an input tensor, this function computes tangent of every +// element in the tensor. Input range is `(-inf, inf)` and +// output range is `(-inf, inf)`. If input lies outside the boundary, `nan` +// is returned. +// +// ```python +// x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")]) +// tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan] +// ``` +func Tan(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Tan", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BiasAddGradAttr is an optional argument to BiasAddGrad. +type BiasAddGradAttr func(optionalAttr) + +// BiasAddGradDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the bias tensor will be added to the last dimension +// of the value tensor. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// The tensor will be added to "in_channels", the third-to-the-last +// dimension. +// If not specified, defaults to "NHWC" +func BiasAddGradDataFormat(value string) BiasAddGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// The backward operation for "BiasAdd" on the "bias" tensor. +// +// It accumulates all the values from out_backprop into the feature dimension. +// For NHWC data format, the feature dimension is the last. For NCHW data format, +// the feature dimension is the third-to-last. +// +// Arguments: +// out_backprop: Any number of dimensions. +// +// Returns 1-D with size the feature dimension of `out_backprop`. +func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BiasAddGrad", + Input: []tf.Input{ + out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Advance the counter of a counter-based RNG. +// +// The state of the RNG after +// `rng_skip(n)` will be the same as that after `stateful_uniform([n])` +// (or any other distribution). The actual increment added to the +// counter is an unspecified implementation detail. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// delta: The amount of advancement. +// +// Returns the created operation. +func RngSkip(scope *Scope, resource tf.Output, algorithm tf.Output, delta tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RngSkip", + Input: []tf.Input{ + resource, algorithm, delta, + }, + } + return scope.AddOperation(opspec) +} + +// Generates values in an interval. +// +// A sequence of `num` evenly-spaced values are generated beginning at `start`. +// If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, +// so that the last one is exactly `stop`. +// +// For example: +// +// ``` +// tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] +// ``` +// +// Arguments: +// start: 0-D tensor. First entry in the range. +// stop: 0-D tensor. Last entry in the range. +// num: 0-D tensor. Number of values to generate. +// +// Returns 1-D. The generated values. +func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LinSpace", + Input: []tf.Input{ + start, stop, num, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MultinomialAttr is an optional argument to Multinomial. +type MultinomialAttr func(optionalAttr) + +// MultinomialSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 is set to be non-zero, the internal random number +// generator is seeded by the given seed. Otherwise, a random seed is used. +// If not specified, defaults to 0 +func MultinomialSeed(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// MultinomialSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func MultinomialSeed2(value int64) MultinomialAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// MultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func MultinomialOutputDtype(value tf.DataType) MultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. +// +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func Multinomial(scope *Scope, logits tf.Output, num_samples tf.Output, optional ...MultinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Multinomial", + Input: []tf.Input{ + logits, num_samples, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// NonDeterministicIntsAttr is an optional argument to NonDeterministicInts. +type NonDeterministicIntsAttr func(optionalAttr) + +// NonDeterministicIntsDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_INT64 +func NonDeterministicIntsDtype(value tf.DataType) NonDeterministicIntsAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Non-deterministically generates some integers. +// +// This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results. +// +// Arguments: +// shape: The shape of the output tensor. +// +// Returns Non-deterministic integer values with specified shape. +func NonDeterministicInts(scope *Scope, shape tf.Output, optional ...NonDeterministicIntsAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NonDeterministicInts", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that caches elements from `input_dataset`. +// +// A CacheDataset will iterate over the input_dataset, and store tensors. If the +// cache already exists, the cache will be used. If the cache is inappropriate +// (e.g. cannot be opened, contains tensors of the wrong shape / size), an error +// will the returned when used. +// +// Arguments: +// +// filename: A path on the filesystem where we should cache the dataset. Note: this +// will be a directory. +// +// +func CacheDataset(scope *Scope, input_dataset tf.Output, filename tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "CacheDataset", + Input: []tf.Input{ + input_dataset, filename, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ThreadPoolHandleAttr is an optional argument to ThreadPoolHandle. +type ThreadPoolHandleAttr func(optionalAttr) + +// ThreadPoolHandleMaxIntraOpParallelism sets the optional max_intra_op_parallelism attribute to value. +// +// value: The maximum degree of parallelism to use within operations that execute on this +// threadpool. +// If not specified, defaults to 1 +func ThreadPoolHandleMaxIntraOpParallelism(value int64) ThreadPoolHandleAttr { + return func(m optionalAttr) { + m["max_intra_op_parallelism"] = value + } +} + +// ThreadPoolHandleContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func ThreadPoolHandleContainer(value string) ThreadPoolHandleAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// ThreadPoolHandleSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func ThreadPoolHandleSharedName(value string) ThreadPoolHandleAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// num_threads: The number of threads in the thread pool. +// display_name: A human-readable name for the threads that may be visible in some +// visualizations. +// threadpool. +// +// Returns A resource that can be consumed by one or more ExperimentalThreadPoolDataset +// ops. +func ThreadPoolHandle(scope *Scope, num_threads int64, display_name string, optional ...ThreadPoolHandleAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_threads": num_threads, "display_name": display_name} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ThreadPoolHandle", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseReduceMaxSparseAttr is an optional argument to SparseReduceMaxSparse. +type SparseReduceMaxSparseAttr func(optionalAttr) + +// SparseReduceMaxSparseKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceMaxSparseKeepDims(value bool) SparseReduceMaxSparseAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the max of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_max()`. In contrast to SparseReduceMax, this Op returns a +// SparseTensor. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +func SparseReduceMaxSparse(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxSparseAttr) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceMaxSparse", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes the maximum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// This operator is similar to the unsorted segment sum operator found +// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). +// Instead of computing the sum over segments, it computes the maximum such that: +// +// \\(output_i = \max_{j...} data[j...]\\) where max is over tuples `j...` such +// that `segment_ids[j...] == i`. +// +// If the maximum is empty for a given segment ID `i`, it outputs the smallest +// possible value for the specific numeric type, +// `output[i] = numeric_limits::lowest()`. +// +// If the given segment ID `i` is negative, then the corresponding value is +// dropped, and will not be included in the result. +// +//
+// +//
+// +// For example: +// +// ``` python +// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) +// tf.unsorted_segment_max(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 4, 3, 3, 4], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A tensor whose shape is a prefix of `data.shape`. +// +// +// Returns Has same shape as data, except for the first `segment_ids.rank` +// dimensions, which are replaced with a single dimension which has size +// `num_segments`. +func UnsortedSegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnsortedSegmentMax", + Input: []tf.Input{ + data, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringUpperAttr is an optional argument to StringUpper. +type StringUpperAttr func(optionalAttr) + +// StringUpperEncoding sets the optional encoding attribute to value. +// If not specified, defaults to "" +func StringUpperEncoding(value string) StringUpperAttr { + return func(m optionalAttr) { + m["encoding"] = value + } +} + +// Converts all lowercase characters into their respective uppercase replacements. +// +// Example: +// +// >>> tf.strings.upper("CamelCase string and ALL CAPS") +// +// +func StringUpper(scope *Scope, input tf.Output, optional ...StringUpperAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringUpper", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Set a summary_writer_interface to record statistics using given stats_aggregator. +// +// Returns the created operation. +func StatsAggregatorSetSummaryWriter(scope *Scope, stats_aggregator tf.Output, summary tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatsAggregatorSetSummaryWriter", + Input: []tf.Input{ + stats_aggregator, summary, + }, + } + return scope.AddOperation(opspec) +} + +// FusedBatchNormGradAttr is an optional argument to FusedBatchNormGrad. +type FusedBatchNormGradAttr func(optionalAttr) + +// FusedBatchNormGradEpsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormGradEpsilon(value float32) FusedBatchNormGradAttr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormGradDataFormat sets the optional data_format attribute to value. +// +// value: The data format for y_backprop, x, x_backprop. +// Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormGradDataFormat(value string) FusedBatchNormGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormGradIsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormGradIsTraining(value bool) FusedBatchNormGradAttr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Gradient for batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// y_backprop: A 4D Tensor for the gradient with respect to y. +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +// mean to be reused in gradient computation. When is_training is +// False, a 1D Tensor for the population mean to be reused in both +// 1st and 2nd order gradient computation. +// reserve_space_2: When is_training is True, a 1D Tensor for the computed batch +// variance (inverted variance in the cuDNN case) to be reused in +// gradient computation. When is_training is False, a 1D Tensor +// for the population variance to be reused in both 1st and 2nd +// order gradient computation. +// +// Returns: +// x_backprop: A 4D Tensor for the gradient with respect to x. +// scale_backprop: A 1D Tensor for the gradient with respect to scale. +// offset_backprop: A 1D Tensor for the gradient with respect to offset. +// reserve_space_3: Unused placeholder to match the mean input in FusedBatchNorm. +// reserve_space_4: Unused placeholder to match the variance input +// in FusedBatchNorm. +func FusedBatchNormGrad(scope *Scope, y_backprop tf.Output, x tf.Output, scale tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, optional ...FusedBatchNormGradAttr) (x_backprop tf.Output, scale_backprop tf.Output, offset_backprop tf.Output, reserve_space_3 tf.Output, reserve_space_4 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormGrad", + Input: []tf.Input{ + y_backprop, x, scale, reserve_space_1, reserve_space_2, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// Subtracts a value from the current value of a variable. +// +// Any ReadVariableOp with a control dependency on this op is guaranteed to +// see the decremented value or a subsequent newer one. +// +// Arguments: +// resource: handle to the resource in which to store the variable. +// value: the value by which the variable will be incremented. +// +// Returns the created operation. +func AssignSubVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AssignSubVariableOp", + Input: []tf.Input{ + resource, value, + }, + } + return scope.AddOperation(opspec) +} + +// SparseReduceMaxAttr is an optional argument to SparseReduceMax. +type SparseReduceMaxAttr func(optionalAttr) + +// SparseReduceMaxKeepDims sets the optional keep_dims attribute to value. +// +// value: If true, retain reduced dimensions with length 1. +// If not specified, defaults to false +func SparseReduceMaxKeepDims(value bool) SparseReduceMaxAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// Computes the max of elements across dimensions of a SparseTensor. +// +// This Op takes a SparseTensor and is the sparse counterpart to +// `tf.reduce_max()`. In particular, this Op also returns a dense `Tensor` +// instead of a sparse one. +// +// Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless +// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in +// `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained +// with length 1. +// +// If `reduction_axes` has no entries, all dimensions are reduced, and a tensor +// with a single element is returned. Additionally, the axes can be negative, +// which are interpreted according to the indexing rules in Python. +// +// Arguments: +// input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// input_values: 1-D. `N` non-empty values corresponding to `input_indices`. +// input_shape: 1-D. Shape of the input SparseTensor. +// reduction_axes: 1-D. Length-`K` vector containing the reduction axes. +// +// Returns `R-K`-D. The reduced Tensor. +func SparseReduceMax(scope *Scope, input_indices tf.Output, input_values tf.Output, input_shape tf.Output, reduction_axes tf.Output, optional ...SparseReduceMaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseReduceMax", + Input: []tf.Input{ + input_indices, input_values, input_shape, reduction_axes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generates sparse cross from a list of sparse and dense tensors. +// +// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each +// representing features of one feature column. It outputs a 2D `SparseTensor` with +// the batchwise crosses of these features. +// +// For example, if the inputs are +// +// inputs[0]: SparseTensor with shape = [2, 2] +// [0, 0]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// inputs[1]: SparseTensor with shape = [2, 1] +// [0, 0]: "d" +// [1, 0]: "e" +// +// inputs[2]: Tensor [["f"], ["g"]] +// +// then the output will be +// +// shape = [2, 2] +// [0, 0]: "a_X_d_X_f" +// [1, 0]: "b_X_e_X_g" +// [1, 1]: "c_X_e_X_g" +// +// if hashed_output=true then the output will be +// +// shape = [2, 2] +// [0, 0]: FingerprintCat64( +// Fingerprint64("f"), FingerprintCat64( +// Fingerprint64("d"), Fingerprint64("a"))) +// [1, 0]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("b"))) +// [1, 1]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("c"))) +// +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// dense_inputs: 2-D. Columns represented by dense `Tensor`. +// num_buckets: It is used if hashed_output is true. +// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. +// strong_hash: boolean, if true, siphash with salt will be used instead of farmhash. +// salt: Specify the salt that will be used by the siphash function. +// +// Returns: +// output_indices: 2-D. Indices of the concatenated `SparseTensor`. +// output_values: 1-D. Non-empty values of the concatenated or hashed +// `SparseTensor`. +// output_shape: 1-D. Shape of the concatenated `SparseTensor`. +func SparseCrossHashed(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, num_buckets tf.Output, strong_hash tf.Output, salt tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseCrossHashed", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), num_buckets, strong_hash, salt, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Conv3DBackpropInputAttr is an optional argument to Conv3DBackpropInput. +type Conv3DBackpropInputAttr func(optionalAttr) + +// Conv3DBackpropInputDilations sets the optional dilations attribute to value. +// If not specified, defaults to +func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of 3-D convolution with respect to the input. +// +// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2 +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, in_channels]`. +// filter: Shape `[depth, rows, cols, in_channels, out_channels]`. +// `in_channels` must match between `input` and `filter`. +// out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, +// out_channels]`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...Conv3DBackpropInputAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Conv3DBackpropInput", + Input: []tf.Input{ + input, filter, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedInstanceNormAttr is an optional argument to QuantizedInstanceNorm. +type QuantizedInstanceNormAttr func(optionalAttr) + +// QuantizedInstanceNormOutputRangeGiven sets the optional output_range_given attribute to value. +// +// value: If True, `given_y_min` and `given_y_min` +// and `given_y_max` are used as the output range. Otherwise, +// the implementation computes the output range. +// If not specified, defaults to false +func QuantizedInstanceNormOutputRangeGiven(value bool) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["output_range_given"] = value + } +} + +// QuantizedInstanceNormGivenYMin sets the optional given_y_min attribute to value. +// +// value: Output in `y_min` if `output_range_given` is True. +// If not specified, defaults to 0 +func QuantizedInstanceNormGivenYMin(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["given_y_min"] = value + } +} + +// QuantizedInstanceNormGivenYMax sets the optional given_y_max attribute to value. +// +// value: Output in `y_max` if `output_range_given` is True. +// If not specified, defaults to 0 +func QuantizedInstanceNormGivenYMax(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["given_y_max"] = value + } +} + +// QuantizedInstanceNormVarianceEpsilon sets the optional variance_epsilon attribute to value. +// +// value: A small float number to avoid dividing by 0. +// If not specified, defaults to 1e-05 +func QuantizedInstanceNormVarianceEpsilon(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["variance_epsilon"] = value + } +} + +// QuantizedInstanceNormMinSeparation sets the optional min_separation attribute to value. +// +// value: Minimum value of `y_max - y_min` +// If not specified, defaults to 0.001 +func QuantizedInstanceNormMinSeparation(value float32) QuantizedInstanceNormAttr { + return func(m optionalAttr) { + m["min_separation"] = value + } +} + +// Quantized Instance normalization. +// +// Arguments: +// x: A 4D input Tensor. +// x_min: The value represented by the lowest quantized input. +// x_max: The value represented by the highest quantized input. +// +// Returns: +// y: A 4D Tensor. +// y_min: The value represented by the lowest quantized output. +// y_max: The value represented by the highest quantized output. +func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.Output, optional ...QuantizedInstanceNormAttr) (y tf.Output, y_min tf.Output, y_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedInstanceNorm", + Input: []tf.Input{ + x, x_min, x_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// FusedBatchNormV3Attr is an optional argument to FusedBatchNormV3. +type FusedBatchNormV3Attr func(optionalAttr) + +// FusedBatchNormV3Epsilon sets the optional epsilon attribute to value. +// +// value: A small float number added to the variance of x. +// If not specified, defaults to 0.0001 +func FusedBatchNormV3Epsilon(value float32) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["epsilon"] = value + } +} + +// FusedBatchNormV3ExponentialAvgFactor sets the optional exponential_avg_factor attribute to value. +// If not specified, defaults to 1 +func FusedBatchNormV3ExponentialAvgFactor(value float32) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["exponential_avg_factor"] = value + } +} + +// FusedBatchNormV3DataFormat sets the optional data_format attribute to value. +// +// value: The data format for x and y. Either "NHWC" (default) or "NCHW". +// If not specified, defaults to "NHWC" +func FusedBatchNormV3DataFormat(value string) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// FusedBatchNormV3IsTraining sets the optional is_training attribute to value. +// +// value: A bool value to indicate the operation is for training (default) +// or inference. +// If not specified, defaults to true +func FusedBatchNormV3IsTraining(value bool) FusedBatchNormV3Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// Batch normalization. +// +// Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". +// The size of 1D Tensors matches the dimension C of the 4D Tensors. +// +// Arguments: +// x: A 4D Tensor for input data. +// scale: A 1D Tensor for scaling factor, to scale the normalized x. +// offset: A 1D Tensor for offset, to shift to the normalized x. +// mean: A 1D Tensor for population mean. Used for inference only; +// must be empty for training. +// variance: A 1D Tensor for population variance. Used for inference only; +// must be empty for training. +// +// Returns: +// y: A 4D Tensor for output data. +// batch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow +// to compute the running mean. +// batch_variance: A 1D Tensor for the computed batch variance, to be used by +// TensorFlow to compute the running variance. +// reserve_space_1: A 1D Tensor for the computed batch mean, to be reused +// in the gradient computation. +// reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance +// in the cuDNN case), to be reused in the gradient computation. +// reserve_space_3: A 1D Tensor for some intermediate results, to be reused in the gradient +// computation for better efficiency. +func FusedBatchNormV3(scope *Scope, x tf.Output, scale tf.Output, offset tf.Output, mean tf.Output, variance tf.Output, optional ...FusedBatchNormV3Attr) (y tf.Output, batch_mean tf.Output, batch_variance tf.Output, reserve_space_1 tf.Output, reserve_space_2 tf.Output, reserve_space_3 tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedBatchNormV3", + Input: []tf.Input{ + x, scale, offset, mean, variance, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5) +} + +// Computes reciprocal of square root of x element-wise. +// +// I.e., \\(y = 1 / \sqrt{x}\\). +func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Rsqrt", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// The gradient operator for the SparseSlice op. +// +// This op takes in the upstream gradient w.r.t. non-empty values of +// the sliced `SparseTensor`, and outputs the gradients w.r.t. +// the non-empty values of input `SparseTensor`. +// +// Arguments: +// backprop_val_grad: 1-D. The gradient with respect to +// the non-empty values of the sliced `SparseTensor`. +// input_indices: 2-D. The `indices` of the input `SparseTensor`. +// input_start: 1-D. tensor represents the start of the slice. +// output_indices: 2-D. The `indices` of the sliced `SparseTensor`. +// +// Returns 1-D. The gradient with respect to the non-empty values of input `SparseTensor`. +func SparseSliceGrad(scope *Scope, backprop_val_grad tf.Output, input_indices tf.Output, input_start tf.Output, output_indices tf.Output) (val_grad tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSliceGrad", + Input: []tf.Input{ + backprop_val_grad, input_indices, input_start, output_indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generates sparse cross from a list of sparse and dense tensors. +// +// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each +// representing features of one feature column. It outputs a 2D `SparseTensor` with +// the batchwise crosses of these features. +// +// For example, if the inputs are +// +// inputs[0]: SparseTensor with shape = [2, 2] +// [0, 0]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// inputs[1]: SparseTensor with shape = [2, 1] +// [0, 0]: "d" +// [1, 0]: "e" +// +// inputs[2]: Tensor [["f"], ["g"]] +// +// then the output will be +// +// shape = [2, 2] +// [0, 0]: "a_X_d_X_f" +// [1, 0]: "b_X_e_X_g" +// [1, 1]: "c_X_e_X_g" +// +// if hashed_output=true then the output will be +// +// shape = [2, 2] +// [0, 0]: FingerprintCat64( +// Fingerprint64("f"), FingerprintCat64( +// Fingerprint64("d"), Fingerprint64("a"))) +// [1, 0]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("b"))) +// [1, 1]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("c"))) +// +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// dense_inputs: 2-D. Columns represented by dense `Tensor`. +// sep: string used when joining a list of string inputs, can be used as separator later. +// +// Returns: +// output_indices: 2-D. Indices of the concatenated `SparseTensor`. +// output_values: 1-D. Non-empty values of the concatenated or hashed +// `SparseTensor`. +// output_shape: 1-D. Shape of the concatenated `SparseTensor`. +func SparseCrossV2(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, sep tf.Output) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseCrossV2", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), sep, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Pads a tensor with mirrored values. +// +// This operation pads a `input` with mirrored values according to the `paddings` +// you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is +// the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates +// how many values to add before the contents of `input` in that dimension, and +// `paddings[D, 1]` indicates how many values to add after the contents of `input` +// in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater +// than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if `copy_border` is true +// (if false, respectively). +// +// The padded size of each dimension D of the output is: +// +// `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 2, 3], [4, 5, 6]]. +// # 'paddings' is [[1, 1]], [2, 2]]. +// # 'mode' is SYMMETRIC. +// # rank of 't' is 2. +// pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2] +// [2, 1, 1, 2, 3, 3, 2] +// [5, 4, 4, 5, 6, 6, 5] +// [5, 4, 4, 5, 6, 6, 5]] +// ``` +// +// Arguments: +// input: The input tensor to be padded. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// mode: Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions +// do not include the borders, while in symmetric mode the padded regions +// do include the borders. For example, if `input` is `[1, 2, 3]` and `paddings` +// is `[0, 2]`, then the output is `[1, 2, 3, 2, 1]` in reflect mode, and +// it is `[1, 2, 3, 3, 2]` in symmetric mode. +// +// Returns The padded tensor. +func MirrorPad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode} + opspec := tf.OpSpec{ + Type: "MirrorPad", + Input: []tf.Input{ + input, paddings, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorArrayV3Attr is an optional argument to TensorArrayV3. +type TensorArrayV3Attr func(optionalAttr) + +// TensorArrayV3ElementShape sets the optional element_shape attribute to value. +// +// value: The expected shape of an element, if known. Used to +// validate the shapes of TensorArray elements. If this shape is not +// fully specified, gathering zero-size TensorArrays is an error. +// If not specified, defaults to +func TensorArrayV3ElementShape(value tf.Shape) TensorArrayV3Attr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// TensorArrayV3DynamicSize sets the optional dynamic_size attribute to value. +// +// value: A boolean that determines whether writes to the TensorArray +// are allowed to grow the size. By default, this is not allowed. +// If not specified, defaults to false +func TensorArrayV3DynamicSize(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["dynamic_size"] = value + } +} + +// TensorArrayV3ClearAfterRead sets the optional clear_after_read attribute to value. +// +// value: If true (default), Tensors in the TensorArray are cleared +// after being read. This disables multiple read semantics but allows early +// release of memory. +// If not specified, defaults to true +func TensorArrayV3ClearAfterRead(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["clear_after_read"] = value + } +} + +// TensorArrayV3IdenticalElementShapes sets the optional identical_element_shapes attribute to value. +// +// value: If true (default is false), then all +// elements in the TensorArray will be expected to have have identical shapes. +// This allows certain behaviors, like dynamically checking for +// consistent shapes on write, and being able to fill in properly +// shaped zero tensors on stack -- even if the element_shape attribute +// is not fully defined. +// If not specified, defaults to false +func TensorArrayV3IdenticalElementShapes(value bool) TensorArrayV3Attr { + return func(m optionalAttr) { + m["identical_element_shapes"] = value + } +} + +// TensorArrayV3TensorArrayName sets the optional tensor_array_name attribute to value. +// +// value: Overrides the name used for the temporary tensor_array +// resource. Default value is the name of the 'TensorArray' op (which +// is guaranteed unique). +// If not specified, defaults to "" +func TensorArrayV3TensorArrayName(value string) TensorArrayV3Attr { + return func(m optionalAttr) { + m["tensor_array_name"] = value + } +} + +// An array of Tensors of given size. +// +// Write data via Write and read via Read or Pack. +// +// Arguments: +// size: The size of the array. +// dtype: The type of the elements on the tensor_array. +// +// Returns: +// handle: The handle to the TensorArray. +// flow: A scalar used to control gradient flow. +func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...TensorArrayV3Attr) (handle tf.Output, flow tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayV3", + Input: []tf.Input{ + size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MatrixSolveLsAttr is an optional argument to MatrixSolveLs. +type MatrixSolveLsAttr func(optionalAttr) + +// MatrixSolveLsFast sets the optional fast attribute to value. +// If not specified, defaults to true +func MatrixSolveLsFast(value bool) MatrixSolveLsAttr { + return func(m optionalAttr) { + m["fast"] = value + } +} + +// Solves one or more linear least-squares problems. +// +// `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions +// form real or complex matrices of size `[M, N]`. `Rhs` is a tensor of the same +// type as `matrix` and shape `[..., M, K]`. +// The output is a tensor shape `[..., N, K]` where each output matrix solves +// each of the equations +// `matrix[..., :, :]` * `output[..., :, :]` = `rhs[..., :, :]` +// in the least squares sense. +// +// We use the following notation for (complex) matrix and right-hand sides +// in the batch: +// +// `matrix`=\\(A \in \mathbb{C}^{m \times n}\\), +// `rhs`=\\(B \in \mathbb{C}^{m \times k}\\), +// `output`=\\(X \in \mathbb{C}^{n \times k}\\), +// `l2_regularizer`=\\(\lambda \in \mathbb{R}\\). +// +// If `fast` is `True`, then the solution is computed by solving the normal +// equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then +// \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares +// problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 + \lambda ||Z||_F^2\\). +// If \\(m \lt n\\) then `output` is computed as +// \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the +// minimum-norm solution to the under-determined linear system, i.e. +// \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\), +// subject to \\(A Z = B\\). Notice that the fast path is only numerically stable +// when \\(A\\) is numerically full rank and has a condition number +// \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or \\(\lambda\\) is +// sufficiently large. +// +// If `fast` is `False` an algorithm based on the numerically robust complete +// orthogonal decomposition is used. This computes the minimum-norm +// least-squares solution, even when \\(A\\) is rank deficient. This path is +// typically 6-7 times slower than the fast path. If `fast` is `False` then +// `l2_regularizer` is ignored. +// +// Arguments: +// matrix: Shape is `[..., M, N]`. +// rhs: Shape is `[..., M, K]`. +// l2_regularizer: Scalar tensor. +// +// @compatibility(numpy) +// Equivalent to np.linalg.lstsq +// @end_compatibility +// +// Returns Shape is `[..., N, K]`. +func MatrixSolveLs(scope *Scope, matrix tf.Output, rhs tf.Output, l2_regularizer tf.Output, optional ...MatrixSolveLsAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixSolveLs", + Input: []tf.Input{ + matrix, rhs, l2_regularizer, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generates sparse cross from a list of sparse and dense tensors. +// +// The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each +// representing features of one feature column. It outputs a 2D `SparseTensor` with +// the batchwise crosses of these features. +// +// For example, if the inputs are +// +// inputs[0]: SparseTensor with shape = [2, 2] +// [0, 0]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// inputs[1]: SparseTensor with shape = [2, 1] +// [0, 0]: "d" +// [1, 0]: "e" +// +// inputs[2]: Tensor [["f"], ["g"]] +// +// then the output will be +// +// shape = [2, 2] +// [0, 0]: "a_X_d_X_f" +// [1, 0]: "b_X_e_X_g" +// [1, 1]: "c_X_e_X_g" +// +// if hashed_output=true then the output will be +// +// shape = [2, 2] +// [0, 0]: FingerprintCat64( +// Fingerprint64("f"), FingerprintCat64( +// Fingerprint64("d"), Fingerprint64("a"))) +// [1, 0]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("b"))) +// [1, 1]: FingerprintCat64( +// Fingerprint64("g"), FingerprintCat64( +// Fingerprint64("e"), Fingerprint64("c"))) +// +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// dense_inputs: 2-D. Columns represented by dense `Tensor`. +// hashed_output: If true, returns the hash of the cross instead of the string. +// This will allow us avoiding string manipulations. +// num_buckets: It is used if hashed_output is true. +// output = hashed_value%num_buckets if num_buckets > 0 else hashed_value. +// hash_key: Specify the hash_key that will be used by the `FingerprintCat64` +// function to combine the crosses fingerprints. +// +// +// +// Returns: +// output_indices: 2-D. Indices of the concatenated `SparseTensor`. +// output_values: 1-D. Non-empty values of the concatenated or hashed +// `SparseTensor`. +// output_shape: 1-D. Shape of the concatenated `SparseTensor`. +func SparseCross(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, dense_inputs []tf.Output, hashed_output bool, num_buckets int64, hash_key int64, out_type tf.DataType, internal_type tf.DataType) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_type": out_type, "internal_type": internal_type} + opspec := tf.OpSpec{ + Type: "SparseCross", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), tf.OutputList(dense_inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Generate a glob pattern matching all sharded file names. +func ShardedFilespec(scope *Scope, basename tf.Output, num_shards tf.Output) (filename tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ShardedFilespec", + Input: []tf.Input{ + basename, num_shards, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingProximalAdagradParametersAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParameters. +type RetrieveTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingProximalAdagradParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingProximalAdagradParametersTableId(value int64) RetrieveTPUEmbeddingProximalAdagradParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingProximalAdagradParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingProximalAdagradParametersTableName(value string) RetrieveTPUEmbeddingProximalAdagradParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingProximalAdagradParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingProximalAdagradParametersConfig(value string) RetrieveTPUEmbeddingProximalAdagradParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve proximal Adagrad embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the proximal Adagrad optimization algorithm. +// accumulators: Parameter accumulators updated by the proximal Adagrad optimization algorithm. +func RetrieveTPUEmbeddingProximalAdagradParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingProximalAdagradParametersAttr) (parameters tf.Output, accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingProximalAdagradParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ReduceJoinAttr is an optional argument to ReduceJoin. +type ReduceJoinAttr func(optionalAttr) + +// ReduceJoinKeepDims sets the optional keep_dims attribute to value. +// +// value: If `True`, retain reduced dimensions with length `1`. +// If not specified, defaults to false +func ReduceJoinKeepDims(value bool) ReduceJoinAttr { + return func(m optionalAttr) { + m["keep_dims"] = value + } +} + +// ReduceJoinSeparator sets the optional separator attribute to value. +// +// value: The separator to use when joining. +// If not specified, defaults to "" +func ReduceJoinSeparator(value string) ReduceJoinAttr { + return func(m optionalAttr) { + m["separator"] = value + } +} + +// Joins a string Tensor across the given dimensions. +// +// Computes the string join across dimensions in the given string Tensor of shape +// `[\\(d_0, d_1, ..., d_{n-1}\\)]`. Returns a new Tensor created by joining the input +// strings with the given separator (default: empty string). Negative indices are +// counted backwards from the end, with `-1` being equivalent to `n - 1`. If +// indices are not specified, joins across all dimensions beginning from `n - 1` +// through `0`. +// +// For example: +// +// ```python +// # tensor `a` is [["a", "b"], ["c", "d"]] +// tf.reduce_join(a, 0) ==> ["ac", "bd"] +// tf.reduce_join(a, 1) ==> ["ab", "cd"] +// tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"] +// tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"] +// tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]] +// tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]] +// tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"] +// tf.reduce_join(a, [0, 1]) ==> "acbd" +// tf.reduce_join(a, [1, 0]) ==> "abcd" +// tf.reduce_join(a, []) ==> [["a", "b"], ["c", "d"]] +// tf.reduce_join(a) = tf.reduce_join(a, [1, 0]) ==> "abcd" +// ``` +// +// Arguments: +// inputs: The input to be joined. All reduced indices must have non-zero size. +// reduction_indices: The dimensions to reduce over. Dimensions are reduced in the +// order specified. Omitting `reduction_indices` is equivalent to passing +// `[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported. +// +// Returns Has shape equal to that of the input with reduced dimensions removed or +// set to `1` depending on `keep_dims`. +func ReduceJoin(scope *Scope, inputs tf.Output, reduction_indices tf.Output, optional ...ReduceJoinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ReduceJoin", + Input: []tf.Input{ + inputs, reduction_indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse 2D fast Fourier transform. +// +// Computes the inverse 2-dimensional discrete Fourier transform over the +// inner-most 2 dimensions of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft2 +// @end_compatibility +func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates a list of `SparseTensor` along the specified dimension. +// +// Concatenation is with respect to the dense versions of these sparse tensors. +// It is assumed that each input is a `SparseTensor` whose elements are ordered +// along increasing dimension number. +// +// All inputs' shapes must match, except for the concat dimension. The +// `indices`, `values`, and `shapes` lists must have the same length. +// +// The output shape is identical to the inputs', except along the concat +// dimension, where it is the sum of the inputs' sizes along that dimension. +// +// The output elements will be resorted to preserve the sort order along +// increasing dimension number. +// +// This op runs in `O(M log M)` time, where `M` is the total number of non-empty +// values across all inputs. This is due to the need for an internal sort in +// order to concatenate efficiently across an arbitrary dimension. +// +// For example, if `concat_dim = 1` and the inputs are +// +// sp_inputs[0]: shape = [2, 3] +// [0, 2]: "a" +// [1, 0]: "b" +// [1, 1]: "c" +// +// sp_inputs[1]: shape = [2, 4] +// [0, 1]: "d" +// [0, 2]: "e" +// +// then the output will be +// +// shape = [2, 7] +// [0, 2]: "a" +// [0, 4]: "d" +// [0, 5]: "e" +// [1, 0]: "b" +// [1, 1]: "c" +// +// Graphically this is equivalent to doing +// +// [ a] concat [ d e ] = [ a d e ] +// [b c ] [ ] [b c ] +// +// Arguments: +// indices: 2-D. Indices of each input `SparseTensor`. +// values: 1-D. Non-empty values of each `SparseTensor`. +// shapes: 1-D. Shapes of each `SparseTensor`. +// concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), +// where rank is the number of dimensions in each input `SparseTensor`. +// +// Returns: +// output_indices: 2-D. Indices of the concatenated `SparseTensor`. +// output_values: 1-D. Non-empty values of the concatenated `SparseTensor`. +// output_shape: 1-D. Shape of the concatenated `SparseTensor`. +func SparseConcat(scope *Scope, indices []tf.Output, values []tf.Output, shapes []tf.Output, concat_dim int64) (output_indices tf.Output, output_values tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"concat_dim": concat_dim} + opspec := tf.OpSpec{ + Type: "SparseConcat", + Input: []tf.Input{ + tf.OutputList(indices), tf.OutputList(values), tf.OutputList(shapes), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// DestroyResourceOpAttr is an optional argument to DestroyResourceOp. +type DestroyResourceOpAttr func(optionalAttr) + +// DestroyResourceOpIgnoreLookupError sets the optional ignore_lookup_error attribute to value. +// +// value: whether to ignore the error when the resource +// doesn't exist. +// If not specified, defaults to true +func DestroyResourceOpIgnoreLookupError(value bool) DestroyResourceOpAttr { + return func(m optionalAttr) { + m["ignore_lookup_error"] = value + } +} + +// Deletes the resource specified by the handle. +// +// All subsequent operations using the resource will result in a NotFound +// error status. +// +// Arguments: +// resource: handle to the resource to delete. +// +// Returns the created operation. +func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyResourceOpAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DestroyResourceOp", + Input: []tf.Input{ + resource, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// HistogramFixedWidthAttr is an optional argument to HistogramFixedWidth. +type HistogramFixedWidthAttr func(optionalAttr) + +// HistogramFixedWidthDtype sets the optional dtype attribute to value. +// If not specified, defaults to DT_INT32 +func HistogramFixedWidthDtype(value tf.DataType) HistogramFixedWidthAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Return histogram of values. +// +// Given the tensor `values`, this operation returns a rank 1 histogram counting +// the number of entries in `values` that fall into every bin. The bins are +// equal width and determined by the arguments `value_range` and `nbins`. +// +// ```python +// # Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf) +// nbins = 5 +// value_range = [0.0, 5.0] +// new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15] +// +// with tf.get_default_session() as sess: +// hist = tf.histogram_fixed_width(new_values, value_range, nbins=5) +// variables.global_variables_initializer().run() +// sess.run(hist) => [2, 1, 1, 0, 2] +// ``` +// +// Arguments: +// values: Numeric `Tensor`. +// value_range: Shape [2] `Tensor` of same `dtype` as `values`. +// values <= value_range[0] will be mapped to hist[0], +// values >= value_range[1] will be mapped to hist[-1]. +// nbins: Scalar `int32 Tensor`. Number of histogram bins. +// +// Returns A 1-D `Tensor` holding histogram of values. +func HistogramFixedWidth(scope *Scope, values tf.Output, value_range tf.Output, nbins tf.Output, optional ...HistogramFixedWidthAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "HistogramFixedWidth", + Input: []tf.Input{ + values, value_range, nbins, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that uses a custom thread pool to compute `input_dataset`. +// +// Arguments: +// +// thread_pool: A resource produced by the ThreadPoolHandle op. +// +// +func ThreadPoolDataset(scope *Scope, input_dataset tf.Output, thread_pool tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ThreadPoolDataset", + Input: []tf.Input{ + input_dataset, thread_pool, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Bitcasts a tensor from one type to another without copying data. +// +// Given a tensor `input`, this operation returns a tensor that has the same buffer +// data as `input` with datatype `type`. +// +// If the input datatype `T` is larger than the output datatype `type` then the +// shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)]. +// +// If `T` is smaller than `type`, the operator requires that the rightmost +// dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from +// [..., sizeof(`type`)/sizeof(`T`)] to [...]. +// +// tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype +// (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() +// gives module error. +// For example, +// +// Example 1: +// +// >>> a = [1., 2., 3.] +// >>> equality_bitcast = tf.bitcast(a, tf.complex128) +// Traceback (most recent call last): +// ... +// InvalidArgumentError: Cannot bitcast from 1 to 18 [Op:Bitcast] +// >>> equality_cast = tf.cast(a, tf.complex128) +// >>> print(equality_cast) +// tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128) +// +// Example 2: +// +// >>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8) +// +// +// Example 3: +// +// >>> x = [1., 2., 3.] +// >>> y = [0., 2., 3.] +// >>> equality= tf.equal(x,y) +// >>> equality_cast = tf.cast(equality,tf.float32) +// >>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8) +// >>> print(equality) +// tf.Tensor([False True True], shape=(3,), dtype=bool) +// >>> print(equality_cast) +// tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32) +// >>> print(equality_bitcast) +// tf.Tensor( +// [[ 0 0 0 0] +// [ 0 0 128 63] +// [ 0 0 128 63]], shape=(3, 4), dtype=uint8) +// +// *NOTE*: Bitcast is implemented as a low-level cast, so machines with different +// endian orderings will give different results. +func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "Bitcast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyAdagradDAAttr is an optional argument to ResourceApplyAdagradDA. +type ResourceApplyAdagradDAAttr func(optionalAttr) + +// ResourceApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyAdagradDAUseLocking(value bool) ResourceApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the proximal adagrad scheme. +// +// Arguments: +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceApplyAdagradDAAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagradDA", + Input: []tf.Input{ + var_, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SparseToDenseAttr is an optional argument to SparseToDense. +type SparseToDenseAttr func(optionalAttr) + +// SparseToDenseValidateIndices sets the optional validate_indices attribute to value. +// +// value: If true, indices are checked to make sure they are sorted in +// lexicographic order and that there are no repeats. +// If not specified, defaults to true +func SparseToDenseValidateIndices(value bool) SparseToDenseAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Converts a sparse representation into a dense tensor. +// +// Builds an array `dense` with shape `output_shape` such that +// +// ``` +// # If sparse_indices is scalar +// dense[i] = (i == sparse_indices ? sparse_values : default_value) +// +// # If sparse_indices is a vector, then for each i +// dense[sparse_indices[i]] = sparse_values[i] +// +// # If sparse_indices is an n by d matrix, then for each i in [0, n) +// dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] +// ``` +// +// All other values in `dense` are set to `default_value`. If `sparse_values` is a +// scalar, all sparse indices are set to this single value. +// +// Indices should be sorted in lexicographic order, and indices must not +// contain any repeats. If `validate_indices` is true, these properties +// are checked during execution. +// +// Arguments: +// sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete +// index where `sparse_values[i]` will be placed. +// output_shape: 1-D. Shape of the dense output tensor. +// sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, +// or a scalar value to be used for all sparse indices. +// default_value: Scalar value to set for indices not specified in +// `sparse_indices`. +// +// Returns Dense output tensor of shape `output_shape`. +func SparseToDense(scope *Scope, sparse_indices tf.Output, output_shape tf.Output, sparse_values tf.Output, default_value tf.Output, optional ...SparseToDenseAttr) (dense tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseToDense", + Input: []tf.Input{ + sparse_indices, output_shape, sparse_values, default_value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapClearAttr is an optional argument to OrderedMapClear. +type OrderedMapClearAttr func(optionalAttr) + +// OrderedMapClearCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapClearCapacity(value int64) OrderedMapClearAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapClearMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapClearMemoryLimit(value int64) OrderedMapClearAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapClearContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func OrderedMapClearContainer(value string) OrderedMapClearAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapClearSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func OrderedMapClearSharedName(value string) OrderedMapClearAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes all elements in the underlying container. +// +// Returns the created operation. +func OrderedMapClear(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapClearAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapClear", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MaxPoolAttr is an optional argument to MaxPool. +type MaxPoolAttr func(optionalAttr) + +// MaxPoolDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolDataFormat(value string) MaxPoolAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs max pooling on the input. +// +// Arguments: +// input: 4-D input to pool over. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns The max pooled output tensor. +func MaxPool(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// BlockLSTMAttr is an optional argument to BlockLSTM. +type BlockLSTMAttr func(optionalAttr) + +// BlockLSTMForgetBias sets the optional forget_bias attribute to value. +// +// value: The forget gate bias. +// If not specified, defaults to 1 +func BlockLSTMForgetBias(value float32) BlockLSTMAttr { + return func(m optionalAttr) { + m["forget_bias"] = value + } +} + +// BlockLSTMCellClip sets the optional cell_clip attribute to value. +// +// value: Value to clip the 'cs' value to. +// If not specified, defaults to 3 +func BlockLSTMCellClip(value float32) BlockLSTMAttr { + return func(m optionalAttr) { + m["cell_clip"] = value + } +} + +// BlockLSTMUsePeephole sets the optional use_peephole attribute to value. +// +// value: Whether to use peephole weights. +// If not specified, defaults to false +func BlockLSTMUsePeephole(value bool) BlockLSTMAttr { + return func(m optionalAttr) { + m["use_peephole"] = value + } +} + +// Computes the LSTM cell forward propagation for all the time steps. +// +// This is equivalent to applying LSTMBlockCell in a loop, like so: +// +// ```python +// for x1 in unpack(x): +// i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock( +// x1, cs_prev, h_prev, w, wci, wcf, wco, b) +// cs_prev = cs1 +// h_prev = h1 +// i.append(i1) +// cs.append(cs1) +// f.append(f1) +// o.append(o1) +// ci.append(ci1) +// co.append(co1) +// h.append(h1) +// return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h) +// ``` +// +// Arguments: +// seq_len_max: Maximum time length actually used by this input. Outputs are padded +// with zeros beyond this length. +// x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). +// cs_prev: Value of the initial cell state. +// h_prev: Initial output of cell (to be used for peephole). +// w: The weight matrix. +// wci: The weight matrix for input gate peephole connection. +// wcf: The weight matrix for forget gate peephole connection. +// wco: The weight matrix for output gate peephole connection. +// b: The bias vector. +// +// Returns: +// i: The input gate over the whole time sequence. +// cs: The cell state before the tanh over the whole time sequence. +// f: The forget gate over the whole time sequence. +// o: The output gate over the whole time sequence. +// ci: The cell input over the whole time sequence. +// co: The cell after the tanh over the whole time sequence. +// h: The output h vector over the whole time sequence. +func BlockLSTM(scope *Scope, seq_len_max tf.Output, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, optional ...BlockLSTMAttr) (i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "BlockLSTM", + Input: []tf.Input{ + seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// Computes the GRU cell forward propagation for 1 time step. +// +// Args +// x: Input to the GRU cell. +// h_prev: State input from the previous GRU cell. +// w_ru: Weight matrix for the reset and update gate. +// w_c: Weight matrix for the cell connection gate. +// b_ru: Bias vector for the reset and update gate. +// b_c: Bias vector for the cell connection gate. +// +// Returns +// r: Output of the reset gate. +// u: Output of the update gate. +// c: Output of the cell connection gate. +// h: Current state of the GRU cell. +// +// Note on notation of the variables: +// +// Concatenation of a and b is represented by a_b +// Element-wise dot product of a and b is represented by ab +// Element-wise dot product is represented by \circ +// Matrix multiplication is represented by * +// +// Biases are initialized with : +// `b_ru` - constant_initializer(1.0) +// `b_c` - constant_initializer(0.0) +// +// This kernel op implements the following mathematical equations: +// +// ``` +// x_h_prev = [x, h_prev] +// +// [r_bar u_bar] = x_h_prev * w_ru + b_ru +// +// r = sigmoid(r_bar) +// u = sigmoid(u_bar) +// +// h_prevr = h_prev \circ r +// +// x_h_prevr = [x h_prevr] +// +// c_bar = x_h_prevr * w_c + b_c +// c = tanh(c_bar) +// +// h = (1-u) \circ c + u \circ h_prev +// ``` +func GRUBlockCell(scope *Scope, x tf.Output, h_prev tf.Output, w_ru tf.Output, w_c tf.Output, b_ru tf.Output, b_c tf.Output) (r tf.Output, u tf.Output, c tf.Output, h tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "GRUBlockCell", + Input: []tf.Input{ + x, h_prev, w_ru, w_c, b_ru, b_c, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Deserialize and concatenate `SparseTensors` from a serialized minibatch. +// +// The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where +// `N` is the minibatch size and the rows correspond to packed outputs of +// `SerializeSparse`. The ranks of the original `SparseTensor` objects +// must all match. When the final `SparseTensor` is created, it has rank one +// higher than the ranks of the incoming `SparseTensor` objects +// (they have been concatenated along a new row dimension). +// +// The output `SparseTensor` object's shape values for all dimensions but the +// first are the max across the input `SparseTensor` objects' shape values +// for the corresponding dimensions. Its first shape value is `N`, the minibatch +// size. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: +// +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects. +// Must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeManySparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "DeserializeManySparse", + Input: []tf.Input{ + serialized_sparse, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Sets the index-th position of the list to contain the given tensor. +// +// input_handle: the list +// index: the position in the list to which the tensor will be assigned +// item: the element to be assigned to that position +// output_handle: the new list, with the element in the proper position +// +func TensorListSetItem(scope *Scope, input_handle tf.Output, index tf.Output, item tf.Output) (output_handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TensorListSetItem", + Input: []tf.Input{ + input_handle, index, item, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Writes the given dataset to the given file using the TFRecord format. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to write. +// filename: A scalar string tensor representing the filename to use. +// compression_type: A scalar string tensor containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// +// Returns the created operation. +func DatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DatasetToTFRecord", + Input: []tf.Input{ + input_dataset, filename, compression_type, + }, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp. +type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr) + +// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the centered RMSProp algorithm. +// +// The centered RMSProp algorithm uses an estimate of the centered second moment +// (i.e., the variance) for normalization, as opposed to regular RMSProp, which +// uses the (uncentered) second moment. This often helps with training, but is +// slightly more expensive in terms of computation and memory. +// +// Note that in dense implementation of this algorithm, mg, ms, and mom will +// update even if the grad is zero, but in this sparse implementation, mg, ms, +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// mean_grad = decay * mean_grad + (1-decay) * gradient +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// mg: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var, ms and mom. +// +// Returns the created operation. +func ResourceSparseApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyCenteredRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyCenteredRMSProp", + Input: []tf.Input{ + var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Elementwise computes the bitwise XOR of `x` and `y`. +// +// The result will have those bits set, that are different in `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +// +// For example: +// +// ```python +// import tensorflow as tf +// from tensorflow.python.ops import bitwise_ops +// dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64, +// tf.uint8, tf.uint16, tf.uint32, tf.uint64] +// +// for dtype in dtype_list: +// lhs = tf.constant([0, 5, 3, 14], dtype=dtype) +// rhs = tf.constant([5, 0, 7, 11], dtype=dtype) +// exp = tf.constant([5, 5, 4, 5], dtype=tf.float32) +// +// res = bitwise_ops.bitwise_xor(lhs, rhs) +// tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE +// ``` +// +func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BitwiseXor", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds two `SparseTensor` objects to produce another `SparseTensor`. +// +// The input `SparseTensor` objects' indices are assumed ordered in standard +// lexicographic order. If this is not the case, before this step run +// `SparseReorder` to restore index ordering. +// +// By default, if two values sum to zero at some index, the output `SparseTensor` +// would still include that particular location in its index, storing a zero in the +// corresponding value slot. To override this, callers can specify `thresh`, +// indicating that if the sum has a magnitude strictly smaller than `thresh`, its +// corresponding value and index would then not be included. In particular, +// `thresh == 0` (default) means everything is kept and actual thresholding happens +// only for a positive value. +// +// In the following shapes, `nnz` is the count after taking `thresh` into account. +// +// Arguments: +// a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. +// a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. +// a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. +// b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. +// b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. +// b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. +// thresh: 0-D. The magnitude threshold that determines if an output value/index +// pair takes space. +func SparseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output, thresh tf.Output) (sum_indices tf.Output, sum_values tf.Output, sum_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseAdd", + Input: []tf.Input{ + a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// RandomShuffleAttr is an optional argument to RandomShuffle. +type RandomShuffleAttr func(optionalAttr) + +// RandomShuffleSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomShuffleSeed(value int64) RandomShuffleAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomShuffleSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomShuffleSeed2(value int64) RandomShuffleAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Randomly shuffles a tensor along its first dimension. +// +// The tensor is shuffled along dimension 0, such that each `value[j]` is mapped +// to one and only one `output[i]`. For example, a mapping that might occur for a +// 3x2 tensor is: +// +// ``` +// [[1, 2], [[5, 6], +// [3, 4], ==> [1, 2], +// [5, 6]] [3, 4]] +// ``` +// +// Arguments: +// value: The tensor to be shuffled. +// +// Returns A tensor of same shape and type as `value`, shuffled along its first +// dimension. +func RandomShuffle(scope *Scope, value tf.Output, optional ...RandomShuffleAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomShuffle", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Selects elements from `x` or `y`, depending on `condition`. +// +// The `x`, and `y` tensors must all have the same shape, and the +// output will also have that shape. +// +// The `condition` tensor must be a scalar if `x` and `y` are scalars. +// If `x` and `y` are vectors or higher rank, then `condition` must be either a +// scalar, a vector with size matching the first dimension of `x`, or must have +// the same shape as `x`. +// +// The `condition` tensor acts as a mask that chooses, based on the value at each +// element, whether the corresponding element / row in the output should be +// taken from `x` (if true) or `y` (if false). +// +// If `condition` is a vector and `x` and `y` are higher rank matrices, then +// it chooses which row (outer dimension) to copy from `x` and `y`. +// If `condition` has the same shape as `x` and `y`, then it chooses which +// element to copy from `x` and `y`. +// +// For example: +// +// ```python +// # 'condition' tensor is [[True, False] +// # [False, True]] +// # 't' is [[1, 2], +// # [3, 4]] +// # 'e' is [[5, 6], +// # [7, 8]] +// select(condition, t, e) # => [[1, 6], [7, 4]] +// +// +// # 'condition' tensor is [True, False] +// # 't' is [[1, 2], +// # [3, 4]] +// # 'e' is [[5, 6], +// # [7, 8]] +// select(condition, t, e) ==> [[1, 2], +// [7, 8]] +// +// ``` +// +// Arguments: +// +// x: = A `Tensor` which may have the same shape as `condition`. +// If `condition` is rank 1, `x` may have higher rank, +// but its first dimension must match the size of `condition`. +// y: = A `Tensor` with the same type and shape as `x`. +// +// Returns = A `Tensor` with the same type and shape as `x` and `y`. +func Select(scope *Scope, condition tf.Output, x tf.Output, y tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Select", + Input: []tf.Input{ + condition, x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// The gradient operator for the SparseAdd op. +// +// The SparseAdd op calculates A + B, where A, B, and the sum are all represented +// as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. +// non-empty values of the sum, and outputs the gradients w.r.t. the non-empty +// values of A and B. +// +// Arguments: +// backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to +// the non-empty values of the sum. +// a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. +// b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. +// sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size +// `[nnz(sum), ndims]`. +// +// Returns: +// a_val_grad: 1-D with shape `[nnz(A)]`. The gradient with respect to the +// non-empty values of A. +// b_val_grad: 1-D with shape `[nnz(B)]`. The gradient with respect to the +// non-empty values of B. +func SparseAddGrad(scope *Scope, backprop_val_grad tf.Output, a_indices tf.Output, b_indices tf.Output, sum_indices tf.Output) (a_val_grad tf.Output, b_val_grad tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseAddGrad", + Input: []tf.Input{ + backprop_val_grad, a_indices, b_indices, sum_indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes the complementary error function of `x` element-wise. +func Erfc(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Erfc", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RandomUniformIntAttr is an optional argument to RandomUniformInt. +type RandomUniformIntAttr func(optionalAttr) + +// RandomUniformIntSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomUniformIntSeed(value int64) RandomUniformIntAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomUniformIntSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomUniformIntSeed2(value int64) RandomUniformIntAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random integers from a uniform distribution. +// +// The generated values are uniform integers in the range `[minval, maxval)`. +// The lower bound `minval` is included in the range, while the upper bound +// `maxval` is excluded. +// +// The random integers are slightly biased unless `maxval - minval` is an exact +// power of two. The bias is small for values of `maxval - minval` significantly +// smaller than the range of the output (either `2^32` or `2^64`). +// +// Arguments: +// shape: The shape of the output tensor. +// minval: 0-D. Inclusive lower bound on the generated integers. +// maxval: 0-D. Exclusive upper bound on the generated integers. +// +// Returns A tensor of the specified shape filled with uniform random integers. +func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf.Output, optional ...RandomUniformIntAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomUniformInt", + Input: []tf.Input{ + shape, minval, maxval, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StridedSliceGradAttr is an optional argument to StridedSliceGrad. +type StridedSliceGradAttr func(optionalAttr) + +// StridedSliceGradBeginMask sets the optional begin_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradBeginMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["begin_mask"] = value + } +} + +// StridedSliceGradEndMask sets the optional end_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradEndMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["end_mask"] = value + } +} + +// StridedSliceGradEllipsisMask sets the optional ellipsis_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradEllipsisMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["ellipsis_mask"] = value + } +} + +// StridedSliceGradNewAxisMask sets the optional new_axis_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradNewAxisMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["new_axis_mask"] = value + } +} + +// StridedSliceGradShrinkAxisMask sets the optional shrink_axis_mask attribute to value. +// If not specified, defaults to 0 +func StridedSliceGradShrinkAxisMask(value int64) StridedSliceGradAttr { + return func(m optionalAttr) { + m["shrink_axis_mask"] = value + } +} + +// Returns the gradient of `StridedSlice`. +// +// Since `StridedSlice` cuts out pieces of its `input` which is size +// `shape`, its gradient will have the same shape (which is passed here +// as `shape`). The gradient will be zero in any element that the slice +// does not select. +// +// Arguments are the same as StridedSliceGrad with the exception that +// `dy` is the input gradient to be propagated and `shape` is the +// shape of `StridedSlice`'s `input`. +func StridedSliceGrad(scope *Scope, shape tf.Output, begin tf.Output, end tf.Output, strides tf.Output, dy tf.Output, optional ...StridedSliceGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StridedSliceGrad", + Input: []tf.Input{ + shape, begin, end, strides, dy, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl. +type ResourceApplyFtrlAttr func(optionalAttr) + +// ResourceApplyFtrlUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyFtrlMultiplyLinearByLr sets the optional multiply_linear_by_lr attribute to value. +// If not specified, defaults to false +func ResourceApplyFtrlMultiplyLinearByLr(value bool) ResourceApplyFtrlAttr { + return func(m optionalAttr) { + m["multiply_linear_by_lr"] = value + } +} + +// Update '*var' according to the Ftrl-proximal scheme. +// +// accum_new = accum + grad * grad +// linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyFtrl(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, lr_power tf.Output, optional ...ResourceApplyFtrlAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyFtrl", + Input: []tf.Input{ + var_, accum, linear, grad, lr, l1, l2, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Creates a dataset that contains the unique elements of `input_dataset`. +func ExperimentalUniqueDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ExperimentalUniqueDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringFormatAttr is an optional argument to StringFormat. +type StringFormatAttr func(optionalAttr) + +// StringFormatTemplate sets the optional template attribute to value. +// +// value: A string, the template to format tensor summaries into. +// If not specified, defaults to "%s" +func StringFormatTemplate(value string) StringFormatAttr { + return func(m optionalAttr) { + m["template"] = value + } +} + +// StringFormatPlaceholder sets the optional placeholder attribute to value. +// +// value: A string, at each placeholder in the template a subsequent tensor summary will be inserted. +// If not specified, defaults to "%s" +func StringFormatPlaceholder(value string) StringFormatAttr { + return func(m optionalAttr) { + m["placeholder"] = value + } +} + +// StringFormatSummarize sets the optional summarize attribute to value. +// +// value: When formatting the tensor summaries print the first and last summarize entries of each tensor dimension. +// If not specified, defaults to 3 +func StringFormatSummarize(value int64) StringFormatAttr { + return func(m optionalAttr) { + m["summarize"] = value + } +} + +// Formats a string template using a list of tensors. +// +// Formats a string template using a list of tensors, pretty-printing tensor summaries. +// +// Arguments: +// inputs: The list of tensors to format into the placeholder string. +// +// Returns = The resulting string scalar. +func StringFormat(scope *Scope, inputs []tf.Output, optional ...StringFormatAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringFormat", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts a SparseTensor to a (possibly batched) CSRSparseMatrix. +// +// Arguments: +// indices: SparseTensor indices. +// values: SparseTensor values. +// dense_shape: SparseTensor dense shape. +// +// Returns A (possibly batched) CSRSparseMatrix. +func SparseTensorToCSRSparseMatrix(scope *Scope, indices tf.Output, values tf.Output, dense_shape tf.Output) (sparse_matrix tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorToCSRSparseMatrix", + Input: []tf.Input{ + indices, values, dense_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes fingerprints of the input strings. +// +// Arguments: +// input: vector of strings to compute fingerprints on. +// +// Returns a (N,2) shaped matrix where N is the number of elements in the input +// vector. Each row contains the low and high parts of the fingerprint. +func SdcaFprint(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SdcaFprint", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Inverse 3D fast Fourier transform. +// +// Computes the inverse 3-dimensional discrete Fourier transform over the +// inner-most 3 dimensions of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most 3 +// dimensions of `input` are replaced with their inverse 3D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifftn with 3 dimensions. +// @end_compatibility +func IFFT3D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT3D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2. +type QueueDequeueUpToV2Attr func(optionalAttr) + +// QueueDequeueUpToV2TimeoutMs sets the optional timeout_ms attribute to value. +// +// value: If the queue has fewer than n elements, this operation +// will block for up to timeout_ms milliseconds. +// Note: This option is not supported yet. +// If not specified, defaults to -1 +func QueueDequeueUpToV2TimeoutMs(value int64) QueueDequeueUpToV2Attr { + return func(m optionalAttr) { + m["timeout_ms"] = value + } +} + +// Dequeues `n` tuples of one or more tensors from the given queue. +// +// This operation is not supported by all queues. If a queue does not support +// DequeueUpTo, then an Unimplemented error is returned. +// +// If the queue is closed and there are more than 0 but less than `n` +// elements remaining, then instead of returning an OutOfRange error like +// QueueDequeueMany, less than `n` elements are returned immediately. If +// the queue is closed and there are 0 elements left in the queue, then +// an OutOfRange error is returned just like in QueueDequeueMany. +// Otherwise the behavior is identical to QueueDequeueMany: +// +// This operation concatenates queue-element component tensors along the +// 0th dimension to make a single component tensor. All of the components +// in the dequeued tuple will have size n in the 0th dimension. +// +// This operation has `k` outputs, where `k` is the number of components in +// the tuples stored in the given queue, and output `i` is the ith +// component of the dequeued tuple. +// +// Arguments: +// handle: The handle to a queue. +// n: The number of tuples to dequeue. +// component_types: The type of each component in a tuple. +// +// Returns One or more tensors that were dequeued as a tuple. +func QueueDequeueUpToV2(scope *Scope, handle tf.Output, n tf.Output, component_types []tf.DataType, optional ...QueueDequeueUpToV2Attr) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QueueDequeueUpToV2", + Input: []tf.Input{ + handle, n, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("QueueDequeueUpToV2", err) + return + } + return components +} + +// Says whether the targets are in the top `K` predictions. +// +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. +// +// More formally, let +// +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// +// Arguments: +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. +// +// Returns Computed Precision at `k` as a `bool Tensor`. +func InTopK(scope *Scope, predictions tf.Output, targets tf.Output, k int64) (precision tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"k": k} + opspec := tf.OpSpec{ + Type: "InTopK", + Input: []tf.Input{ + predictions, targets, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x - y element-wise. +// +// *NOTE*: `Subtract` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Sub(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sub", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FusedResizeAndPadConv2DAttr is an optional argument to FusedResizeAndPadConv2D. +type FusedResizeAndPadConv2DAttr func(optionalAttr) + +// FusedResizeAndPadConv2DResizeAlignCorners sets the optional resize_align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func FusedResizeAndPadConv2DResizeAlignCorners(value bool) FusedResizeAndPadConv2DAttr { + return func(m optionalAttr) { + m["resize_align_corners"] = value + } +} + +// Performs a resize and padding as a preprocess during a convolution. +// +// It's often possible to do spatial transformations more efficiently as part of +// the packing stage of a convolution, so this op allows for an optimized +// implementation where these stages are fused together. This prevents the need to +// write out the intermediate results as whole tensors, reducing memory pressure, +// and we can get some latency gains by merging the transformation calculations. +// The data_format attribute for Conv2D isn't supported by this op, and defaults to +// 'NHWC' order. +// Internally this op uses a single per-graph scratch buffer, which means that it +// will block if multiple versions are being run in parallel. This is because this +// operator is primarily an optimization to minimize memory usage. +// +// Arguments: +// input: 4-D with shape `[batch, in_height, in_width, in_channels]`. +// size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// filter: 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. +// +// strides: 1-D of length 4. The stride of the sliding window for each dimension +// of `input`. Must be in the same order as the dimension specified with format. +// padding: The type of padding algorithm to use. +func FusedResizeAndPadConv2D(scope *Scope, input tf.Output, size tf.Output, paddings tf.Output, filter tf.Output, mode string, strides []int64, padding string, optional ...FusedResizeAndPadConv2DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FusedResizeAndPadConv2D", + Input: []tf.Input{ + input, size, paddings, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the product along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// This operator is similar to the unsorted segment sum operator found +// [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum). +// Instead of computing the sum over segments, it computes the product of all +// entries belonging to a segment such that: +// +// \\(output_i = \prod_{j...} data[j...]\\) where the product is over tuples +// `j...` such that `segment_ids[j...] == i`. +// +// For example: +// +// ``` python +// c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]]) +// tf.unsorted_segment_prod(c, tf.constant([0, 1, 0]), num_segments=2) +// # ==> [[ 4, 6, 6, 4], +// # [5, 6, 7, 8]] +// ``` +// +// If there is no entry for a given segment ID `i`, it outputs 1. +// +// If the given segment ID `i` is negative, then the corresponding value is +// dropped, and will not be included in the result. +// +// Arguments: +// +// segment_ids: A tensor whose shape is a prefix of `data.shape`. +// +// +// Returns Has same shape as data, except for the first `segment_ids.rank` +// dimensions, which are replaced with a single dimension which has size +// `num_segments`. +func UnsortedSegmentProd(scope *Scope, data tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnsortedSegmentProd", + Input: []tf.Input{ + data, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel. +type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor of type float via per-channel floats +// +// Fake-quantize the `inputs` tensor of type float per-channel and one of the +// shapes: `[d]`, `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` +// of shape `[d]` to `outputs` tensor of same shape as `inputs`. +// +// Attributes +// +// * `[min; max]` define the clamping range for the `inputs` data. +// * `inputs` values are quantized into the quantization range ( +// `[0; 2^num_bits - 1]` when `narrow_range` is false and `[1; 2^num_bits - 1]` +// when it is true) and then de-quantized and output as floats in `[min; max]` +// interval. +// * `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// +// Before quantization, `min` and `max` values are adjusted with the following +// logic. +// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values, +// the behavior can be unexpected: +// +// * If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`. +// * If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`. +// * If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `, +// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`. +// +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVarsPerChannel", + Input: []tf.Input{ + inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Checks whether a resource handle-based variable has been initialized. +// +// Arguments: +// resource: the input resource handle. +// +// Returns a scalar boolean which is true if the variable has been +// initialized. +func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "VarIsInitializedOp", + Input: []tf.Input{ + resource, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AngleAttr is an optional argument to Angle. +type AngleAttr func(optionalAttr) + +// AngleTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func AngleTout(value tf.DataType) AngleAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the argument of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the argument of each element in `input`. All elements in +// `input` must be complex numbers of the form \\(a + bj\\), where *a* +// is the real part and *b* is the imaginary part. +// +// The argument returned by this operation is of the form \\(atan2(b, a)\\). +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.angle(input) ==> [2.0132, 1.056] +// ``` +// +// @compatibility(numpy) +// Equivalent to np.angle. +// @end_compatibility +func Angle(scope *Scope, input tf.Output, optional ...AngleAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Angle", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes natural logarithm of x element-wise. +// +// I.e., \\(y = \log_e x\\). +// +// Example: +// +// ```python +// x = tf.constant([0, 0.5, 1, 5]) +// tf.math.log(x) ==> [-inf, -0.6931472, 0. , 1.609438] +// ``` +func Log(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Log", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// IRFFT2DAttr is an optional argument to IRFFT2D. +type IRFFT2DAttr func(optionalAttr) + +// IRFFT2DTreal sets the optional Treal attribute to value. +// If not specified, defaults to DT_FLOAT +func IRFFT2DTreal(value tf.DataType) IRFFT2DAttr { + return func(m optionalAttr) { + m["Treal"] = value + } +} + +// Inverse 2D real-valued fast Fourier transform. +// +// Computes the inverse 2-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most 2 dimensions of `input`. +// +// The inner-most 2 dimensions of `input` are assumed to be the result of `RFFT2D`: +// The inner-most dimension contains the `fft_length / 2 + 1` unique components of +// the DFT of a real-valued signal. If `fft_length` is not provided, it is computed +// from the size of the inner-most 2 dimensions of `input`. If the FFT length used +// to compute `input` is odd, it should be provided since it cannot be inferred +// properly. +// +// Along each axis `IRFFT2D` is computed on, if `fft_length` (or +// `fft_length / 2 + 1` for the inner-most dimension) is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A complex tensor. +// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most 2 +// dimensions of `input` are replaced with the `fft_length` samples of their +// inverse 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.irfft2 +// @end_compatibility +func IRFFT2D(scope *Scope, input tf.Output, fft_length tf.Output, optional ...IRFFT2DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IRFFT2D", + Input: []tf.Input{ + input, fft_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResizeBicubicAttr is an optional argument to ResizeBicubic. +type ResizeBicubicAttr func(optionalAttr) + +// ResizeBicubicAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeBicubicAlignCorners(value bool) ResizeBicubicAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeBicubicHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeBicubicHalfPixelCenters(value bool) ResizeBicubicAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize `images` to `size` using bicubic interpolation. +// +// Input images can be of different types but output images are always float. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeBicubic(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeBicubicAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeBicubic", + Input: []tf.Input{ + images, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the mean along sparse segments of a tensor. +// +// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is +// missing, the `output` tensor at that position will be zeroed. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// num_segments: Should equal the number of distinct segment IDs. +// +// Returns Has same shape as data, except for dimension 0 which has size +// `num_segments`. +func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentMeanWithNumSegments", + Input: []tf.Input{ + data, indices, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RFFT2DAttr is an optional argument to RFFT2D. +type RFFT2DAttr func(optionalAttr) + +// RFFT2DTcomplex sets the optional Tcomplex attribute to value. +// If not specified, defaults to DT_COMPLEX64 +func RFFT2DTcomplex(value tf.DataType) RFFT2DAttr { + return func(m optionalAttr) { + m["Tcomplex"] = value + } +} + +// 2D real-valued fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most 2 dimensions of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the +// `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension +// of `output`: the zero-frequency term, followed by the `fft_length / 2` +// positive-frequency terms. +// +// Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [2]. The FFT length for each dimension. +// +// Returns A complex64 tensor of the same rank as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. The +// inner-most dimension contains `fft_length / 2 + 1` unique frequency +// components. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfft2 +// @end_compatibility +func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output, optional ...RFFT2DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RFFT2D", + Input: []tf.Input{ + input, fft_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RFFTAttr is an optional argument to RFFT. +type RFFTAttr func(optionalAttr) + +// RFFTTcomplex sets the optional Tcomplex attribute to value. +// If not specified, defaults to DT_COMPLEX64 +func RFFTTcomplex(value tf.DataType) RFFTAttr { + return func(m optionalAttr) { + m["Tcomplex"] = value + } +} + +// Real-valued fast Fourier transform. +// +// Computes the 1-dimensional discrete Fourier transform of a real-valued signal +// over the inner-most dimension of `input`. +// +// Since the DFT of a real signal is Hermitian-symmetric, `RFFT` only returns the +// `fft_length / 2 + 1` unique components of the FFT: the zero-frequency term, +// followed by the `fft_length / 2` positive-frequency terms. +// +// Along the axis `RFFT` is computed on, if `fft_length` is smaller than the +// corresponding dimension of `input`, the dimension is cropped. If it is larger, +// the dimension is padded with zeros. +// +// Arguments: +// input: A float32 tensor. +// fft_length: An int32 tensor of shape [1]. The FFT length. +// +// Returns A complex64 tensor of the same rank as `input`. The inner-most +// dimension of `input` is replaced with the `fft_length / 2 + 1` unique +// frequency components of its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.rfft +// @end_compatibility +func RFFT(scope *Scope, input tf.Output, fft_length tf.Output, optional ...RFFTAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RFFT", + Input: []tf.Input{ + input, fft_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 3D fast Fourier transform. +// +// Computes the 3-dimensional discrete Fourier transform over the inner-most 3 +// dimensions of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most 3 +// dimensions of `input` are replaced with their 3D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fftn with 3 dimensions. +// @end_compatibility +func FFT3D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT3D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that passes a sliding window over `input_dataset`. +// +// Arguments: +// +// window_size: A scalar representing the number of elements in the +// sliding window. +// window_shift: A scalar representing the steps moving the sliding window +// forward in one iteration. It must be positive. +// window_stride: A scalar representing the stride of the input elements of the sliding window. +// It must be positive. +// +// +func SlidingWindowDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, window_shift tf.Output, window_stride tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "SlidingWindowDataset", + Input: []tf.Input{ + input_dataset, window_size, window_shift, window_stride, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Locks a mutex resource. The output is the lock. So long as the lock tensor +// +// is alive, any other request to use `MutexLock` with this mutex will wait. +// +// This is particularly useful for creating a critical section when used in +// conjunction with `MutexLockIdentity`: +// +// ```python +// +// mutex = mutex_v2( +// shared_name=handle_name, container=container, name=name) +// +// def execute_in_critical_section(fn, *args, **kwargs): +// lock = gen_resource_variable_ops.mutex_lock(mutex) +// +// with ops.control_dependencies([lock]): +// r = fn(*args, **kwargs) +// +// with ops.control_dependencies(nest.flatten(r)): +// with ops.colocate_with(mutex): +// ensure_lock_exists = mutex_lock_identity(lock) +// +// # Make sure that if any element of r is accessed, all of +// # them are executed together. +// r = nest.map_structure(tf.identity, r) +// +// with ops.control_dependencies([ensure_lock_exists]): +// return nest.map_structure(tf.identity, r) +// ``` +// +// While `fn` is running in the critical section, no other functions which wish to +// use this critical section may run. +// +// Often the use case is that two executions of the same graph, in parallel, +// wish to run `fn`; and we wish to ensure that only one of them executes +// at a time. This is especially important if `fn` modifies one or more +// variables at a time. +// +// It is also useful if two separate functions must share a resource, but we +// wish to ensure the usage is exclusive. +// +// Arguments: +// mutex: The mutex resource to lock. +// +// Returns A tensor that keeps a shared pointer to a lock on the mutex; +// when the Tensor is destroyed, the use count on the shared pointer is decreased +// by 1. When it reaches 0, the lock is released. +func MutexLock(scope *Scope, mutex tf.Output) (mutex_lock tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MutexLock", + Input: []tf.Input{ + mutex, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MaxPoolGradWithArgmaxAttr is an optional argument to MaxPoolGradWithArgmax. +type MaxPoolGradWithArgmaxAttr func(optionalAttr) + +// MaxPoolGradWithArgmaxIncludeBatchInIndex sets the optional include_batch_in_index attribute to value. +// +// value: Whether to include batch dimension in flattened index of `argmax`. +// If not specified, defaults to false +func MaxPoolGradWithArgmaxIncludeBatchInIndex(value bool) MaxPoolGradWithArgmaxAttr { + return func(m optionalAttr) { + m["include_batch_in_index"] = value + } +} + +// Computes gradients of the maxpooling function. +// +// Arguments: +// input: The original input. +// grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the +// output of `max_pool`. +// argmax: The indices of the maximum values chosen for each output of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients w.r.t. the input of `max_pool`. +func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradWithArgmaxAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradWithArgmax", + Input: []tf.Input{ + input, grad, argmax, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// 2D fast Fourier transform. +// +// Computes the 2-dimensional discrete Fourier transform over the inner-most +// 2 dimensions of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most 2 +// dimensions of `input` are replaced with their 2D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft2 +// @end_compatibility +func FFT2D(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT2D", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SdcaOptimizerAttr is an optional argument to SdcaOptimizer. +type SdcaOptimizerAttr func(optionalAttr) + +// SdcaOptimizerAdaptative sets the optional adaptative attribute to value. +// +// value: Whether to use Adaptive SDCA for the inner loop. +// If not specified, defaults to true +func SdcaOptimizerAdaptative(value bool) SdcaOptimizerAttr { + return func(m optionalAttr) { + m["adaptative"] = value + } +} + +// Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for +// +// linear models with L1 + L2 regularization. As global optimization objective is +// strongly-convex, the optimizer optimizes the dual objective at each step. The +// optimizer applies each update one example at a time. Examples are sampled +// uniformly, and the optimizer is learning rate free and enjoys linear convergence +// rate. +// +// [Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
+// Shai Shalev-Shwartz, Tong Zhang. 2012 +// +// $$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$ +// +// [Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
+// Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, +// Peter Richtarik, Martin Takac. 2015 +// +// [Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
+// Dominik Csiba, Zheng Qu, Peter Richtarik. 2015 +// +// Arguments: +// sparse_example_indices: a list of vectors which contain example indices. +// sparse_feature_indices: a list of vectors which contain feature indices. +// sparse_feature_values: a list of vectors which contains feature value +// associated with each feature group. +// dense_features: a list of matrices which contains the dense feature values. +// example_weights: a vector which contains the weight associated with each +// example. +// example_labels: a vector which contains the label/target associated with each +// example. +// sparse_indices: a list of vectors where each value is the indices which has +// corresponding weights in sparse_weights. This field maybe omitted for the +// dense approach. +// sparse_weights: a list of vectors where each value is the weight associated with +// a sparse feature group. +// dense_weights: a list of vectors where the values are the weights associated +// with a dense feature group. +// example_state_data: a list of vectors containing the example state data. +// loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, +// squared and hinge losses. +// l1: Symmetric l1 regularization strength. +// l2: Symmetric l2 regularization strength. +// num_loss_partitions: Number of partitions of the global loss function. +// num_inner_iterations: Number of iterations per mini-batch. +// +// Returns: +// out_example_state_data: a list of vectors containing the updated example state +// data. +// out_delta_sparse_weights: a list of vectors where each value is the delta +// weights associated with a sparse feature group. +// out_delta_dense_weights: a list of vectors where the values are the delta +// weights associated with a dense feature group. +func SdcaOptimizer(scope *Scope, sparse_example_indices []tf.Output, sparse_feature_indices []tf.Output, sparse_feature_values []tf.Output, dense_features []tf.Output, example_weights tf.Output, example_labels tf.Output, sparse_indices []tf.Output, sparse_weights []tf.Output, dense_weights []tf.Output, example_state_data tf.Output, loss_type string, l1 float32, l2 float32, num_loss_partitions int64, num_inner_iterations int64, optional ...SdcaOptimizerAttr) (out_example_state_data tf.Output, out_delta_sparse_weights []tf.Output, out_delta_dense_weights []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"loss_type": loss_type, "l1": l1, "l2": l2, "num_loss_partitions": num_loss_partitions, "num_inner_iterations": num_inner_iterations} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SdcaOptimizer", + Input: []tf.Input{ + tf.OutputList(sparse_example_indices), tf.OutputList(sparse_feature_indices), tf.OutputList(sparse_feature_values), tf.OutputList(dense_features), example_weights, example_labels, tf.OutputList(sparse_indices), tf.OutputList(sparse_weights), tf.OutputList(dense_weights), example_state_data, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + out_example_state_data = op.Output(idx) + if out_delta_sparse_weights, idx, err = makeOutputList(op, idx, "out_delta_sparse_weights"); err != nil { + scope.UpdateErr("SdcaOptimizer", err) + return + } + if out_delta_dense_weights, idx, err = makeOutputList(op, idx, "out_delta_dense_weights"); err != nil { + scope.UpdateErr("SdcaOptimizer", err) + return + } + return out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights +} + +// Inverse fast Fourier transform. +// +// Computes the inverse 1-dimensional discrete Fourier transform over the +// inner-most dimension of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its inverse 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.ifft +// @end_compatibility +func IFFT(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IFFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CollectiveGatherAttr is an optional argument to CollectiveGather. +type CollectiveGatherAttr func(optionalAttr) + +// CollectiveGatherCommunicationHint sets the optional communication_hint attribute to value. +// If not specified, defaults to "auto" +func CollectiveGatherCommunicationHint(value string) CollectiveGatherAttr { + return func(m optionalAttr) { + m["communication_hint"] = value + } +} + +// CollectiveGatherTimeoutSeconds sets the optional timeout_seconds attribute to value. +// If not specified, defaults to 0 +func CollectiveGatherTimeoutSeconds(value float32) CollectiveGatherAttr { + return func(m optionalAttr) { + m["timeout_seconds"] = value + } +} + +// Mutually accumulates multiple tensors of identical type and shape. +func CollectiveGather(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, shape tf.Shape, optional ...CollectiveGatherAttr) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CollectiveGather", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// L2 Loss. +// +// Computes half the L2 norm of a tensor without the `sqrt`: +// +// output = sum(t ** 2) / 2 +// +// Arguments: +// t: Typically 2-D, but may have any dimensions. +// +// Returns 0-D. +func L2Loss(scope *Scope, t tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "L2Loss", + Input: []tf.Input{ + t, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An op that receives embedding activations on the TPU. +// +// The TPU system performs the embedding lookups and aggregations specified by +// the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The +// results of these aggregations are visible to the Tensorflow Graph as the +// outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing +// one Tensor of activations per table specified in the model. There can be at +// most one RecvTPUEmbeddingActivations op in the TPU graph. +// +// Arguments: +// num_outputs: The number of output activation tensors, equal to the number of +// embedding tables in the model. +// config: Serialized TPUEmbeddingConfiguration proto. +// +// Returns A TensorList of embedding activations containing one Tensor per +// embedding table in the model. +func RecvTPUEmbeddingActivations(scope *Scope, num_outputs int64, config string) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_outputs": num_outputs, "config": config} + opspec := tf.OpSpec{ + Type: "RecvTPUEmbeddingActivations", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("RecvTPUEmbeddingActivations", err) + return + } + return outputs +} + +// InfeedEnqueuePrelinearizedBufferAttr is an optional argument to InfeedEnqueuePrelinearizedBuffer. +type InfeedEnqueuePrelinearizedBufferAttr func(optionalAttr) + +// InfeedEnqueuePrelinearizedBufferDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op is running on a TPU device +// and = 0 when the Op is running on the CPU device. +// If not specified, defaults to -1 +func InfeedEnqueuePrelinearizedBufferDeviceOrdinal(value int64) InfeedEnqueuePrelinearizedBufferAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// An op which enqueues prelinearized buffer into TPU infeed. +// +// Arguments: +// input: A variant tensor representing linearized output. +// +// Returns the created operation. +func InfeedEnqueuePrelinearizedBuffer(scope *Scope, input tf.Output, optional ...InfeedEnqueuePrelinearizedBufferAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InfeedEnqueuePrelinearizedBuffer", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the derivative of a Gamma random sample w.r.t. `alpha`. +func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RandomGammaGrad", + Input: []tf.Input{ + alpha, sample, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LRNGradAttr is an optional argument to LRNGrad. +type LRNGradAttr func(optionalAttr) + +// LRNGradDepthRadius sets the optional depth_radius attribute to value. +// +// value: A depth radius. +// If not specified, defaults to 5 +func LRNGradDepthRadius(value int64) LRNGradAttr { + return func(m optionalAttr) { + m["depth_radius"] = value + } +} + +// LRNGradBias sets the optional bias attribute to value. +// +// value: An offset (usually > 0 to avoid dividing by 0). +// If not specified, defaults to 1 +func LRNGradBias(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["bias"] = value + } +} + +// LRNGradAlpha sets the optional alpha attribute to value. +// +// value: A scale factor, usually positive. +// If not specified, defaults to 1 +func LRNGradAlpha(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// LRNGradBeta sets the optional beta attribute to value. +// +// value: An exponent. +// If not specified, defaults to 0.5 +func LRNGradBeta(value float32) LRNGradAttr { + return func(m optionalAttr) { + m["beta"] = value + } +} + +// Gradients for Local Response Normalization. +// +// Arguments: +// input_grads: 4-D with shape `[batch, height, width, channels]`. +// input_image: 4-D with shape `[batch, height, width, channels]`. +// output_image: 4-D with shape `[batch, height, width, channels]`. +// +// Returns The gradients for LRN. +func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LRNGrad", + Input: []tf.Input{ + input_grads, input_image, output_image, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PrelinearizeAttr is an optional argument to Prelinearize. +type PrelinearizeAttr func(optionalAttr) + +// PrelinearizeShape sets the optional shape attribute to value. +// +// value: The shape of the tensor. +// If not specified, defaults to <> +func PrelinearizeShape(value tf.Shape) PrelinearizeAttr { + return func(m optionalAttr) { + m["shape"] = value + } +} + +// PrelinearizeLayout sets the optional layout attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence. If a layout +// attribute is passed but its values are all -1 the layout will be computed by +// the infeed operation. +// If not specified, defaults to <> +func PrelinearizeLayout(value []int64) PrelinearizeAttr { + return func(m optionalAttr) { + m["layout"] = value + } +} + +// An op which linearizes one Tensor value to an opaque variant tensor. +// +// Arguments: +// input: A tensor that will be linearized. +func Prelinearize(scope *Scope, input tf.Output, optional ...PrelinearizeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Prelinearize", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sparse Cholesky decomposition of `input`. +// +// Computes the Sparse Cholesky decomposition of a sparse matrix, with the given +// fill-in reducing permutation. +// +// The input sparse matrix and the fill-in reducing permutation `permutation` must +// have compatible shapes. If the sparse matrix has rank 3; with the batch +// dimension `B`, then the `permutation` must be of rank 2; with the same batch +// dimension `B`. There is no support for broadcasting. +// +// Furthermore, each component vector of `permutation` must be of length `N`, +// containing each of the integers {0, 1, ..., N - 1} exactly once, where `N` is +// the number of rows of each component of the sparse matrix. +// +// Each component of the input sparse matrix must represent a symmetric positive +// definite (SPD) matrix; although only the lower triangular part of the matrix is +// read. If any individual component is not SPD, then an InvalidArgument error is +// thrown. +// +// The returned sparse matrix has the same dense shape as the input sparse matrix. +// For each component `A` of the input sparse matrix, the corresponding output +// sparse matrix represents `L`, the lower triangular Cholesky factor satisfying +// the following identity: +// +// ``` +// A = L * Lt +// ``` +// +// where Lt denotes the transpose of L (or its conjugate transpose, if `type` is +// `complex64` or `complex128`). +// +// The `type` parameter denotes the type of the matrix elements. The supported +// types are: `float32`, `float64`, `complex64` and `complex128`. +// +// Usage example: +// +// ```python +// from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops +// +// a_indices = np.array([[0, 0], [1, 1], [2, 1], [2, 2], [3, 3]]) +// a_values = np.array([1.0, 2.0, 1.0, 3.0, 4.0], np.float32) +// a_dense_shape = [4, 4] +// +// with tf.Session() as sess: +// # Define (COO format) SparseTensor over Numpy array. +// a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape) +// +// # Convert SparseTensors to CSR SparseMatrix. +// a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix( +// a_st.indices, a_st.values, a_st.dense_shape) +// +// # Obtain the Sparse Cholesky factor using AMD Ordering for reducing zero +// # fill-in (number of structural non-zeros in the sparse Cholesky factor). +// ordering_amd = sparse_csr_matrix_ops.sparse_matrix_ordering_amd(sparse_matrix) +// cholesky_sparse_matrices = ( +// sparse_csr_matrix_ops.sparse_matrix_sparse_cholesky( +// sparse_matrix, ordering_amd, type=tf.float32)) +// +// # Convert the CSRSparseMatrix Cholesky factor to a dense Tensor +// dense_cholesky = sparse_csr_matrix_ops.csr_sparse_matrix_to_dense( +// cholesky_sparse_matrices, tf.float32) +// +// # Evaluate the dense Tensor value. +// dense_cholesky_value = sess.run(dense_cholesky) +// ``` +// +// `dense_cholesky_value` stores the dense Cholesky factor: +// +// ``` +// [[ 1. 0. 0. 0.] +// [ 0. 1.41 0. 0.] +// [ 0. 0.70 1.58 0.] +// [ 0. 0. 0. 2.]] +// ``` +// +// +// input: A `CSRSparseMatrix`. +// permutation: A `Tensor`. +// type: The type of `input`. +// +// Arguments: +// input: A `CSRSparseMatrix`. +// permutation: A fill-in reducing permutation matrix. +// +// +// Returns The sparse Cholesky decompsition of `input`. +func SparseMatrixSparseCholesky(scope *Scope, input tf.Output, permutation tf.Output, type_ tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "SparseMatrixSparseCholesky", + Input: []tf.Input{ + input, permutation, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatefulTruncatedNormalAttr is an optional argument to StatefulTruncatedNormal. +type StatefulTruncatedNormalAttr func(optionalAttr) + +// StatefulTruncatedNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatefulTruncatedNormalDtype(value tf.DataType) StatefulTruncatedNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random values from a truncated normal distribution. +// +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns Random values with specified shape. +func StatefulTruncatedNormal(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulTruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulTruncatedNormal", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns true if queue is closed. +// +// This operation returns true if the queue is closed and false if the queue +// is open. +// +// Arguments: +// handle: The handle to a queue. +func QueueIsClosedV2(scope *Scope, handle tf.Output) (is_closed tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "QueueIsClosedV2", + Input: []tf.Input{ + handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Checks whether a quantile stream has been initialized. +// +// An Op that checks if quantile stream resource is initialized. +// +// Arguments: +// quantile_stream_resource_handle: resource; The reference to quantile stream resource handle. +// +// Returns bool; True if the resource is initialized, False otherwise. +func IsBoostedTreesQuantileStreamResourceInitialized(scope *Scope, quantile_stream_resource_handle tf.Output) (is_initialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsBoostedTreesQuantileStreamResourceInitialized", + Input: []tf.Input{ + quantile_stream_resource_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ParseSequenceExampleAttr is an optional argument to ParseSequenceExample. +type ParseSequenceExampleAttr func(optionalAttr) + +// ParseSequenceExampleNcontextSparse sets the optional Ncontext_sparse attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleNcontextSparse(value int64) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["Ncontext_sparse"] = value + } +} + +// ParseSequenceExampleNcontextDense sets the optional Ncontext_dense attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleNcontextDense(value int64) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["Ncontext_dense"] = value + } +} + +// ParseSequenceExampleNfeatureListSparse sets the optional Nfeature_list_sparse attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleNfeatureListSparse(value int64) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["Nfeature_list_sparse"] = value + } +} + +// ParseSequenceExampleNfeatureListDense sets the optional Nfeature_list_dense attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func ParseSequenceExampleNfeatureListDense(value int64) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["Nfeature_list_dense"] = value + } +} + +// ParseSequenceExampleContextSparseTypes sets the optional context_sparse_types attribute to value. +// +// value: A list of Ncontext_sparse types; the data types of data in +// each context Feature given in context_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleContextSparseTypes(value []tf.DataType) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["context_sparse_types"] = value + } +} + +// ParseSequenceExampleFeatureListDenseTypes sets the optional feature_list_dense_types attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleFeatureListDenseTypes(value []tf.DataType) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_dense_types"] = value + } +} + +// ParseSequenceExampleContextDenseShapes sets the optional context_dense_shapes attribute to value. +// +// value: A list of Ncontext_dense shapes; the shapes of data in +// each context Feature given in context_dense_keys. +// The number of elements in the Feature corresponding to context_dense_key[j] +// must always equal context_dense_shapes[j].NumEntries(). +// The shape of context_dense_values[j] will match context_dense_shapes[j]. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleContextDenseShapes(value []tf.Shape) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["context_dense_shapes"] = value + } +} + +// ParseSequenceExampleFeatureListSparseTypes sets the optional feature_list_sparse_types attribute to value. +// +// value: A list of Nfeature_list_sparse types; the data types +// of data in each FeatureList given in feature_list_sparse_keys. +// Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleFeatureListSparseTypes(value []tf.DataType) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_sparse_types"] = value + } +} + +// ParseSequenceExampleFeatureListDenseShapes sets the optional feature_list_dense_shapes attribute to value. +// +// value: A list of Nfeature_list_dense shapes; the shapes of +// data in each FeatureList given in feature_list_dense_keys. +// The shape of each Feature in the FeatureList corresponding to +// feature_list_dense_key[j] must always equal +// feature_list_dense_shapes[j].NumEntries(). +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func ParseSequenceExampleFeatureListDenseShapes(value []tf.Shape) ParseSequenceExampleAttr { + return func(m optionalAttr) { + m["feature_list_dense_shapes"] = value + } +} + +// Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors. +// +// Arguments: +// serialized: A vector containing binary serialized SequenceExample protos. +// debug_name: A vector containing the names of the serialized protos. +// May contain, for example, table key (descriptive) name for the +// corresponding serialized proto. This is purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty vector if no name is available. +// context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty). +// context_dense_defaults[j] provides default values +// when the SequenceExample's context map lacks context_dense_key[j]. +// If an empty Tensor is provided for context_dense_defaults[j], +// then the Feature context_dense_keys[j] is required. +// The input type is inferred from context_dense_defaults[j], even when it's +// empty. If context_dense_defaults[j] is not empty, its shape must match +// context_dense_shapes[j]. +// feature_list_dense_missing_assumed_empty: A vector listing the +// FeatureList keys which may be missing from the SequenceExamples. If the +// associated FeatureList is missing, it is treated as empty. By default, +// any FeatureList not listed in this vector must exist in the SequenceExamples. +// context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars). +// The keys expected in the Examples' features associated with context_sparse +// values. +// context_dense_keys: A list of Ncontext_dense string Tensors (scalars). +// The keys expected in the SequenceExamples' context features associated with +// dense values. +// feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors +// (scalars). The keys expected in the FeatureLists associated with sparse +// values. +// feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars). +// The keys expected in the SequenceExamples' feature_lists associated +// with lists of dense values. +func ParseSequenceExample(scope *Scope, serialized tf.Output, debug_name tf.Output, context_dense_defaults []tf.Output, feature_list_dense_missing_assumed_empty []string, context_sparse_keys []string, context_dense_keys []string, feature_list_sparse_keys []string, feature_list_dense_keys []string, optional ...ParseSequenceExampleAttr) (context_sparse_indices []tf.Output, context_sparse_values []tf.Output, context_sparse_shapes []tf.Output, context_dense_values []tf.Output, feature_list_sparse_indices []tf.Output, feature_list_sparse_values []tf.Output, feature_list_sparse_shapes []tf.Output, feature_list_dense_values []tf.Output, feature_list_dense_lengths []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"feature_list_dense_missing_assumed_empty": feature_list_dense_missing_assumed_empty, "context_sparse_keys": context_sparse_keys, "context_dense_keys": context_dense_keys, "feature_list_sparse_keys": feature_list_sparse_keys, "feature_list_dense_keys": feature_list_dense_keys} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ParseSequenceExample", + Input: []tf.Input{ + serialized, debug_name, tf.OutputList(context_dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if context_sparse_indices, idx, err = makeOutputList(op, idx, "context_sparse_indices"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if context_sparse_values, idx, err = makeOutputList(op, idx, "context_sparse_values"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if context_sparse_shapes, idx, err = makeOutputList(op, idx, "context_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if context_dense_values, idx, err = makeOutputList(op, idx, "context_dense_values"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if feature_list_sparse_indices, idx, err = makeOutputList(op, idx, "feature_list_sparse_indices"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if feature_list_sparse_values, idx, err = makeOutputList(op, idx, "feature_list_sparse_values"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if feature_list_sparse_shapes, idx, err = makeOutputList(op, idx, "feature_list_sparse_shapes"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if feature_list_dense_values, idx, err = makeOutputList(op, idx, "feature_list_dense_values"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + if feature_list_dense_lengths, idx, err = makeOutputList(op, idx, "feature_list_dense_lengths"); err != nil { + scope.UpdateErr("ParseSequenceExample", err) + return + } + return context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values, feature_list_dense_lengths +} + +// Fast Fourier transform. +// +// Computes the 1-dimensional discrete Fourier transform over the inner-most +// dimension of `input`. +// +// Arguments: +// input: A complex tensor. +// +// Returns A complex tensor of the same shape as `input`. The inner-most +// dimension of `input` is replaced with its 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.fft +// @end_compatibility +func FFT(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FFT", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UniqueV2Attr is an optional argument to UniqueV2. +type UniqueV2Attr func(optionalAttr) + +// UniqueV2OutIdx sets the optional out_idx attribute to value. +// If not specified, defaults to DT_INT32 +func UniqueV2OutIdx(value tf.DataType) UniqueV2Attr { + return func(m optionalAttr) { + m["out_idx"] = value + } +} + +// Finds unique elements along an axis of a tensor. +// +// This operation either returns a tensor `y` containing unique elements +// along the `axis` of a tensor. The returned unique elements is sorted +// in the same order as they occur along `axis` in `x`. +// This operation also returns a tensor `idx` that is the same size as +// the number of the elements in `x` along the `axis` dimension. It +// contains the index in the unique output `y`. +// In other words, for an `1-D` tensor `x` with `axis = None: +// +// `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` +// +// For example: +// +// ``` +// # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] +// y, idx = unique(x) +// y ==> [1, 2, 4, 7, 8] +// idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] +// ``` +// +// For an `2-D` tensor `x` with `axis = 0`: +// +// ``` +// # tensor 'x' is [[1, 0, 0], +// # [1, 0, 0], +// # [2, 0, 0]] +// y, idx = unique(x, axis=0) +// y ==> [[1, 0, 0], +// [2, 0, 0]] +// idx ==> [0, 0, 1] +// ``` +// +// For an `2-D` tensor `x` with `axis = 1`: +// +// ``` +// # tensor 'x' is [[1, 0, 0], +// # [1, 0, 0], +// # [2, 0, 0]] +// y, idx = unique(x, axis=1) +// y ==> [[1, 0], +// [1, 0], +// [2, 0]] +// idx ==> [0, 1, 1] +// ``` +// +// Arguments: +// x: A `Tensor`. +// axis: A `Tensor` of type `int32` (default: None). The axis of the Tensor to +// find the unique elements. +// +// Returns: +// y: A `Tensor`. Unique elements along the `axis` of `Tensor` x. +// idx: A 1-D Tensor. Has the same type as x that contains the index of each +// value of x in the output y. +func UniqueV2(scope *Scope, x tf.Output, axis tf.Output, optional ...UniqueV2Attr) (y tf.Output, idx tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UniqueV2", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// DecodePaddedRawAttr is an optional argument to DecodePaddedRaw. +type DecodePaddedRawAttr func(optionalAttr) + +// DecodePaddedRawLittleEndian sets the optional little_endian attribute to value. +// +// value: Whether the input `input_bytes` is in little-endian order. Ignored for +// `out_type` values that are stored in a single byte, like `uint8` +// If not specified, defaults to true +func DecodePaddedRawLittleEndian(value bool) DecodePaddedRawAttr { + return func(m optionalAttr) { + m["little_endian"] = value + } +} + +// Reinterpret the bytes of a string as a vector of numbers. +// +// Arguments: +// input_bytes: Tensor of string to be decoded. +// fixed_length: Length in bytes for each element of the decoded output. Must be a multiple +// of the size of the output type. +// +// +// Returns A Tensor with one more dimension than the input `bytes`. The added dimension +// will have size equal to the length of the elements of `bytes` divided by the +// number of bytes to represent `out_type`. +func DecodePaddedRaw(scope *Scope, input_bytes tf.Output, fixed_length tf.Output, out_type tf.DataType, optional ...DecodePaddedRawAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"out_type": out_type} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodePaddedRaw", + Input: []tf.Input{ + input_bytes, fixed_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingADAMParametersAttr is an optional argument to RetrieveTPUEmbeddingADAMParameters. +type RetrieveTPUEmbeddingADAMParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingADAMParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingADAMParametersTableId(value int64) RetrieveTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingADAMParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingADAMParametersTableName(value string) RetrieveTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingADAMParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingADAMParametersConfig(value string) RetrieveTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve ADAM embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the ADAM optimization algorithm. +// momenta: Parameter momenta updated by the ADAM optimization algorithm. +// velocities: Parameter velocities updated by the ADAM optimization algorithm. +func RetrieveTPUEmbeddingADAMParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingADAMParametersAttr) (parameters tf.Output, momenta tf.Output, velocities tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingADAMParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StatelessRandomBinomialAttr is an optional argument to StatelessRandomBinomial. +type StatelessRandomBinomialAttr func(optionalAttr) + +// StatelessRandomBinomialDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_INT64 +func StatelessRandomBinomialDtype(value tf.DataType) StatelessRandomBinomialAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom random numbers from a binomial distribution. +// +// Outputs random values from a binomial distribution. +// +// The outputs are a deterministic function of `shape`, `seed`, `counts`, and `probs`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// counts: The counts of the binomial distribution. Must be broadcastable with `probs`, +// and broadcastable with the rightmost dimensions of `shape`. +// probs: The probability of success for the binomial distribution. Must be broadcastable +// with `counts` and broadcastable with the rightmost dimensions of `shape`. +// +// Returns Random values with specified shape. +func StatelessRandomBinomial(scope *Scope, shape tf.Output, seed tf.Output, counts tf.Output, probs tf.Output, optional ...StatelessRandomBinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessRandomBinomial", + Input: []tf.Input{ + shape, seed, counts, probs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingAdagradParametersGradAccumDebug. +type RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingAdagradParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve Adagrad embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the Adagrad optimization algorithm. +// accumulators: Parameter accumulators updated by the Adagrad optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the Adagrad optimization algorithm. +func RetrieveTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdagradParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// StatelessMultinomialAttr is an optional argument to StatelessMultinomial. +type StatelessMultinomialAttr func(optionalAttr) + +// StatelessMultinomialOutputDtype sets the optional output_dtype attribute to value. +// If not specified, defaults to DT_INT64 +func StatelessMultinomialOutputDtype(value tf.DataType) StatelessMultinomialAttr { + return func(m optionalAttr) { + m["output_dtype"] = value + } +} + +// Draws samples from a multinomial distribution. +// +// Arguments: +// logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` +// represents the unnormalized log probabilities for all classes. +// num_samples: 0-D. Number of independent samples to draw for each row slice. +// seed: 2 seeds (shape [2]). +// +// Returns 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` +// contains the drawn class labels with range `[0, num_classes)`. +func StatelessMultinomial(scope *Scope, logits tf.Output, num_samples tf.Output, seed tf.Output, optional ...StatelessMultinomialAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessMultinomial", + Input: []tf.Input{ + logits, num_samples, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a copy of the input tensor. +func Snapshot(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Snapshot", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. +// +// This Op does not require `a_indices` be sorted in standard lexicographic order. +// +// Arguments: +// a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. +// a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. +// a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. +// b: `ndims`-D Tensor. With shape `a_shape`. +func SparseTensorDenseAdd(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseTensorDenseAdd", + Input: []tf.Input{ + a_indices, a_values, a_shape, b, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RaggedBincountAttr is an optional argument to RaggedBincount. +type RaggedBincountAttr func(optionalAttr) + +// RaggedBincountBinaryOutput sets the optional binary_output attribute to value. +// +// value: bool; Whether the kernel should count the appearance or number of occurrences. +// If not specified, defaults to false +func RaggedBincountBinaryOutput(value bool) RaggedBincountAttr { + return func(m optionalAttr) { + m["binary_output"] = value + } +} + +// Counts the number of occurrences of each value in an integer array. +// +// Outputs a vector with length `size` and the same dtype as `weights`. If +// `weights` are empty, then index `i` stores the number of times the value `i` is +// counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of +// the value in `weights` at each index where the corresponding value in `arr` is +// `i`. +// +// Values in `arr` outside of the range [0, size) are ignored. +// +// Arguments: +// splits: 1D int64 `Tensor`. +// values: 2D int `Tensor`. +// size: non-negative int scalar `Tensor`. +// weights: is an int32, int64, float32, or float64 `Tensor` with the same +// shape as `input`, or a length-0 `Tensor`, in which case it acts as all weights +// equal to 1. +// +// Returns 1D `Tensor` with length equal to `size` or 2D `Tensor` with [batch_size, `size`]. +// The counts or summed weights for each value in the range [0, size). +func RaggedBincount(scope *Scope, splits tf.Output, values tf.Output, size tf.Output, weights tf.Output, optional ...RaggedBincountAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RaggedBincount", + Input: []tf.Input{ + splits, values, size, weights, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. +type StatelessRandomNormalAttr func(optionalAttr) + +// StatelessRandomNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessRandomNormal", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the sum along sparse segments of a tensor divided by the sqrt of N. +// +// N is the size of the segment being reduced. +// +// See `tf.sparse.segment_sum` for usage examples. +// +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SparseSegmentSqrtN(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSqrtN", + Input: []tf.Input{ + data, indices, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnicodeDecodeWithOffsetsAttr is an optional argument to UnicodeDecodeWithOffsets. +type UnicodeDecodeWithOffsetsAttr func(optionalAttr) + +// UnicodeDecodeWithOffsetsErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeDecodeWithOffsetsErrors(value string) UnicodeDecodeWithOffsetsAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeDecodeWithOffsetsReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD or U+65533.) +// If not specified, defaults to 65533 +func UnicodeDecodeWithOffsetsReplacementChar(value int64) UnicodeDecodeWithOffsetsAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// UnicodeDecodeWithOffsetsReplaceControlCharacters sets the optional replace_control_characters attribute to value. +// +// value: Whether to replace the C0 control characters (00-1F) with the +// `replacement_char`. Default is false. +// If not specified, defaults to false +func UnicodeDecodeWithOffsetsReplaceControlCharacters(value bool) UnicodeDecodeWithOffsetsAttr { + return func(m optionalAttr) { + m["replace_control_characters"] = value + } +} + +// UnicodeDecodeWithOffsetsTsplits sets the optional Tsplits attribute to value. +// If not specified, defaults to DT_INT64 +func UnicodeDecodeWithOffsetsTsplits(value tf.DataType) UnicodeDecodeWithOffsetsAttr { + return func(m optionalAttr) { + m["Tsplits"] = value + } +} + +// Decodes each string in `input` into a sequence of Unicode code points. +// +// The character codepoints for all strings are returned using a single vector +// `char_values`, with strings expanded to characters in row-major order. +// Similarly, the character start byte offsets are returned using a single vector +// `char_to_byte_starts`, with strings expanded in row-major order. +// +// The `row_splits` tensor indicates where the codepoints and start offsets for +// each input string begin and end within the `char_values` and +// `char_to_byte_starts` tensors. In particular, the values for the `i`th +// string (in row-major order) are stored in the slice +// `[row_splits[i]:row_splits[i+1]]`. Thus: +// +// * `char_values[row_splits[i]+j]` is the Unicode codepoint for the `j`th +// character in the `i`th string (in row-major order). +// * `char_to_bytes_starts[row_splits[i]+j]` is the start byte offset for the `j`th +// character in the `i`th string (in row-major order). +// * `row_splits[i+1] - row_splits[i]` is the number of characters in the `i`th +// string (in row-major order). +// +// Arguments: +// input: The text to be decoded. Can have any shape. Note that the output is flattened +// to a vector of char values. +// input_encoding: Text encoding of the input strings. This is any of the encodings supported +// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. +// +// Returns: +// row_splits: A 1D int32 tensor containing the row splits. +// char_values: A 1D int32 Tensor containing the decoded codepoints. +// char_to_byte_starts: A 1D int32 Tensor containing the byte index in the input string where each +// character in `char_values` starts. +func UnicodeDecodeWithOffsets(scope *Scope, input tf.Output, input_encoding string, optional ...UnicodeDecodeWithOffsetsAttr) (row_splits tf.Output, char_values tf.Output, char_to_byte_starts tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_encoding": input_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeDecodeWithOffsets", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// This op is used as a placeholder in If branch functions. It doesn't provide a +// valid output when run, so must either be removed (e.g. replaced with a +// function input) or guaranteed not to be used (e.g. if mirroring an +// intermediate output needed for the gradient computation of the other branch). +// +// Arguments: +// dtype: The type of the output. +// shape: The purported shape of the output. This is only used for shape inference; +// the output will not necessarily have this shape. Can be a partial shape. +// +// Returns \"Fake\" output value. This should not be consumed by another op. +func FakeParam(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + opspec := tf.OpSpec{ + Type: "FakeParam", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// UnicodeTranscodeAttr is an optional argument to UnicodeTranscode. +type UnicodeTranscodeAttr func(optionalAttr) + +// UnicodeTranscodeErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeTranscodeErrors(value string) UnicodeTranscodeAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeTranscodeReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD or U+65533.) +// +// Note that for UTF-8, passing a replacement character expressible in 1 byte, such +// as ' ', will preserve string alignment to the source since invalid bytes will be +// replaced with a 1-byte replacement. For UTF-16-BE and UTF-16-LE, any 1 or 2 byte +// replacement character will preserve byte alignment to the source. +// If not specified, defaults to 65533 +func UnicodeTranscodeReplacementChar(value int64) UnicodeTranscodeAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// UnicodeTranscodeReplaceControlCharacters sets the optional replace_control_characters attribute to value. +// +// value: Whether to replace the C0 control characters (00-1F) with the +// `replacement_char`. Default is false. +// If not specified, defaults to false +func UnicodeTranscodeReplaceControlCharacters(value bool) UnicodeTranscodeAttr { + return func(m optionalAttr) { + m["replace_control_characters"] = value + } +} + +// Transcode the input text from a source encoding to a destination encoding. +// +// The input is a string tensor of any shape. The output is a string tensor of +// the same shape containing the transcoded strings. Output strings are always +// valid unicode. If the input contains invalid encoding positions, the +// `errors` attribute sets the policy for how to deal with them. If the default +// error-handling policy is used, invalid formatting will be substituted in the +// output by the `replacement_char`. If the errors policy is to `ignore`, any +// invalid encoding positions in the input are skipped and not included in the +// output. If it set to `strict` then any invalid formatting will result in an +// InvalidArgument error. +// +// This operation can be used with `output_encoding = input_encoding` to enforce +// correct formatting for inputs even if they are already in the desired encoding. +// +// If the input is prefixed by a Byte Order Mark needed to determine encoding +// (e.g. if the encoding is UTF-16 and the BOM indicates big-endian), then that +// BOM will be consumed and not emitted into the output. If the input encoding +// is marked with an explicit endianness (e.g. UTF-16-BE), then the BOM is +// interpreted as a non-breaking-space and is preserved in the output (including +// always for UTF-8). +// +// The end result is that if the input is marked as an explicit endianness the +// transcoding is faithful to all codepoints in the source. If it is not marked +// with an explicit endianness, the BOM is not considered part of the string itself +// but as metadata, and so is not preserved in the output. +// +// Examples: +// +// >>> tf.strings.unicode_transcode(["Hello", "TensorFlow", "2.x"], "UTF-8", "UTF-16-BE") +// +// >>> tf.strings.unicode_transcode(["A", "B", "C"], "US ASCII", "UTF-8").numpy() +// array([b'A', b'B', b'C'], dtype=object) +// +// Arguments: +// input: The text to be processed. Can have any shape. +// input_encoding: Text encoding of the input strings. This is any of the encodings supported +// by ICU ucnv algorithmic converters. Examples: `"UTF-16", "US ASCII", "UTF-8"`. +// output_encoding: The unicode encoding to use in the output. Must be one of +// `"UTF-8", "UTF-16-BE", "UTF-32-BE"`. Multi-byte encodings will be big-endian. +// +// Returns A string tensor containing unicode text encoded using `output_encoding`. +func UnicodeTranscode(scope *Scope, input tf.Output, input_encoding string, output_encoding string, optional ...UnicodeTranscodeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_encoding": input_encoding, "output_encoding": output_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeTranscode", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x // y element-wise. +// +// *NOTE*: `FloorDiv` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func FloorDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "FloorDiv", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An Op to permute tensors across replicated TPU instances. +// +// Each instance supplies its own input. +// +// For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing +// source_target_pairs=`[[0,1],[1,2],[2,3],[3,0]]` gets the outputs: +// `[D, A, B, C]`. +// +// Arguments: +// input: The local input to be permuted. Currently only supports float and +// bfloat16. +// source_target_pairs: A tensor with shape [num_pairs, 2]. +// +// Returns The permuted input. +func CollectivePermute(scope *Scope, input tf.Output, source_target_pairs tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CollectivePermute", + Input: []tf.Input{ + input, source_target_pairs, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizedReluXAttr is an optional argument to QuantizedReluX. +type QuantizedReluXAttr func(optionalAttr) + +// QuantizedReluXOutType sets the optional out_type attribute to value. +// If not specified, defaults to DT_QUINT8 +func QuantizedReluXOutType(value tf.DataType) QuantizedReluXAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)` +// +// Arguments: +// +// +// min_features: The float value that the lowest quantized value represents. +// max_features: The float value that the highest quantized value represents. +// +// Returns: +// activations: Has the same output shape as "features". +// min_activations: The float value that the lowest quantized value represents. +// max_activations: The float value that the highest quantized value represents. +func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_features tf.Output, max_features tf.Output, optional ...QuantizedReluXAttr) (activations tf.Output, min_activations tf.Output, max_activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedReluX", + Input: []tf.Input{ + features, max_value, min_features, max_features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Forwards `data` to the output port determined by `pred`. +// +// If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise, +// the data goes to `output_false`. +// +// See also `RefSwitch` and `Merge`. +// +// Arguments: +// data: The tensor to be forwarded to the appropriate output. +// pred: A scalar that specifies which output port will receive data. +// +// Returns: +// output_false: If `pred` is false, data will be forwarded to this output. +// output_true: If `pred` is true, data will be forwarded to this output. +func Switch(scope *Scope, data tf.Output, pred tf.Output) (output_false tf.Output, output_true tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Switch", + Input: []tf.Input{ + data, pred, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingFTRLParametersGradAccumDebug. +type RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingFTRLParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve FTRL embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the FTRL optimization algorithm. +// accumulators: Parameter accumulators updated by the FTRL optimization algorithm. +// linears: Parameter linears updated by the FTRL optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the FTRL optimization algorithm. +func RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// UnicodeEncodeAttr is an optional argument to UnicodeEncode. +type UnicodeEncodeAttr func(optionalAttr) + +// UnicodeEncodeErrors sets the optional errors attribute to value. +// +// value: Error handling policy when there is invalid formatting found in the input. +// The value of 'strict' will cause the operation to produce a InvalidArgument +// error on any invalid input formatting. A value of 'replace' (the default) will +// cause the operation to replace any invalid formatting in the input with the +// `replacement_char` codepoint. A value of 'ignore' will cause the operation to +// skip any invalid formatting in the input and produce no corresponding output +// character. +// If not specified, defaults to "replace" +func UnicodeEncodeErrors(value string) UnicodeEncodeAttr { + return func(m optionalAttr) { + m["errors"] = value + } +} + +// UnicodeEncodeReplacementChar sets the optional replacement_char attribute to value. +// +// value: The replacement character codepoint to be used in place of any invalid +// formatting in the input when `errors='replace'`. Any valid unicode codepoint may +// be used. The default value is the default unicode replacement character is +// 0xFFFD (U+65533). +// If not specified, defaults to 65533 +func UnicodeEncodeReplacementChar(value int64) UnicodeEncodeAttr { + return func(m optionalAttr) { + m["replacement_char"] = value + } +} + +// Encode a tensor of ints into unicode strings. +// +// Returns a vector of strings, where `output[i]` is constructed by encoding the +// Unicode codepoints in `input_values[input_splits[i]:input_splits[i+1]]` +// using `output_encoding`. +// +// --- +// +// Example: +// +// ``` +// input_values = [72, 101, 108, 108, 111, 87, 111, 114, 108, 100] +// input_splits = [0, 5, 10] +// output_encoding = 'UTF-8' +// +// output = ['Hello', 'World'] +// ``` +// +// Arguments: +// input_values: A 1D tensor containing the unicode codepoints that should be encoded. +// input_splits: A 1D tensor specifying how the unicode codepoints should be split into strings. +// In particular, `output[i]` is constructed by encoding the codepoints in the +// slice `input_values[input_splits[i]:input_splits[i+1]]`. +// output_encoding: Unicode encoding of the output strings. Valid encodings are: `"UTF-8", +// "UTF-16-BE", and "UTF-32-BE"`. +// +// Returns The 1-D Tensor of strings encoded from the provided unicode codepoints. +func UnicodeEncode(scope *Scope, input_values tf.Output, input_splits tf.Output, output_encoding string, optional ...UnicodeEncodeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_encoding": output_encoding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnicodeEncode", + Input: []tf.Input{ + input_values, input_splits, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PrelinearizeTupleAttr is an optional argument to PrelinearizeTuple. +type PrelinearizeTupleAttr func(optionalAttr) + +// PrelinearizeTupleLayouts sets the optional layouts attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence for all the +// tuple shapes in the order the shapes appear in the "shapes" input. The layout +// elements for a sub-shape can be set to -1 in which case the corresponding layout +// will be computed by the infeed operation. +// If not specified, defaults to <> +func PrelinearizeTupleLayouts(value []int64) PrelinearizeTupleAttr { + return func(m optionalAttr) { + m["layouts"] = value + } +} + +// An op which linearizes multiple Tensor values to an opaque variant tensor. +// +// Arguments: +// inputs: A list of tensors that will be provided using the infeed mechanism. +// shapes: The shapes of each tensor in `inputs`. +func PrelinearizeTuple(scope *Scope, inputs []tf.Output, shapes []tf.Shape, optional ...PrelinearizeTupleAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PrelinearizeTuple", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the LSTM cell backward propagation for the entire time sequence. +// +// This implementation is to be used in conjunction of LSTMBlock. +// +// Arguments: +// seq_len_max: Maximum time length actually used by this input. Outputs are padded +// with zeros beyond this length. +// x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). +// cs_prev: Value of the initial cell state. +// h_prev: Initial output of cell (to be used for peephole). +// w: The weight matrix. +// wci: The weight matrix for input gate peephole connection. +// wcf: The weight matrix for forget gate peephole connection. +// wco: The weight matrix for output gate peephole connection. +// b: The bias vector. +// i: The input gate over the whole time sequence. +// cs: The cell state before the tanh over the whole time sequence. +// f: The forget gate over the whole time sequence. +// o: The output gate over the whole time sequence. +// ci: The cell input over the whole time sequence. +// co: The cell after the tanh over the whole time sequence. +// h: The output h vector over the whole time sequence. +// cs_grad: The current gradient of cs. +// h_grad: The gradient of h vector. +// use_peephole: Whether to use peephole weights. +// +// Returns: +// x_grad: The gradient of x to be back-propped. +// cs_prev_grad: The gradient of cs_prev to be back-propped. +// h_prev_grad: The gradient of h_prev to be back-propped. +// w_grad: The gradient for w to be back-propped. +// wci_grad: The gradient for wci to be back-propped. +// wcf_grad: The gradient for wcf to be back-propped. +// wco_grad: The gradient for wco to be back-propped. +// b_grad: The gradient for w to be back-propped. +func BlockLSTMGrad(scope *Scope, seq_len_max tf.Output, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output, cs_grad tf.Output, h_grad tf.Output, use_peephole bool) (x_grad tf.Output, cs_prev_grad tf.Output, h_prev_grad tf.Output, w_grad tf.Output, wci_grad tf.Output, wcf_grad tf.Output, wco_grad tf.Output, b_grad tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"use_peephole": use_peephole} + opspec := tf.OpSpec{ + Type: "BlockLSTMGrad", + Input: []tf.Input{ + seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6), op.Output(7) +} + +// OutfeedDequeueTupleAttr is an optional argument to OutfeedDequeueTuple. +type OutfeedDequeueTupleAttr func(optionalAttr) + +// OutfeedDequeueTupleDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func OutfeedDequeueTupleDeviceOrdinal(value int64) OutfeedDequeueTupleAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// Retrieve multiple values from the computation outfeed. +// +// This operation will block indefinitely until data is available. Output `i` +// corresponds to XLA tuple element `i`. +// +// Arguments: +// dtypes: The element types of each element in `outputs`. +// shapes: The shapes of each tensor in `outputs`. +// +// Returns A list of tensors that will be read from the outfeed. +func OutfeedDequeueTuple(scope *Scope, dtypes []tf.DataType, shapes []tf.Shape, optional ...OutfeedDequeueTupleAttr) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes, "shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OutfeedDequeueTuple", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("OutfeedDequeueTuple", err) + return + } + return outputs +} + +// DecodeCompressedAttr is an optional argument to DecodeCompressed. +type DecodeCompressedAttr func(optionalAttr) + +// DecodeCompressedCompressionType sets the optional compression_type attribute to value. +// +// value: A scalar containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// If not specified, defaults to "" +func DecodeCompressedCompressionType(value string) DecodeCompressedAttr { + return func(m optionalAttr) { + m["compression_type"] = value + } +} + +// Decompress strings. +// +// This op decompresses each element of the `bytes` input `Tensor`, which +// is assumed to be compressed using the given `compression_type`. +// +// The `output` is a string `Tensor` of the same shape as `bytes`, +// each element containing the decompressed data from the corresponding +// element in `bytes`. +// +// Arguments: +// bytes: A Tensor of string which is compressed. +// +// Returns A Tensor with the same shape as input `bytes`, uncompressed +// from bytes. +func DecodeCompressed(scope *Scope, bytes tf.Output, optional ...DecodeCompressedAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeCompressed", + Input: []tf.Input{ + bytes, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process. The hash function is a keyed hash function, where attribute `key` +// defines the key of the hash function. `key` is an array of 2 elements. +// +// A strong hash is important when inputs may be malicious, e.g. URLs with +// additional components. Adversaries could try to make their inputs hash to the +// same bucket for a denial-of-service attack or to skew the results. A strong +// hash can be used to make it difficult to find inputs with a skewed hash value +// distribution over buckets. This requires that the hash function is +// seeded by a high-entropy (random) "key" unknown to the adversary. +// +// The additional robustness comes at a cost of roughly 4x higher compute +// time than `tf.string_to_hash_bucket_fast`. +// +// Examples: +// +// >>> tf.strings.to_hash_bucket_strong(["Hello", "TF"], 3, [1, 2]).numpy() +// array([2, 0]) +// +// Arguments: +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// key: The key used to seed the hash function, passed as a list of two uint64 +// elements. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketStrong(scope *Scope, input tf.Output, num_buckets int64, key []int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets, "key": key} + opspec := tf.OpSpec{ + Type: "StringToHashBucketStrong", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Deserialize bucket boundaries and ready flag into current QuantileAccumulator. +// +// An op that deserializes bucket boundaries and are boundaries ready flag into current QuantileAccumulator. +// +// Arguments: +// quantile_stream_resource_handle: resource handle referring to a QuantileStreamResource. +// bucket_boundaries: float; List of Rank 1 Tensors each containing the bucket boundaries for a feature. +// +// Returns the created operation. +func BoostedTreesQuantileStreamResourceDeserialize(scope *Scope, quantile_stream_resource_handle tf.Output, bucket_boundaries []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesQuantileStreamResourceDeserialize", + Input: []tf.Input{ + quantile_stream_resource_handle, tf.OutputList(bucket_boundaries), + }, + } + return scope.AddOperation(opspec) +} + +// ResourceApplyAdadeltaAttr is an optional argument to ResourceApplyAdadelta. +type ResourceApplyAdadeltaAttr func(optionalAttr) + +// ResourceApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var, accum and update_accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyAdadeltaUseLocking(value bool) ResourceApplyAdadeltaAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the adadelta scheme. +// +// accum = rho() * accum + (1 - rho()) * grad.square(); +// update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; +// update_accum = rho() * update_accum + (1 - rho()) * update.square(); +// var -= update; +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// accum_update: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdadeltaAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdadelta", + Input: []tf.Input{ + var_, accum, accum_update, lr, rho, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Converts each string in the input Tensor to its hash mod by a number of buckets. +// +// The hash function is deterministic on the content of the string within the +// process and will never change. However, it is not suitable for cryptography. +// This function may be used when CPU time is scarce and inputs are trusted or +// unimportant. There is a risk of adversaries constructing inputs that all hash +// to the same bucket. To prevent this problem, use a strong hash function with +// `tf.string_to_hash_bucket_strong`. +// +// Examples: +// +// >>> tf.strings.to_hash_bucket_fast(["Hello", "TensorFlow", "2.x"], 3).numpy() +// array([0, 2, 2]) +// +// Arguments: +// input: The strings to assign a hash bucket. +// num_buckets: The number of buckets. +// +// Returns A Tensor of the same shape as the input `string_tensor`. +func StringToHashBucketFast(scope *Scope, input tf.Output, num_buckets int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_buckets": num_buckets} + opspec := tf.OpSpec{ + Type: "StringToHashBucketFast", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringJoinAttr is an optional argument to StringJoin. +type StringJoinAttr func(optionalAttr) + +// StringJoinSeparator sets the optional separator attribute to value. +// +// value: string, an optional join separator. +// If not specified, defaults to "" +func StringJoinSeparator(value string) StringJoinAttr { + return func(m optionalAttr) { + m["separator"] = value + } +} + +// Joins the strings in the given list of string tensors into one tensor; +// +// with the given separator (default is an empty separator). +// +// Examples: +// +// >>> s = ["hello", "world", "tensorflow"] +// >>> tf.strings.join(s, " ") +// +// +// Arguments: +// inputs: A list of string tensors. The tensors must all have the same shape, +// or be scalars. Scalars may be mixed in; these will be broadcast to the shape +// of non-scalar inputs. +func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StringJoin", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Replaces the contents of the table with the specified keys and values. +// +// The tensor `keys` must be of the same type as the keys of the table. +// The tensor `values` must be of the type of the table values. +// +// Arguments: +// table_handle: Handle to the table. +// keys: Any shape. Keys to look up. +// values: Values to associate with keys. +// +// Returns the created operation. +func LookupTableImportV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableImportV2", + Input: []tf.Input{ + table_handle, keys, values, + }, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingMomentumParametersAttr is an optional argument to LoadTPUEmbeddingMomentumParameters. +type LoadTPUEmbeddingMomentumParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingMomentumParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingMomentumParametersTableId(value int64) LoadTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingMomentumParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMomentumParametersTableName(value string) LoadTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingMomentumParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMomentumParametersConfig(value string) LoadTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load Momentum embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Momentum optimization algorithm. +// momenta: Value of momenta used in the Momentum optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingMomentumParameters(scope *Scope, parameters tf.Output, momenta tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMomentumParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingMomentumParameters", + Input: []tf.Input{ + parameters, momenta, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SkipgramAttr is an optional argument to Skipgram. +type SkipgramAttr func(optionalAttr) + +// SkipgramWindowSize sets the optional window_size attribute to value. +// +// value: The number of words to predict to the left and right of the target. +// If not specified, defaults to 5 +func SkipgramWindowSize(value int64) SkipgramAttr { + return func(m optionalAttr) { + m["window_size"] = value + } +} + +// SkipgramMinCount sets the optional min_count attribute to value. +// +// value: The minimum number of word occurrences for it to be included in the +// vocabulary. +// If not specified, defaults to 5 +func SkipgramMinCount(value int64) SkipgramAttr { + return func(m optionalAttr) { + m["min_count"] = value + } +} + +// SkipgramSubsample sets the optional subsample attribute to value. +// +// value: Threshold for word occurrence. Words that appear with higher +// frequency will be randomly down-sampled. Set to 0 to disable. +// If not specified, defaults to 0.001 +func SkipgramSubsample(value float32) SkipgramAttr { + return func(m optionalAttr) { + m["subsample"] = value + } +} + +// Parses a text file and creates a batch of examples. +// +// DEPRECATED at GraphDef version 19: Moving word2vec into tensorflow_models/tutorials and deprecating its ops here as a result +// +// Arguments: +// filename: The corpus's text file name. +// batch_size: The size of produced batch. +// +// Returns: +// vocab_word: A vector of words in the corpus. +// vocab_freq: Frequencies of words. Sorted in the non-ascending order. +// words_per_epoch: Number of words per epoch in the data file. +// current_epoch: The current epoch number. +// total_words_processed: The total number of words processed so far. +// examples: A vector of word ids. +// labels: A vector of word ids. +func Skipgram(scope *Scope, filename string, batch_size int64, optional ...SkipgramAttr) (vocab_word tf.Output, vocab_freq tf.Output, words_per_epoch tf.Output, current_epoch tf.Output, total_words_processed tf.Output, examples tf.Output, labels tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"filename": filename, "batch_size": batch_size} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Skipgram", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// StaticRegexReplaceAttr is an optional argument to StaticRegexReplace. +type StaticRegexReplaceAttr func(optionalAttr) + +// StaticRegexReplaceReplaceGlobal sets the optional replace_global attribute to value. +// +// value: If True, the replacement is global, otherwise the replacement +// is done only on the first match. +// If not specified, defaults to true +func StaticRegexReplaceReplaceGlobal(value bool) StaticRegexReplaceAttr { + return func(m optionalAttr) { + m["replace_global"] = value + } +} + +// Replaces the match of pattern in input with rewrite. +// +// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: The text to be processed. +// pattern: The regular expression to match the input. +// rewrite: The rewrite to be applied to the matched expression. +// +// Returns The text after applying pattern and rewrite. +func StaticRegexReplace(scope *Scope, input tf.Output, pattern string, rewrite string, optional ...StaticRegexReplaceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pattern": pattern, "rewrite": rewrite} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StaticRegexReplace", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns which elements of x are finite. +// +// @compatibility(numpy) +// Equivalent to np.isfinite +// @end_compatibility +// +// Example: +// +// ```python +// x = tf.constant([5.0, 4.8, 6.8, np.inf, np.nan]) +// tf.math.is_finite(x) ==> [True, True, True, False, False] +// ``` +func IsFinite(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IsFinite", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a tensor of zeros with the same shape and type as x. +// +// Arguments: +// x: a tensor of type T. +// +// Returns a tensor of the same shape and type as x but filled with zeros. +func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ZerosLike", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingAdadeltaParametersAttr is an optional argument to RetrieveTPUEmbeddingAdadeltaParameters. +type RetrieveTPUEmbeddingAdadeltaParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdadeltaParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingAdadeltaParametersTableId(value int64) RetrieveTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdadeltaParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdadeltaParametersTableName(value string) RetrieveTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingAdadeltaParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdadeltaParametersConfig(value string) RetrieveTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve Adadelta embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the Adadelta optimization algorithm. +// accumulators: Parameter accumulators updated by the Adadelta optimization algorithm. +// updates: Parameter updates updated by the Adadelta optimization algorithm. +func RetrieveTPUEmbeddingAdadeltaParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdadeltaParametersAttr) (parameters tf.Output, accumulators tf.Output, updates tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdadeltaParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// RegexReplaceAttr is an optional argument to RegexReplace. +type RegexReplaceAttr func(optionalAttr) + +// RegexReplaceReplaceGlobal sets the optional replace_global attribute to value. +// +// value: If True, the replacement is global (that is, all matches of the `pattern` regular +// expression in each input string are rewritten), otherwise the `rewrite` +// substitution is only made for the first `pattern` match. +// If not specified, defaults to true +func RegexReplaceReplaceGlobal(value bool) RegexReplaceAttr { + return func(m optionalAttr) { + m["replace_global"] = value + } +} + +// Replaces matches of the `pattern` regular expression in `input` with the +// replacement string provided in `rewrite`. +// +// It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: The text to be processed. +// pattern: The regular expression to be matched in the `input` strings. +// rewrite: The rewrite string to be substituted for the `pattern` expression where it is +// matched in the `input` strings. +// +// Returns The text after applying pattern match and rewrite substitution. +func RegexReplace(scope *Scope, input tf.Output, pattern tf.Output, rewrite tf.Output, optional ...RegexReplaceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RegexReplace", + Input: []tf.Input{ + input, pattern, rewrite, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ExperimentalRebatchDatasetAttr is an optional argument to ExperimentalRebatchDataset. +type ExperimentalRebatchDatasetAttr func(optionalAttr) + +// ExperimentalRebatchDatasetUseFallback sets the optional use_fallback attribute to value. +// If not specified, defaults to true +func ExperimentalRebatchDatasetUseFallback(value bool) ExperimentalRebatchDatasetAttr { + return func(m optionalAttr) { + m["use_fallback"] = value + } +} + +// Creates a dataset that changes the batch size. +// +// Creates a dataset that changes the batch size of the dataset to current batch +// size // num_replicas. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// num_replicas: A scalar representing the number of replicas to distribute this batch across. As +// a result of this transformation the current batch size would end up being +// divided by this parameter. +// +// +func ExperimentalRebatchDataset(scope *Scope, input_dataset tf.Output, num_replicas tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...ExperimentalRebatchDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ExperimentalRebatchDataset", + Input: []tf.Input{ + input_dataset, num_replicas, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates tensors along one dimension. +// +// Arguments: +// concat_dim: 0-D. The dimension along which to concatenate. Must be in the +// range [0, rank(values)). +// values: The `N` Tensors to concatenate. Their ranks and types must match, +// and their sizes must match in all dimensions except `concat_dim`. +// +// Returns A `Tensor` with the concatenation of values stacked along the +// `concat_dim` dimension. This tensor's shape matches that of `values` except +// in `concat_dim` where it has the sum of the sizes. +func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Concat", + Input: []tf.Input{ + concat_dim, tf.OutputList(values), + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyPowerSignAttr is an optional argument to ResourceApplyPowerSign. +type ResourceApplyPowerSignAttr func(optionalAttr) + +// ResourceApplyPowerSignUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and m tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyPowerSignUseLocking(value bool) ResourceApplyPowerSignAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the AddSign update. +// +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g +// variable <- variable - lr_t * update +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// logbase: Must be a scalar. +// sign_decay: Must be a scalar. +// beta: Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, logbase tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyPowerSignAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyPowerSign", + Input: []tf.Input{ + var_, m, lr, logbase, sign_decay, beta, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Converts a tensor to a scalar predicate. +// +// Converts a tensor to a scalar predicate with the following rules: +// +// - For 0D tensors, truthiness is determined by comparing against a "zero" +// value. For numerical types it is the obvious zero. For strings it is the +// empty string. +// +// - For >0D tensors, truthiness is determined by looking at the number of +// elements. If has zero elements, then the result is false. Otherwise the +// result is true. +// +// This matches the behavior of If and While for determining if a tensor counts +// as true/false for a branch condition. +func ToBool(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ToBool", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// GenerateBoundingBoxProposalsAttr is an optional argument to GenerateBoundingBoxProposals. +type GenerateBoundingBoxProposalsAttr func(optionalAttr) + +// GenerateBoundingBoxProposalsPostNmsTopn sets the optional post_nms_topn attribute to value. +// +// value: An integer. Maximum number of rois in the output. +// If not specified, defaults to 300 +func GenerateBoundingBoxProposalsPostNmsTopn(value int64) GenerateBoundingBoxProposalsAttr { + return func(m optionalAttr) { + m["post_nms_topn"] = value + } +} + +// This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497 +// +// The op selects top `pre_nms_topn` scoring boxes, decodes them with respect to anchors, +// applies non-maximal suppression on overlapping boxes with higher than +// `nms_threshold` intersection-over-union (iou) value, discarding boxes where shorter +// side is less than `min_size`. +// Inputs: +// `scores`: A 4D tensor of shape [Batch, Height, Width, Num Anchors] containing the scores per anchor at given position +// `bbox_deltas`: is a tensor of shape [Batch, Height, Width, 4 x Num Anchors] boxes encoded to each anchor +// `anchors`: A 1D tensor of shape [4 x Num Anchors], representing the anchors. +// Outputs: +// `rois`: output RoIs, a 3D tensor of shape [Batch, post_nms_topn, 4], padded by 0 if less than post_nms_topn candidates found. +// `roi_probabilities`: probability scores of each roi in 'rois', a 2D tensor of shape [Batch,post_nms_topn], padded with 0 if needed, sorted by scores. +// +// Arguments: +// scores: A 4-D float tensor of shape `[num_images, height, width, num_achors]` containing scores of the boxes for given anchors, can be unsorted. +// bbox_deltas: A 4-D float tensor of shape `[num_images, height, width, 4 x num_anchors]`. encoding boxes with respec to each anchor. +// Coordinates are given in the form [dy, dx, dh, dw]. +// image_info: A 2-D float tensor of shape `[num_images, 5]` containing image information Height, Width, Scale. +// anchors: A 2-D float tensor of shape `[num_anchors, 4]` describing the anchor boxes. Boxes are formatted in the form [y1, x1, y2, x2]. +// nms_threshold: A scalar float tensor for non-maximal-suppression threshold. +// pre_nms_topn: A scalar int tensor for the number of top scoring boxes to be used as input. +// min_size: A scalar float tensor. Any box that has a smaller size than min_size will be discarded. +// +// Returns: +// rois: A 3-D float tensor of shape `[num_images,post_nms_topn,4]` representing the selected +// region of interest boxes. Sorted in descending order in scores. +// roi_probabilities: A 2-D float tensor of shape `[num_images, post_nms_topn]` representing the score of the +// region of interest box in `rois` tensor at the same index. +func GenerateBoundingBoxProposals(scope *Scope, scores tf.Output, bbox_deltas tf.Output, image_info tf.Output, anchors tf.Output, nms_threshold tf.Output, pre_nms_topn tf.Output, min_size tf.Output, optional ...GenerateBoundingBoxProposalsAttr) (rois tf.Output, roi_probabilities tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "GenerateBoundingBoxProposals", + Input: []tf.Input{ + scores, bbox_deltas, image_info, anchors, nms_threshold, pre_nms_topn, min_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// InitializeTableFromTextFileV2Attr is an optional argument to InitializeTableFromTextFileV2. +type InitializeTableFromTextFileV2Attr func(optionalAttr) + +// InitializeTableFromTextFileV2VocabSize sets the optional vocab_size attribute to value. +// +// value: Number of elements of the file, use -1 if unknown. +// If not specified, defaults to -1 +// +// REQUIRES: value >= -1 +func InitializeTableFromTextFileV2VocabSize(value int64) InitializeTableFromTextFileV2Attr { + return func(m optionalAttr) { + m["vocab_size"] = value + } +} + +// InitializeTableFromTextFileV2Delimiter sets the optional delimiter attribute to value. +// +// value: Delimiter to separate fields in a line. +// If not specified, defaults to "\t" +func InitializeTableFromTextFileV2Delimiter(value string) InitializeTableFromTextFileV2Attr { + return func(m optionalAttr) { + m["delimiter"] = value + } +} + +// Initializes a table from a text file. +// +// It inserts one key-value pair into the table for each line of the file. +// The key and value is extracted from the whole line content, elements from the +// split line based on `delimiter` or the line number (starting from zero). +// Where to extract the key and value from a line is specified by `key_index` and +// `value_index`. +// +// - A value of -1 means use the line number(starting from zero), expects `int64`. +// - A value of -2 means use the whole line content, expects `string`. +// - A value >= 0 means use the index (starting at zero) of the split line based +// on `delimiter`. +// +// Arguments: +// table_handle: Handle to a table which will be initialized. +// filename: Filename of a vocabulary text file. +// key_index: Column index in a line to get the table `key` values from. +// value_index: Column index that represents information of a line to get the table +// `value` values from. +// +// Returns the created operation. +func InitializeTableFromTextFileV2(scope *Scope, table_handle tf.Output, filename tf.Output, key_index int64, value_index int64, optional ...InitializeTableFromTextFileV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"key_index": key_index, "value_index": value_index} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InitializeTableFromTextFileV2", + Input: []tf.Input{ + table_handle, filename, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns an element-wise indication of the sign of a number. +// +// `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`. +// +// For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`. +// +// Example usage: +// >>> tf.math.sign([0., 2., -3.]) +// +func Sign(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sign", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyAddSignAttr is an optional argument to ResourceApplyAddSign. +type ResourceApplyAddSignAttr func(optionalAttr) + +// ResourceApplyAddSignUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and m tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAddSignUseLocking(value bool) ResourceApplyAddSignAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the AddSign update. +// +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// update <- (alpha + sign_decay * sign(g) *sign(m)) * g +// variable <- variable - lr_t * update +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// alpha: Must be a scalar. +// sign_decay: Must be a scalar. +// beta: Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAddSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Output, alpha tf.Output, sign_decay tf.Output, beta tf.Output, grad tf.Output, optional ...ResourceApplyAddSignAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAddSign", + Input: []tf.Input{ + var_, m, lr, alpha, sign_decay, beta, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns the number of work units this Reader has finished processing. +// +// Arguments: +// reader_handle: Handle to a Reader. +func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units_completed tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderNumWorkUnitsCompletedV2", + Input: []tf.Input{ + reader_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// FractionalMaxPoolAttr is an optional argument to FractionalMaxPool. +type FractionalMaxPoolAttr func(optionalAttr) + +// FractionalMaxPoolPseudoRandom sets the optional pseudo_random attribute to value. +// +// value: When set to True, generates the pooling sequence in a +// pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin +// Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for +// difference between pseudorandom and random. +// If not specified, defaults to false +func FractionalMaxPoolPseudoRandom(value bool) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["pseudo_random"] = value + } +} + +// FractionalMaxPoolOverlapping sets the optional overlapping attribute to value. +// +// value: When set to True, it means when pooling, the values at the boundary +// of adjacent pooling cells are used by both cells. For example: +// +// `index 0 1 2 3 4` +// +// `value 20 5 16 3 7` +// +// If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. +// The result would be [20, 16] for fractional max pooling. +// If not specified, defaults to false +func FractionalMaxPoolOverlapping(value bool) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["overlapping"] = value + } +} + +// FractionalMaxPoolDeterministic sets the optional deterministic attribute to value. +// +// value: When set to True, a fixed pooling region will be used when +// iterating over a FractionalMaxPool node in the computation graph. Mainly used +// in unit test to make FractionalMaxPool deterministic. +// If not specified, defaults to false +func FractionalMaxPoolDeterministic(value bool) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["deterministic"] = value + } +} + +// FractionalMaxPoolSeed sets the optional seed attribute to value. +// +// value: If either seed or seed2 are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func FractionalMaxPoolSeed(value int64) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// FractionalMaxPoolSeed2 sets the optional seed2 attribute to value. +// +// value: An second seed to avoid seed collision. +// If not specified, defaults to 0 +func FractionalMaxPoolSeed2(value int64) FractionalMaxPoolAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Performs fractional max pooling on the input. +// +// Fractional max pooling is slightly different than regular max pooling. In +// regular max pooling, you downsize an input set by taking the maximum value of +// smaller N x N subsections of the set (often 2x2), and try to reduce the set by +// a factor of N, where N is an integer. Fractional max pooling, as you might +// expect from the word "fractional", means that the overall reduction ratio N +// does not have to be an integer. +// +// The sizes of the pooling regions are generated randomly but are fairly uniform. +// For example, let's look at the height dimension, and the constraints on the +// list of rows that will be pool boundaries. +// +// First we define the following: +// +// 1. input_row_length : the number of rows from the input set +// 2. output_row_length : which will be smaller than the input +// 3. alpha = input_row_length / output_row_length : our reduction ratio +// 4. K = floor(alpha) +// 5. row_pooling_sequence : this is the result list of pool boundary rows +// +// Then, row_pooling_sequence should satisfy: +// +// 1. a[0] = 0 : the first value of the sequence is 0 +// 2. a[end] = input_row_length : the last value of the sequence is the size +// 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size +// 4. length(row_pooling_sequence) = output_row_length+1 +// +// For more details on fractional max pooling, see this paper: +// [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) +// +// Arguments: +// value: 4-D with shape `[batch, height, width, channels]`. +// pooling_ratio: Pooling ratio for each dimension of `value`, currently only +// supports row and col dimension and should be >= 1.0. For example, a valid +// pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements +// must be 1.0 because we don't allow pooling on batch and channels +// dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions +// respectively. +// +// Returns: +// output: output tensor after fractional max pooling. +// row_pooling_sequence: row pooling sequence, needed to calculate gradient. +// col_pooling_sequence: column pooling sequence, needed to calculate gradient. +func FractionalMaxPool(scope *Scope, value tf.Output, pooling_ratio []float32, optional ...FractionalMaxPoolAttr) (output tf.Output, row_pooling_sequence tf.Output, col_pooling_sequence tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"pooling_ratio": pooling_ratio} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FractionalMaxPool", + Input: []tf.Input{ + value, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Computes the reciprocal of x element-wise. +// +// I.e., \\(y = 1 / x\\). +func Reciprocal(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Reciprocal", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdagradParametersGradAccumDebug. +type LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingAdagradParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdagradParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load Adagrad embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the Adagrad optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdagradParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Strip leading and trailing whitespaces from the Tensor. +// +// Arguments: +// input: A string `Tensor` of any shape. +// +// Returns A string `Tensor` of the same shape as the input. +// +// Examples: +// +// >>> tf.strings.strip(["\nTensorFlow", " The python library "]).numpy() +// array([b'TensorFlow', b'The python library'], dtype=object) +func StringStrip(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StringStrip", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the minimum along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such +// that `segment_ids[j] == i`. +// +// If the min is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_min(c, tf.constant([0, 0, 1])) +// # ==> [[1, 2, 2, 1], +// # [5, 6, 7, 8]] +// ``` +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMin", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Execute a sub graph on a remote processor. +// +// The graph specifications(such as graph itself, input tensors and output names) +// are stored as a serialized protocol buffer of RemoteFusedGraphExecuteInfo +// as serialized_remote_fused_graph_execute_info. +// The specifications will be passed to a dedicated registered +// remote fused graph executor. The executor will send the graph specifications +// to a remote processor and execute that graph. The execution results +// will be passed to consumer nodes as outputs of this node. +// +// Arguments: +// inputs: Arbitrary number of tensors with arbitrary data types +// +// serialized_remote_fused_graph_execute_info: Serialized protocol buffer +// of RemoteFusedGraphExecuteInfo which contains graph specifications. +// +// Returns Arbitrary number of tensors with arbitrary data types +func RemoteFusedGraphExecute(scope *Scope, inputs []tf.Output, Toutputs []tf.DataType, serialized_remote_fused_graph_execute_info string) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"Toutputs": Toutputs, "serialized_remote_fused_graph_execute_info": serialized_remote_fused_graph_execute_info} + opspec := tf.OpSpec{ + Type: "RemoteFusedGraphExecute", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("RemoteFusedGraphExecute", err) + return + } + return outputs +} + +// LoadTPUEmbeddingMDLAdagradLightParametersAttr is an optional argument to LoadTPUEmbeddingMDLAdagradLightParameters. +type LoadTPUEmbeddingMDLAdagradLightParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingMDLAdagradLightParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingMDLAdagradLightParametersTableId(value int64) LoadTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingMDLAdagradLightParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMDLAdagradLightParametersTableName(value string) LoadTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingMDLAdagradLightParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMDLAdagradLightParametersConfig(value string) LoadTPUEmbeddingMDLAdagradLightParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load MDL Adagrad Light embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the MDL Adagrad Light optimization algorithm. +// accumulators: Value of accumulators used in the MDL Adagrad Light optimization algorithm. +// weights: Value of weights used in the MDL Adagrad Light optimization algorithm. +// benefits: Value of benefits used in the MDL Adagrad Light optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingMDLAdagradLightParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, weights tf.Output, benefits tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMDLAdagradLightParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingMDLAdagradLightParameters", + Input: []tf.Input{ + parameters, accumulators, weights, benefits, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MapPeekAttr is an optional argument to MapPeek. +type MapPeekAttr func(optionalAttr) + +// MapPeekCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapPeekCapacity(value int64) MapPeekAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapPeekMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapPeekMemoryLimit(value int64) MapPeekAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapPeekContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapPeekContainer(value string) MapPeekAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapPeekSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapPeekSharedName(value string) MapPeekAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op peeks at the values at the specified key. If the +// +// underlying container does not contain this key +// this op will block until it does. +func MapPeek(scope *Scope, key tf.Output, indices tf.Output, dtypes []tf.DataType, optional ...MapPeekAttr) (values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapPeek", + Input: []tf.Input{ + key, indices, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if values, idx, err = makeOutputList(op, idx, "values"); err != nil { + scope.UpdateErr("MapPeek", err) + return + } + return values +} + +// RetrieveTPUEmbeddingCenteredRMSPropParametersAttr is an optional argument to RetrieveTPUEmbeddingCenteredRMSPropParameters. +type RetrieveTPUEmbeddingCenteredRMSPropParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingCenteredRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingCenteredRMSPropParametersTableId(value int64) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingCenteredRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingCenteredRMSPropParametersTableName(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingCenteredRMSPropParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingCenteredRMSPropParametersConfig(value string) RetrieveTPUEmbeddingCenteredRMSPropParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve centered RMSProp embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the centered RMSProp optimization algorithm. +// ms: Parameter ms updated by the centered RMSProp optimization algorithm. +// mom: Parameter mom updated by the centered RMSProp optimization algorithm. +// mg: Parameter mg updated by the centered RMSProp optimization algorithm. +func RetrieveTPUEmbeddingCenteredRMSPropParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingCenteredRMSPropParametersAttr) (parameters tf.Output, ms tf.Output, mom tf.Output, mg tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingCenteredRMSPropParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Transforms a vector of brain.Example protos (as strings) into typed tensors. +// +// Arguments: +// serialized: A vector containing a batch of binary serialized Example protos. +// names: A vector containing the names of the serialized protos. +// May contain, for example, table key (descriptive) names for the +// corresponding serialized protos. These are purely useful for debugging +// purposes, and the presence of values here has no effect on the output. +// May also be an empty vector if no names are available. +// If non-empty, this vector must be the same length as "serialized". +// sparse_keys: A list of Nsparse string Tensors (scalars). +// The keys expected in the Examples' features associated with sparse values. +// dense_keys: A list of Ndense string Tensors (scalars). +// The keys expected in the Examples' features associated with dense values. +// dense_defaults: A list of Ndense Tensors (some may be empty). +// dense_defaults[j] provides default values +// when the example's feature_map lacks dense_key[j]. If an empty Tensor is +// provided for dense_defaults[j], then the Feature dense_keys[j] is required. +// The input type is inferred from dense_defaults[j], even when it's empty. +// If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, +// then the shape of dense_defaults[j] must match that of dense_shapes[j]. +// If dense_shapes[j] has an undefined major dimension (variable strides dense +// feature), dense_defaults[j] must contain a single element: +// the padding element. +// sparse_types: A list of Nsparse types; the data types of data in each Feature +// given in sparse_keys. +// Currently the ParseExample supports DT_FLOAT (FloatList), +// DT_INT64 (Int64List), and DT_STRING (BytesList). +// dense_shapes: A list of Ndense shapes; the shapes of data in each Feature +// given in dense_keys. +// The number of elements in the Feature corresponding to dense_key[j] +// must always equal dense_shapes[j].NumEntries(). +// If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output +// Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN): +// The dense outputs are just the inputs row-stacked by batch. +// This works for dense_shapes[j] = (-1, D1, ..., DN). In this case +// the shape of the output Tensor dense_values[j] will be +// (|serialized|, M, D1, .., DN), where M is the maximum number of blocks +// of elements of length D1 * .... * DN, across all minibatch entries +// in the input. Any minibatch entry with less than M blocks of elements of +// length D1 * ... * DN will be padded with the corresponding default_value +// scalar element along the second dimension. +func ParseExample(scope *Scope, serialized tf.Output, names tf.Output, sparse_keys []tf.Output, dense_keys []tf.Output, dense_defaults []tf.Output, sparse_types []tf.DataType, dense_shapes []tf.Shape) (sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shapes []tf.Output, dense_values []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sparse_types": sparse_types, "dense_shapes": dense_shapes} + opspec := tf.OpSpec{ + Type: "ParseExample", + Input: []tf.Input{ + serialized, names, tf.OutputList(sparse_keys), tf.OutputList(dense_keys), tf.OutputList(dense_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if sparse_indices, idx, err = makeOutputList(op, idx, "sparse_indices"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + if sparse_values, idx, err = makeOutputList(op, idx, "sparse_values"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + if sparse_shapes, idx, err = makeOutputList(op, idx, "sparse_shapes"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + if dense_values, idx, err = makeOutputList(op, idx, "dense_values"); err != nil { + scope.UpdateErr("ParseExample", err) + return + } + return sparse_indices, sparse_values, sparse_shapes, dense_values +} + +// DatasetToGraphAttr is an optional argument to DatasetToGraph. +type DatasetToGraphAttr func(optionalAttr) + +// DatasetToGraphStatefulWhitelist sets the optional stateful_whitelist attribute to value. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func DatasetToGraphStatefulWhitelist(value []string) DatasetToGraphAttr { + return func(m optionalAttr) { + m["stateful_whitelist"] = value + } +} + +// DatasetToGraphAllowStateful sets the optional allow_stateful attribute to value. +// If not specified, defaults to false +func DatasetToGraphAllowStateful(value bool) DatasetToGraphAttr { + return func(m optionalAttr) { + m["allow_stateful"] = value + } +} + +// DatasetToGraphStripDeviceAssignment sets the optional strip_device_assignment attribute to value. +// If not specified, defaults to false +func DatasetToGraphStripDeviceAssignment(value bool) DatasetToGraphAttr { + return func(m optionalAttr) { + m["strip_device_assignment"] = value + } +} + +// Returns a serialized GraphDef representing `input_dataset`. +// +// Returns a graph representation for `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to return the graph representation for. +// +// Returns The graph representation of the dataset (as serialized GraphDef). +func DatasetToGraph(scope *Scope, input_dataset tf.Output, optional ...DatasetToGraphAttr) (graph tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DatasetToGraph", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyAdadeltaAttr is an optional argument to ResourceSparseApplyAdadelta. +type ResourceSparseApplyAdadeltaAttr func(optionalAttr) + +// ResourceSparseApplyAdadeltaUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdadeltaUseLocking(value bool) ResourceSparseApplyAdadeltaAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// var: Should be from a Variable(). +// +// Arguments: +// +// accum: Should be from a Variable(). +// accum_update: : Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// rho: Decay factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyAdadelta(scope *Scope, var_ tf.Output, accum tf.Output, accum_update tf.Output, lr tf.Output, rho tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdadeltaAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdadelta", + Input: []tf.Input{ + var_, accum, accum_update, lr, rho, epsilon, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes sigmoid of `x` element-wise. +// +// Specifically, `y = 1 / (1 + exp(-x))`. +func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sigmoid", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingADAMParametersGradAccumDebug. +type RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingADAMParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingADAMParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingADAMParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve ADAM embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the ADAM optimization algorithm. +// momenta: Parameter momenta updated by the ADAM optimization algorithm. +// velocities: Parameter velocities updated by the ADAM optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the ADAM optimization algorithm. +func RetrieveTPUEmbeddingADAMParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingADAMParametersGradAccumDebugAttr) (parameters tf.Output, momenta tf.Output, velocities tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingADAMParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// ResourceApplyAdamAttr is an optional argument to ResourceApplyAdam. +type ResourceApplyAdamAttr func(optionalAttr) + +// ResourceApplyAdamUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdamUseLocking(value bool) ResourceApplyAdamAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyAdamUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, uses the nesterov update. +// If not specified, defaults to false +func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update '*var' according to the Adam algorithm. +// +// $$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$ +// $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$ +// $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$ +// $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{v_t} + \epsilon)$$ +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// beta1_power: Must be a scalar. +// beta2_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdam(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, beta2_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdamAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdam", + Input: []tf.Input{ + var_, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResizeNearestNeighborAttr is an optional argument to ResizeNearestNeighbor. +type ResizeNearestNeighborAttr func(optionalAttr) + +// ResizeNearestNeighborAlignCorners sets the optional align_corners attribute to value. +// +// value: If true, the centers of the 4 corner pixels of the input and output tensors are +// aligned, preserving the values at the corner pixels. Defaults to false. +// If not specified, defaults to false +func ResizeNearestNeighborAlignCorners(value bool) ResizeNearestNeighborAttr { + return func(m optionalAttr) { + m["align_corners"] = value + } +} + +// ResizeNearestNeighborHalfPixelCenters sets the optional half_pixel_centers attribute to value. +// If not specified, defaults to false +func ResizeNearestNeighborHalfPixelCenters(value bool) ResizeNearestNeighborAttr { + return func(m optionalAttr) { + m["half_pixel_centers"] = value + } +} + +// Resize `images` to `size` using nearest neighbor interpolation. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// size: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The +// new size for the images. +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ResizeNearestNeighbor(scope *Scope, images tf.Output, size tf.Output, optional ...ResizeNearestNeighborAttr) (resized_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResizeNearestNeighbor", + Input: []tf.Input{ + images, size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A placeholder op for a value that will be fed into the computation. +// +// Arguments: +// dtype: The type of elements in the tensor. +// shape: The shape of the tensor. +// +// Returns A tensor that will be provided using the infeed mechanism. +func InfeedDequeue(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + opspec := tf.OpSpec{ + Type: "InfeedDequeue", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Encodes a `RaggedTensor` into a `variant` Tensor. +// +// +// Encodes the given `RaggedTensor` and returns a `variant` Tensor. If +// `batched_input` is True, then input `RaggedTensor` is unbatched along the +// zero-th dimension, each component `RaggedTensor` is encoded into a scalar +// `variant` Tensor, and these are stacked to return a 1-D `variant` Tensor. +// If `batched_input` is False, then the input `RaggedTensor` is encoded as is and +// a scalar `variant` Tensor is returned. A `RaggedTensor` is encoded by first +// creating a 1-D `variant` Tensor with `ragged_rank + 1` elements, containing the +// splits and values Tensors of the `RaggedTensor`. Then the 1-D `variant` Tensor +// is wrapped in a scalar `variant` Tensor. See `RaggedTensorFromVariant` for the +// corresponding decoding logic. +// +// +// Arguments: +// rt_nested_splits: A list of one or more Tensors representing the splits of the input +// `RaggedTensor`. +// rt_dense_values: A Tensor representing the values of the input `RaggedTensor`. +// batched_input: A `bool` denoting whether the input is a batched `RaggedTensor`. +// +// Returns A `variant` Tensor that containing encoded `RaggedTensor`. +func RaggedTensorToVariant(scope *Scope, rt_nested_splits []tf.Output, rt_dense_values tf.Output, batched_input bool) (encoded_ragged tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"batched_input": batched_input} + opspec := tf.OpSpec{ + Type: "RaggedTensorToVariant", + Input: []tf.Input{ + tf.OutputList(rt_nested_splits), rt_dense_values, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyKerasMomentumAttr is an optional argument to ResourceApplyKerasMomentum. +type ResourceApplyKerasMomentumAttr func(optionalAttr) + +// ResourceApplyKerasMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyKerasMomentumUseLocking(value bool) ResourceApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyKerasMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var + momentum * accum, so in the end, the var you get is actually +// var + momentum * accum. +// If not specified, defaults to false +func ResourceApplyKerasMomentumUseNesterov(value bool) ResourceApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update '*var' according to the momentum scheme. +// +// Set use_nesterov = True if you want to use Nesterov momentum. +// +// accum = accum * momentum - lr * grad +// var += accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyKerasMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyKerasMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyKerasMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// TensorArrayConcatV2Attr is an optional argument to TensorArrayConcatV2. +type TensorArrayConcatV2Attr func(optionalAttr) + +// TensorArrayConcatV2ElementShapeExcept0 sets the optional element_shape_except0 attribute to value. +// If not specified, defaults to +func TensorArrayConcatV2ElementShapeExcept0(value tf.Shape) TensorArrayConcatV2Attr { + return func(m optionalAttr) { + m["element_shape_except0"] = value + } +} + +// Deprecated. Use TensorArrayConcatV3 +func TensorArrayConcatV2(scope *Scope, handle tf.Output, flow_in tf.Output, dtype tf.DataType, optional ...TensorArrayConcatV2Attr) (value tf.Output, lengths tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorArrayConcatV2", + Input: []tf.Input{ + handle, flow_in, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MatrixSolveAttr is an optional argument to MatrixSolve. +type MatrixSolveAttr func(optionalAttr) + +// MatrixSolveAdjoint sets the optional adjoint attribute to value. +// +// value: Boolean indicating whether to solve with `matrix` or its (block-wise) +// adjoint. +// If not specified, defaults to false +func MatrixSolveAdjoint(value bool) MatrixSolveAttr { + return func(m optionalAttr) { + m["adjoint"] = value + } +} + +// Solves systems of linear equations. +// +// `Matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. `Rhs` is a tensor of shape `[..., M, K]`. The `output` is +// a tensor shape `[..., M, K]`. If `adjoint` is `False` then each output matrix +// satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. +// If `adjoint` is `True` then each output matrix satisfies +// `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`. +// +// Arguments: +// matrix: Shape is `[..., M, M]`. +// rhs: Shape is `[..., M, K]`. +// +// Returns Shape is `[..., M, K]`. +func MatrixSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...MatrixSolveAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MatrixSolve", + Input: []tf.Input{ + matrix, rhs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Writes contents to the file at input filename. Creates file and recursively +// +// creates directory if not existing. +// +// Arguments: +// filename: scalar. The name of the file to which we write the contents. +// contents: scalar. The content to be written to the output file. +// +// Returns the created operation. +func WriteFile(scope *Scope, filename tf.Output, contents tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "WriteFile", + Input: []tf.Input{ + filename, contents, + }, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyMomentumAttr is an optional argument to ResourceSparseApplyMomentum. +type ResourceSparseApplyMomentumAttr func(optionalAttr) + +// ResourceSparseApplyMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseLocking(value bool) ResourceSparseApplyMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var - lr * momentum * accum, so in the end, the var you get is actually +// var - lr * momentum * accum. +// If not specified, defaults to false +func ResourceSparseApplyMomentumUseNesterov(value bool) ResourceSparseApplyMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the momentum scheme. +// +// Set use_nesterov = True if you want to use Nesterov momentum. +// +// That is for rows we have grad for, we update var and accum as follows: +// +// accum = accum * momentum + grad +// var -= lr * accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, indices, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// RecvAttr is an optional argument to Recv. +type RecvAttr func(optionalAttr) + +// RecvClientTerminated sets the optional client_terminated attribute to value. +// +// value: If set to true, this indicates that the node was added +// to the graph as a result of a client-side feed or fetch of Tensor data, +// in which case the corresponding send or recv is expected to be managed +// locally by the caller. +// If not specified, defaults to false +func RecvClientTerminated(value bool) RecvAttr { + return func(m optionalAttr) { + m["client_terminated"] = value + } +} + +// Receives the named tensor from send_device on recv_device. +// +// Arguments: +// +// tensor_name: The name of the tensor to receive. +// send_device: The name of the device sending the tensor. +// send_device_incarnation: The current incarnation of send_device. +// recv_device: The name of the device receiving the tensor. +// +// Returns The tensor to receive. +func Recv(scope *Scope, tensor_type tf.DataType, tensor_name string, send_device string, send_device_incarnation int64, recv_device string, optional ...RecvAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"tensor_type": tensor_type, "tensor_name": tensor_name, "send_device": send_device, "send_device_incarnation": send_device_incarnation, "recv_device": recv_device} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Recv", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OrderedMapStageAttr is an optional argument to OrderedMapStage. +type OrderedMapStageAttr func(optionalAttr) + +// OrderedMapStageCapacity sets the optional capacity attribute to value. +// +// value: Maximum number of elements in the Staging Area. If > 0, inserts +// on the container will block when the capacity is reached. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapStageCapacity(value int64) OrderedMapStageAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// OrderedMapStageMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func OrderedMapStageMemoryLimit(value int64) OrderedMapStageAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// OrderedMapStageContainer sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. Otherwise, +// a default container is used. +// If not specified, defaults to "" +func OrderedMapStageContainer(value string) OrderedMapStageAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// OrderedMapStageSharedName sets the optional shared_name attribute to value. +// +// value: It is necessary to match this name to the matching Unstage Op. +// If not specified, defaults to "" +func OrderedMapStageSharedName(value string) OrderedMapStageAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Stage (key, values) in the underlying container which behaves like a ordered +// +// associative container. Elements are ordered by key. +// +// Arguments: +// key: int64 +// +// values: a list of tensors +// dtypes A list of data types that inserted values should adhere to. +// +// +// Returns the created operation. +func OrderedMapStage(scope *Scope, key tf.Output, indices tf.Output, values []tf.Output, dtypes []tf.DataType, optional ...OrderedMapStageAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OrderedMapStage", + Input: []tf.Input{ + key, indices, tf.OutputList(values), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// TPUReplicateMetadataAttr is an optional argument to TPUReplicateMetadata. +type TPUReplicateMetadataAttr func(optionalAttr) + +// TPUReplicateMetadataNumCoresPerReplica sets the optional num_cores_per_replica attribute to value. +// +// value: Number of cores per replica. Used for model parallelism. +// If not specified, defaults to 1 +func TPUReplicateMetadataNumCoresPerReplica(value int64) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["num_cores_per_replica"] = value + } +} + +// TPUReplicateMetadataTopology sets the optional topology attribute to value. +// +// value: TopologyProto indicating the topology of the TPU pod slice. +// If not specified, defaults to "" +func TPUReplicateMetadataTopology(value string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["topology"] = value + } +} + +// TPUReplicateMetadataUseTpu sets the optional use_tpu attribute to value. +// +// value: Whether to place the computation on the TPU. +// If not specified, defaults to true +func TPUReplicateMetadataUseTpu(value bool) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["use_tpu"] = value + } +} + +// TPUReplicateMetadataDeviceAssignment sets the optional device_assignment attribute to value. +// +// value: The assignment of devices for the computation. +// If not specified, defaults to <> +func TPUReplicateMetadataDeviceAssignment(value []int64) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["device_assignment"] = value + } +} + +// TPUReplicateMetadataComputationShape sets the optional computation_shape attribute to value. +// +// value: DEPRECATED. Use num_cores_per_replica instead. +// If not specified, defaults to <> +func TPUReplicateMetadataComputationShape(value []int64) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["computation_shape"] = value + } +} + +// TPUReplicateMetadataHostComputeCore sets the optional host_compute_core attribute to value. +// If not specified, defaults to <> +func TPUReplicateMetadataHostComputeCore(value []string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["host_compute_core"] = value + } +} + +// TPUReplicateMetadataPaddingMap sets the optional padding_map attribute to value. +// If not specified, defaults to <> +func TPUReplicateMetadataPaddingMap(value []string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["padding_map"] = value + } +} + +// TPUReplicateMetadataStepMarkerLocation sets the optional step_marker_location attribute to value. +// If not specified, defaults to "STEP_MARK_AT_ENTRY" +func TPUReplicateMetadataStepMarkerLocation(value string) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["step_marker_location"] = value + } +} + +// TPUReplicateMetadataAllowSoftPlacement sets the optional allow_soft_placement attribute to value. +// If not specified, defaults to false +func TPUReplicateMetadataAllowSoftPlacement(value bool) TPUReplicateMetadataAttr { + return func(m optionalAttr) { + m["allow_soft_placement"] = value + } +} + +// Metadata indicating how the TPU computation should be replicated. +// +// This operation holds the metadata common to operations of a `tpu.replicate()` computation subgraph. +// +// Arguments: +// num_replicas: Number of replicas of the computation +// +// Returns the created operation. +func TPUReplicateMetadata(scope *Scope, num_replicas int64, optional ...TPUReplicateMetadataAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_replicas": num_replicas} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TPUReplicateMetadata", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// TensorListConcatAttr is an optional argument to TensorListConcat. +type TensorListConcatAttr func(optionalAttr) + +// TensorListConcatElementShape sets the optional element_shape attribute to value. +// If not specified, defaults to +func TensorListConcatElementShape(value tf.Shape) TensorListConcatAttr { + return func(m optionalAttr) { + m["element_shape"] = value + } +} + +// Concats all tensors in the list along the 0th dimension. +// +// Requires that all tensors have the same shape except the first dimension. +// +// input_handle: The input list. +// tensor: The concated result. +// lengths: Output tensor containing sizes of the 0th dimension of tensors in the list, used for computing the gradient. +// +func TensorListConcat(scope *Scope, input_handle tf.Output, element_dtype tf.DataType, optional ...TensorListConcatAttr) (tensor tf.Output, lengths tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorListConcat", + Input: []tf.Input{ + input_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingRMSPropParametersGradAccumDebug. +type LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingRMSPropParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingRMSPropParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingRMSPropParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load RMSProp embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the RMSProp optimization algorithm. +// ms: Value of ms used in the RMSProp optimization algorithm. +// mom: Value of mom used in the RMSProp optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the RMSProp optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingRMSPropParametersGradAccumDebug(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingRMSPropParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingRMSPropParametersGradAccumDebug", + Input: []tf.Input{ + parameters, ms, mom, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingFTRLParametersGradAccumDebug. +type LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingFTRLParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingFTRLParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingFTRLParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingFTRLParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingFTRLParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingFTRLParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load FTRL embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the FTRL optimization algorithm. +// accumulators: Value of accumulators used in the FTRL optimization algorithm. +// linears: Value of linears used in the FTRL optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the FTRL optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingFTRLParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, linears tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingFTRLParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingFTRLParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, linears, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingAdadeltaParametersAttr is an optional argument to LoadTPUEmbeddingAdadeltaParameters. +type LoadTPUEmbeddingAdadeltaParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingAdadeltaParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingAdadeltaParametersTableId(value int64) LoadTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdadeltaParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdadeltaParametersTableName(value string) LoadTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingAdadeltaParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdadeltaParametersConfig(value string) LoadTPUEmbeddingAdadeltaParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load Adadelta embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adadelta optimization algorithm. +// accumulators: Value of accumulators used in the Adadelta optimization algorithm. +// updates: Value of updates used in the Adadelta optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdadeltaParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, updates tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdadeltaParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdadeltaParameters", + Input: []tf.Input{ + parameters, accumulators, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2. +type ResourceSparseApplyFtrlV2Attr func(optionalAttr) + +// ResourceSparseApplyFtrlV2UseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2Attr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyFtrlV2MultiplyLinearByLr sets the optional multiply_linear_by_lr attribute to value. +// If not specified, defaults to false +func ResourceSparseApplyFtrlV2MultiplyLinearByLr(value bool) ResourceSparseApplyFtrlV2Attr { + return func(m optionalAttr) { + m["multiply_linear_by_lr"] = value + } +} + +// Update relevant entries in '*var' according to the Ftrl-proximal scheme. +// +// That is for rows we have grad for, we update var, accum and linear as follows: +// grad_with_shrinkage = grad + 2 * l2_shrinkage * var +// accum_new = accum + grad_with_shrinkage * grad_with_shrinkage +// linear += grad_with_shrinkage + +// (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var +// quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 +// var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 +// accum = accum_new +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// linear: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 shrinkage regularization. Must be a scalar. +// +// lr_power: Scaling factor. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyFtrlV2(scope *Scope, var_ tf.Output, accum tf.Output, linear tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, l2_shrinkage tf.Output, lr_power tf.Output, optional ...ResourceSparseApplyFtrlV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyFtrlV2", + Input: []tf.Input{ + var_, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyAdagradV2Attr is an optional argument to ResourceSparseApplyAdagradV2. +type ResourceSparseApplyAdagradV2Attr func(optionalAttr) + +// ResourceSparseApplyAdagradV2UseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradV2UseLocking(value bool) ResourceSparseApplyAdagradV2Attr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyAdagradV2UpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceSparseApplyAdagradV2UpdateSlots(value bool) ResourceSparseApplyAdagradV2Attr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the adagrad scheme. +// +// That is for rows we have grad for, we update var and accum as follows: +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyAdagradV2(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, epsilon tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyAdagradV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdagradV2", + Input: []tf.Input{ + var_, accum, lr, epsilon, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Restore a Reader to its initial clean state. +// +// Arguments: +// reader_handle: Handle to a Reader. +// +// Returns the created operation. +func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderResetV2", + Input: []tf.Input{ + reader_handle, + }, + } + return scope.AddOperation(opspec) +} + +// FakeQuantWithMinMaxVarsPerChannelGradientAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannelGradient. +type FakeQuantWithMinMaxVarsPerChannelGradientAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsPerChannelGradientNumBits sets the optional num_bits attribute to value. +// +// value: The bitwidth of the quantization; between 2 and 16, inclusive. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsPerChannelGradientNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelGradientAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsPerChannelGradientNarrowRange sets the optional narrow_range attribute to value. +// +// value: Whether to quantize into 2^num_bits - 1 distinct values. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsPerChannelGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelGradientAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation. +// +// Arguments: +// gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation, +// shape one of: `[d]`, `[b, d]`, `[b, h, w, d]`. +// inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape +// same as `gradients`. +// min, max: Quantization interval, floats of shape `[d]`. +// +// +// +// Returns: +// backprops_wrt_input: Backpropagated gradients w.r.t. inputs, shape same as +// `inputs`: +// `gradients * (inputs >= min && inputs <= max)`. +// backprop_wrt_min: Backpropagated gradients w.r.t. min parameter, shape `[d]`: +// `sum_per_d(gradients * (inputs < min))`. +// backprop_wrt_max: Backpropagated gradients w.r.t. max parameter, shape `[d]`: +// `sum_per_d(gradients * (inputs > max))`. +func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVarsPerChannelGradient", + Input: []tf.Input{ + gradients, inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// PrintV2Attr is an optional argument to PrintV2. +type PrintV2Attr func(optionalAttr) + +// PrintV2OutputStream sets the optional output_stream attribute to value. +// +// value: A string specifying the output stream or logging level to print to. +// If not specified, defaults to "stderr" +func PrintV2OutputStream(value string) PrintV2Attr { + return func(m optionalAttr) { + m["output_stream"] = value + } +} + +// PrintV2End sets the optional end attribute to value. +// If not specified, defaults to "\n" +func PrintV2End(value string) PrintV2Attr { + return func(m optionalAttr) { + m["end"] = value + } +} + +// Prints a string scalar. +// +// Prints a string scalar to the desired output_stream. +// +// Arguments: +// input: The string scalar to print. +// +// Returns the created operation. +func PrintV2(scope *Scope, input tf.Output, optional ...PrintV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PrintV2", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Inserts a dimension of 1 into a tensor's shape. +// +// Given a tensor `input`, this operation inserts a dimension of 1 at the +// dimension index `axis` of `input`'s shape. The dimension index `axis` starts at +// zero; if you specify a negative number for `axis` it is counted backward from +// the end. +// +// This operation is useful if you want to add a batch dimension to a single +// element. For example, if you have a single image of shape `[height, width, +// channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, +// which will make the shape `[1, height, width, channels]`. +// +// Other examples: +// +// ``` +// # 't' is a tensor of shape [2] +// shape(expand_dims(t, 0)) ==> [1, 2] +// shape(expand_dims(t, 1)) ==> [2, 1] +// shape(expand_dims(t, -1)) ==> [2, 1] +// +// # 't2' is a tensor of shape [2, 3, 5] +// shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] +// shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] +// shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] +// ``` +// +// This operation requires that: +// +// `-1-input.dims() <= dim <= input.dims()` +// +// This operation is related to `squeeze()`, which removes dimensions of +// size 1. +// +// Arguments: +// +// axis: 0-D (scalar). Specifies the dimension index at which to +// expand the shape of `input`. Must be in the range +// `[-rank(input) - 1, rank(input)]`. +// +// Returns Contains the same data as `input`, but its shape has an additional +// dimension of size 1 added. +func ExpandDims(scope *Scope, input tf.Output, axis tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ExpandDims", + Input: []tf.Input{ + input, axis, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceSparseApplyProximalGradientDescentAttr is an optional argument to ResourceSparseApplyProximalGradientDescent. +type ResourceSparseApplyProximalGradientDescentAttr func(optionalAttr) + +// ResourceSparseApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value. +// +// value: If True, the subtraction will be protected by a lock; +// otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyProximalGradientDescentUseLocking(value bool) ResourceSparseApplyProximalGradientDescentAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Sparse update '*var' as FOBOS algorithm with fixed learning rate. +// +// That is for rows we have grad for, we update var as follows: +// prox_v = var - alpha * grad +// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// alpha: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// +// Returns the created operation. +func ResourceSparseApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, indices tf.Output, optional ...ResourceSparseApplyProximalGradientDescentAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyProximalGradientDescent", + Input: []tf.Input{ + var_, alpha, l1, l2, grad, indices, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// SparseMatrixTransposeAttr is an optional argument to SparseMatrixTranspose. +type SparseMatrixTransposeAttr func(optionalAttr) + +// SparseMatrixTransposeConjugate sets the optional conjugate attribute to value. +// +// value: Indicates whether `input` should be conjugated. +// If not specified, defaults to false +func SparseMatrixTransposeConjugate(value bool) SparseMatrixTransposeAttr { + return func(m optionalAttr) { + m["conjugate"] = value + } +} + +// Transposes the inner (matrix) dimensions of a CSRSparseMatrix. +// +// Transposes the inner (matrix) dimensions of a SparseMatrix and optionally +// conjugates its values. +// +// Arguments: +// input: A CSRSparseMatrix. +// +// +// Returns A CSRSparseMatrix. +func SparseMatrixTranspose(scope *Scope, input tf.Output, type_ tf.DataType, optional ...SparseMatrixTransposeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseMatrixTranspose", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingProximalAdagradParametersAttr is an optional argument to LoadTPUEmbeddingProximalAdagradParameters. +type LoadTPUEmbeddingProximalAdagradParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingProximalAdagradParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingProximalAdagradParametersTableId(value int64) LoadTPUEmbeddingProximalAdagradParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingProximalAdagradParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingProximalAdagradParametersTableName(value string) LoadTPUEmbeddingProximalAdagradParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingProximalAdagradParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingProximalAdagradParametersConfig(value string) LoadTPUEmbeddingProximalAdagradParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load proximal Adagrad embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the proximal Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the proximal Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingProximalAdagradParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingProximalAdagradParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingProximalAdagradParameters", + Input: []tf.Input{ + parameters, accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA. +type ResourceSparseApplyAdagradDAAttr func(optionalAttr) + +// ResourceSparseApplyAdagradDAUseLocking sets the optional use_locking attribute to value. +// +// value: If True, updating of the var and accum tensors will be protected by +// a lock; otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceSparseApplyAdagradDAUseLocking(value bool) ResourceSparseApplyAdagradDAAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update entries in '*var' and '*accum' according to the proximal adagrad scheme. +// +// Arguments: +// var_: Should be from a Variable(). +// gradient_accumulator: Should be from a Variable(). +// gradient_squared_accumulator: Should be from a Variable(). +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// lr: Learning rate. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// global_step: Training step number. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyAdagradDA(scope *Scope, var_ tf.Output, gradient_accumulator tf.Output, gradient_squared_accumulator tf.Output, grad tf.Output, indices tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, global_step tf.Output, optional ...ResourceSparseApplyAdagradDAAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyAdagradDA", + Input: []tf.Input{ + var_, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Applies softmax to a batched N-D `SparseTensor`. +// +// The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` +// (where `N >= 2`), and with indices sorted in the canonical lexicographic order. +// +// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost +// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly +// zero elements do not participate*. Specifically, the algorithm is equivalent +// to the following: +// +// (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix +// with shape `[B, C]`, along the size-C dimension; +// (2) Masks out the original implicitly-zero locations; +// (3) Renormalizes the remaining elements. +// +// Hence, the `SparseTensor` result has exactly the same non-zero indices and +// shape. +// +// Arguments: +// sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a +// SparseTensor, in canonical ordering. +// sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// +// Returns 1-D. The `NNZ` values for the result `SparseTensor`. +func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSoftmax", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Adjust the hue of one or more images. +// +// `images` is a tensor of at least 3 dimensions. The last dimension is +// interpreted as channels, and must be three. +// +// The input image is considered in the RGB colorspace. Conceptually, the RGB +// colors are first mapped into HSV. A delta is then applied all the hue values, +// and then remapped back to RGB colorspace. +// +// Arguments: +// images: Images to adjust. At least 3-D. +// delta: A float delta to add to the hue. +// +// Returns The hue-adjusted image or images. +func AdjustHue(scope *Scope, images tf.Output, delta tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "AdjustHue", + Input: []tf.Input{ + images, delta, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes hyperbolic cosine of x element-wise. +// +// Given an input tensor, this function computes hyperbolic cosine of every +// element in the tensor. Input range is `[-inf, inf]` and output range +// is `[1, inf]`. +// +// ```python +// x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")]) +// tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf] +// ``` +func Cosh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Cosh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CollectiveReduceAttr is an optional argument to CollectiveReduce. +type CollectiveReduceAttr func(optionalAttr) + +// CollectiveReduceWaitFor sets the optional wait_for attribute to value. +// If not specified, defaults to <> +func CollectiveReduceWaitFor(value []int64) CollectiveReduceAttr { + return func(m optionalAttr) { + m["wait_for"] = value + } +} + +// CollectiveReduceCommunicationHint sets the optional communication_hint attribute to value. +// If not specified, defaults to "auto" +func CollectiveReduceCommunicationHint(value string) CollectiveReduceAttr { + return func(m optionalAttr) { + m["communication_hint"] = value + } +} + +// CollectiveReduceTimeoutSeconds sets the optional timeout_seconds attribute to value. +// If not specified, defaults to 0 +func CollectiveReduceTimeoutSeconds(value float32) CollectiveReduceAttr { + return func(m optionalAttr) { + m["timeout_seconds"] = value + } +} + +// Mutually reduces multiple tensors of identical type and shape. +func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64, optional ...CollectiveReduceAttr) (data tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"group_size": group_size, "group_key": group_key, "instance_key": instance_key, "merge_op": merge_op, "final_op": final_op, "subdiv_offsets": subdiv_offsets} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CollectiveReduce", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyAdaMaxAttr is an optional argument to ResourceApplyAdaMax. +type ResourceApplyAdaMaxAttr func(optionalAttr) + +// ResourceApplyAdaMaxUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdaMaxUseLocking(value bool) ResourceApplyAdaMaxAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the AdaMax algorithm. +// +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// v_t <- max(beta2 * v_{t-1}, abs(g)) +// variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// beta1_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdaMax(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdaMaxAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdaMax", + Input: []tf.Input{ + var_, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the LSTM cell backward propagation for the entire time sequence. +// +// This implementation is to be used in conjunction of BlockLSTMV2. +// +// Arguments: +// seq_len_max: Maximum time length actually used by this input. Outputs are padded +// with zeros beyond this length. +// x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). +// cs_prev: Value of the initial cell state. +// h_prev: Initial output of cell (to be used for peephole). +// w: The weight matrix. +// wci: The weight matrix for input gate peephole connection. +// wcf: The weight matrix for forget gate peephole connection. +// wco: The weight matrix for output gate peephole connection. +// b: The bias vector. +// i: The input gate over the whole time sequence. +// cs: The cell state before the tanh over the whole time sequence. +// f: The forget gate over the whole time sequence. +// o: The output gate over the whole time sequence. +// ci: The cell input over the whole time sequence. +// co: The cell after the tanh over the whole time sequence. +// h: The output h vector over the whole time sequence. +// cs_grad: The current gradient of cs. +// h_grad: The gradient of h vector. +// use_peephole: Whether to use peephole weights. +// +// Returns: +// x_grad: The gradient of x to be back-propped. +// cs_prev_grad: The gradient of cs_prev to be back-propped. +// h_prev_grad: The gradient of h_prev to be back-propped. +// w_grad: The gradient for w to be back-propped. +// wci_grad: The gradient for wci to be back-propped. +// wcf_grad: The gradient for wcf to be back-propped. +// wco_grad: The gradient for wco to be back-propped. +// b_grad: The gradient for w to be back-propped. +func BlockLSTMGradV2(scope *Scope, seq_len_max tf.Output, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output, cs_grad tf.Output, h_grad tf.Output, use_peephole bool) (x_grad tf.Output, cs_prev_grad tf.Output, h_prev_grad tf.Output, w_grad tf.Output, wci_grad tf.Output, wcf_grad tf.Output, wco_grad tf.Output, b_grad tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"use_peephole": use_peephole} + opspec := tf.OpSpec{ + Type: "BlockLSTMGradV2", + Input: []tf.Input{ + seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6), op.Output(7) +} + +// Returns the element-wise max of two SparseTensors. +// +// Assumes the two SparseTensors have the same shape, i.e., no broadcasting. +// +// Arguments: +// a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, in the canonical lexicographic ordering. +// a_values: 1-D. `N` non-empty values corresponding to `a_indices`. +// a_shape: 1-D. Shape of the input SparseTensor. +// b_indices: counterpart to `a_indices` for the other operand. +// b_values: counterpart to `a_values` for the other operand; must be of the same dtype. +// b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. +// +// Returns: +// output_indices: 2-D. The indices of the output SparseTensor. +// output_values: 1-D. The values of the output SparseTensor. +func SparseSparseMaximum(scope *Scope, a_indices tf.Output, a_values tf.Output, a_shape tf.Output, b_indices tf.Output, b_values tf.Output, b_shape tf.Output) (output_indices tf.Output, output_values tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSparseMaximum", + Input: []tf.Input{ + a_indices, a_values, a_shape, b_indices, b_values, b_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// LSTMBlockCellAttr is an optional argument to LSTMBlockCell. +type LSTMBlockCellAttr func(optionalAttr) + +// LSTMBlockCellForgetBias sets the optional forget_bias attribute to value. +// +// value: The forget gate bias. +// If not specified, defaults to 1 +func LSTMBlockCellForgetBias(value float32) LSTMBlockCellAttr { + return func(m optionalAttr) { + m["forget_bias"] = value + } +} + +// LSTMBlockCellCellClip sets the optional cell_clip attribute to value. +// +// value: Value to clip the 'cs' value to. +// If not specified, defaults to 3 +func LSTMBlockCellCellClip(value float32) LSTMBlockCellAttr { + return func(m optionalAttr) { + m["cell_clip"] = value + } +} + +// LSTMBlockCellUsePeephole sets the optional use_peephole attribute to value. +// +// value: Whether to use peephole weights. +// If not specified, defaults to false +func LSTMBlockCellUsePeephole(value bool) LSTMBlockCellAttr { + return func(m optionalAttr) { + m["use_peephole"] = value + } +} + +// Computes the LSTM cell forward propagation for 1 time step. +// +// This implementation uses 1 weight matrix and 1 bias vector, and there's an +// optional peephole connection. +// +// This kernel op implements the following mathematical equations: +// +// ```python +// xh = [x, h_prev] +// [i, f, ci, o] = xh * w + b +// f = f + forget_bias +// +// if not use_peephole: +// wci = wcf = wco = 0 +// +// i = sigmoid(cs_prev * wci + i) +// f = sigmoid(cs_prev * wcf + f) +// ci = tanh(ci) +// +// cs = ci .* i + cs_prev .* f +// cs = clip(cs, cell_clip) +// +// o = sigmoid(cs * wco + o) +// co = tanh(cs) +// h = co .* o +// ``` +// +// Arguments: +// x: The input to the LSTM cell, shape (batch_size, num_inputs). +// cs_prev: Value of the cell state at previous time step. +// h_prev: Output of the previous cell at previous time step. +// w: The weight matrix. +// wci: The weight matrix for input gate peephole connection. +// wcf: The weight matrix for forget gate peephole connection. +// wco: The weight matrix for output gate peephole connection. +// b: The bias vector. +// +// Returns: +// i: The input gate. +// cs: The cell state before the tanh. +// f: The forget gate. +// o: The output gate. +// ci: The cell input. +// co: The cell after the tanh. +// h: The output h vector. +func LSTMBlockCell(scope *Scope, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, optional ...LSTMBlockCellAttr) (i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LSTMBlockCell", + Input: []tf.Input{ + x, cs_prev, h_prev, w, wci, wcf, wco, b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6) +} + +// A TPU core selector Op. +// +// This Op produces a set of TPU cores (for warm-up) or a single TPU core +// (for regular inference) to execute the TPU program on. The output is +// consumed by TPUPartitionedCall. +// +// Returns A vector 1 or more TPU cores. +func TPUOrdinalSelector(scope *Scope) (device_ordinals tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TPUOrdinalSelector", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SubstrAttr is an optional argument to Substr. +type SubstrAttr func(optionalAttr) + +// SubstrUnit sets the optional unit attribute to value. +// +// value: The unit that is used to create the substring. One of: `"BYTE"` (for +// defining position and length by bytes) or `"UTF8_CHAR"` (for the UTF-8 +// encoded Unicode code points). The default is `"BYTE"`. Results are undefined if +// `unit=UTF8_CHAR` and the `input` strings do not contain structurally valid +// UTF-8. +// If not specified, defaults to "BYTE" +func SubstrUnit(value string) SubstrAttr { + return func(m optionalAttr) { + m["unit"] = value + } +} + +// Return substrings from `Tensor` of strings. +// +// For each string in the input `Tensor`, creates a substring starting at index +// `pos` with a total length of `len`. +// +// If `len` defines a substring that would extend beyond the length of the input +// string, or if `len` is negative, then as many characters as possible are used. +// +// A negative `pos` indicates distance within the string backwards from the end. +// +// If `pos` specifies an index which is out of range for any of the input strings, +// then an `InvalidArgumentError` is thrown. +// +// `pos` and `len` must have the same shape, otherwise a `ValueError` is thrown on +// Op creation. +// +// *NOTE*: `Substr` supports broadcasting up to two dimensions. More about +// broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// --- +// +// Examples +// +// Using scalar `pos` and `len`: +// +// ```python +// input = [b'Hello', b'World'] +// position = 1 +// length = 3 +// +// output = [b'ell', b'orl'] +// ``` +// +// Using `pos` and `len` with same shape as `input`: +// +// ```python +// input = [[b'ten', b'eleven', b'twelve'], +// [b'thirteen', b'fourteen', b'fifteen'], +// [b'sixteen', b'seventeen', b'eighteen']] +// position = [[1, 2, 3], +// [1, 2, 3], +// [1, 2, 3]] +// length = [[2, 3, 4], +// [4, 3, 2], +// [5, 5, 5]] +// +// output = [[b'en', b'eve', b'lve'], +// [b'hirt', b'urt', b'te'], +// [b'ixtee', b'vente', b'hteen']] +// ``` +// +// Broadcasting `pos` and `len` onto `input`: +// +// ``` +// input = [[b'ten', b'eleven', b'twelve'], +// [b'thirteen', b'fourteen', b'fifteen'], +// [b'sixteen', b'seventeen', b'eighteen'], +// [b'nineteen', b'twenty', b'twentyone']] +// position = [1, 2, 3] +// length = [1, 2, 3] +// +// output = [[b'e', b'ev', b'lve'], +// [b'h', b'ur', b'tee'], +// [b'i', b've', b'hte'], +// [b'i', b'en', b'nty']] +// ``` +// +// Broadcasting `input` onto `pos` and `len`: +// +// ``` +// input = b'thirteen' +// position = [1, 5, 7] +// length = [3, 2, 1] +// +// output = [b'hir', b'ee', b'n'] +// ``` +// +// Raises: +// +// * `ValueError`: If the first argument cannot be converted to a +// Tensor of `dtype string`. +// * `InvalidArgumentError`: If indices are out of range. +// * `ValueError`: If `pos` and `len` are not the same shape. +// +// +// Arguments: +// input: Tensor of strings +// pos: Scalar defining the position of first character in each substring +// len: Scalar defining the number of characters to include in each substring +// +// Returns Tensor of substrings +func Substr(scope *Scope, input tf.Output, pos tf.Output, len tf.Output, optional ...SubstrAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Substr", + Input: []tf.Input{ + input, pos, len, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Convert JSON-encoded Example records to binary protocol buffer strings. +// +// This op translates a tensor containing Example records, encoded using +// the [standard JSON +// mapping](https://developers.google.com/protocol-buffers/docs/proto3#json), +// into a tensor containing the same records encoded as binary protocol +// buffers. The resulting tensor can then be fed to any of the other +// Example-parsing ops. +// +// Arguments: +// json_examples: Each string is a JSON object serialized according to the JSON +// mapping of the Example proto. +// +// Returns Each string is a binary Example protocol buffer corresponding +// to the respective element of `json_examples`. +func DecodeJSONExample(scope *Scope, json_examples tf.Output) (binary_examples tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DecodeJSONExample", + Input: []tf.Input{ + json_examples, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Elementwise computes the bitwise AND of `x` and `y`. +// +// The result will have those bits set, that are set in both `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +// +// For example: +// +// ```python +// import tensorflow as tf +// from tensorflow.python.ops import bitwise_ops +// dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64, +// tf.uint8, tf.uint16, tf.uint32, tf.uint64] +// +// for dtype in dtype_list: +// lhs = tf.constant([0, 5, 3, 14], dtype=dtype) +// rhs = tf.constant([5, 0, 7, 11], dtype=dtype) +// exp = tf.constant([0, 0, 3, 10], dtype=tf.float32) +// +// res = bitwise_ops.bitwise_and(lhs, rhs) +// tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE +// ``` +// +func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BitwiseAnd", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DecodeCSVAttr is an optional argument to DecodeCSV. +type DecodeCSVAttr func(optionalAttr) + +// DecodeCSVFieldDelim sets the optional field_delim attribute to value. +// +// value: char delimiter to separate fields in a record. +// If not specified, defaults to "," +func DecodeCSVFieldDelim(value string) DecodeCSVAttr { + return func(m optionalAttr) { + m["field_delim"] = value + } +} + +// DecodeCSVUseQuoteDelim sets the optional use_quote_delim attribute to value. +// +// value: If false, treats double quotation marks as regular +// characters inside of the string fields (ignoring RFC 4180, Section 2, +// Bullet 5). +// If not specified, defaults to true +func DecodeCSVUseQuoteDelim(value bool) DecodeCSVAttr { + return func(m optionalAttr) { + m["use_quote_delim"] = value + } +} + +// DecodeCSVNaValue sets the optional na_value attribute to value. +// +// value: Additional string to recognize as NA/NaN. +// If not specified, defaults to "" +func DecodeCSVNaValue(value string) DecodeCSVAttr { + return func(m optionalAttr) { + m["na_value"] = value + } +} + +// DecodeCSVSelectCols sets the optional select_cols attribute to value. +// If not specified, defaults to <> +func DecodeCSVSelectCols(value []int64) DecodeCSVAttr { + return func(m optionalAttr) { + m["select_cols"] = value + } +} + +// Convert CSV records to tensors. Each column maps to one tensor. +// +// RFC 4180 format is expected for the CSV records. +// (https://tools.ietf.org/html/rfc4180) +// Note that we allow leading and trailing spaces with int or float field. +// +// Arguments: +// records: Each string is a record/row in the csv and all records should have +// the same format. +// record_defaults: One tensor per column of the input record, with either a +// scalar default value for that column or an empty vector if the column is +// required. +// +// Returns Each tensor will have the same shape as records. +func DecodeCSV(scope *Scope, records tf.Output, record_defaults []tf.Output, optional ...DecodeCSVAttr) (output []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeCSV", + Input: []tf.Input{ + records, tf.OutputList(record_defaults), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if output, idx, err = makeOutputList(op, idx, "output"); err != nil { + scope.UpdateErr("DecodeCSV", err) + return + } + return output +} + +// SerializeIteratorAttr is an optional argument to SerializeIterator. +type SerializeIteratorAttr func(optionalAttr) + +// SerializeIteratorExternalStatePolicy sets the optional external_state_policy attribute to value. +// If not specified, defaults to 0 +func SerializeIteratorExternalStatePolicy(value int64) SerializeIteratorAttr { + return func(m optionalAttr) { + m["external_state_policy"] = value + } +} + +// Converts the given `resource_handle` representing an iterator to a variant tensor. +// +// Arguments: +// resource_handle: A handle to an iterator resource. +// +// Returns A variant tensor storing the state of the iterator contained in the +// resource. +func SerializeIterator(scope *Scope, resource_handle tf.Output, optional ...SerializeIteratorAttr) (serialized tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SerializeIterator", + Input: []tf.Input{ + resource_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyCenteredRMSPropAttr is an optional argument to ResourceApplyCenteredRMSProp. +type ResourceApplyCenteredRMSPropAttr func(optionalAttr) + +// ResourceApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, mg, ms, and mom tensors is +// protected by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyCenteredRMSPropUseLocking(value bool) ResourceApplyCenteredRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the centered RMSProp algorithm. +// +// The centered RMSProp algorithm uses an estimate of the centered second moment +// (i.e., the variance) for normalization, as opposed to regular RMSProp, which +// uses the (uncentered) second moment. This often helps with training, but is +// slightly more expensive in terms of computation and memory. +// +// Note that in dense implementation of this algorithm, mg, ms, and mom will +// update even if the grad is zero, but in this sparse implementation, mg, ms, +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// mean_grad = decay * mean_grad + (1-decay) * gradient +// +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) +// +// mg <- rho * mg_{t-1} + (1-rho) * grad +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// mg: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyCenteredRMSProp(scope *Scope, var_ tf.Output, mg tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyCenteredRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyCenteredRMSProp", + Input: []tf.Input{ + var_, mg, ms, mom, lr, rho, momentum, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// UnsortedSegmentJoinAttr is an optional argument to UnsortedSegmentJoin. +type UnsortedSegmentJoinAttr func(optionalAttr) + +// UnsortedSegmentJoinSeparator sets the optional separator attribute to value. +// +// value: The separator to use when joining. +// If not specified, defaults to "" +func UnsortedSegmentJoinSeparator(value string) UnsortedSegmentJoinAttr { + return func(m optionalAttr) { + m["separator"] = value + } +} + +// Joins the elements of `inputs` based on `segment_ids`. +// +// Computes the string join along segments of a tensor. +// Given `segment_ids` with rank `N` and `data` with rank `N+M`: +// +// `output[i, k1...kM] = strings.join([data[j1...jN, k1...kM])` +// +// where the join is over all [j1...jN] such that segment_ids[j1...jN] = i. +// Strings are joined in row-major order. +// +// For example: +// +// ```python +// inputs = [['Y', 'q', 'c'], ['Y', '6', '6'], ['p', 'G', 'a']] +// output_array = string_ops.unsorted_segment_join(inputs=inputs, +// segment_ids=[1, 0, 1], +// num_segments=2, +// separator=':')) +// # output_array ==> [['Y', '6', '6'], ['Y:p', 'q:G', 'c:a']] +// +// +// inputs = ['this', 'is', 'a', 'test'] +// output_array = string_ops.unsorted_segment_join(inputs=inputs, +// segment_ids=[0, 0, 0, 0], +// num_segments=1, +// separator=':')) +// # output_array ==> ['this:is:a:test'] +// ``` +// +// Arguments: +// inputs: The input to be joined. +// segment_ids: A tensor whose shape is a prefix of data.shape. Negative segment ids are not +// supported. +// num_segments: A scalar. +func UnsortedSegmentJoin(scope *Scope, inputs tf.Output, segment_ids tf.Output, num_segments tf.Output, optional ...UnsortedSegmentJoinAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "UnsortedSegmentJoin", + Input: []tf.Input{ + inputs, segment_ids, num_segments, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LuAttr is an optional argument to Lu. +type LuAttr func(optionalAttr) + +// LuOutputIdxType sets the optional output_idx_type attribute to value. +// If not specified, defaults to DT_INT32 +func LuOutputIdxType(value tf.DataType) LuAttr { + return func(m optionalAttr) { + m["output_idx_type"] = value + } +} + +// Computes the LU decomposition of one or more square matrices. +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. +// +// The input has to be invertible. +// +// The output consists of two tensors LU and P containing the LU decomposition +// of all input submatrices `[..., :, :]`. LU encodes the lower triangular and +// upper triangular factors. +// +// For each input submatrix of shape `[M, M]`, L is a lower triangular matrix of +// shape `[M, M]` with unit diagonal whose entries correspond to the strictly lower +// triangular part of LU. U is a upper triangular matrix of shape `[M, M]` whose +// entries correspond to the upper triangular part, including the diagonal, of LU. +// +// P represents a permutation matrix encoded as a list of indices each between `0` +// and `M-1`, inclusive. If P_mat denotes the permutation matrix corresponding to +// P, then the L, U and P satisfies P_mat * input = L * U. +// +// Arguments: +// input: A tensor of shape `[..., M, M]` whose inner-most 2 dimensions form matrices of +// size `[M, M]`. +// +// Returns: +// lu: A tensor of shape `[..., M, M]` whose strictly lower triangular part denotes the +// lower triangular factor `L` with unit diagonal, and whose upper triangular part +// denotes the upper triangular factor `U`. +// p: Permutation of the rows encoded as a list of indices in `0..M-1`. Shape is +// `[..., M]`. +// @compatibility(scipy) +// Similar to `scipy.linalg.lu`, except the triangular factors `L` and `U` are +// packed into a single tensor, the permutation is applied to `input` instead of +// the right hand side and the permutation `P` is returned as a list of indices +// instead of a permutation matrix. +// @end_compatibility +func Lu(scope *Scope, input tf.Output, optional ...LuAttr) (lu tf.Output, p tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Lu", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Outputs deterministic pseudorandom random numbers from a Poisson distribution. +// +// Outputs random values from a Poisson distribution. +// +// The outputs are a deterministic function of `shape`, `seed`, and `lam`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// lam: The rate of the Poisson distribution. Shape must match the rightmost dimensions +// of `shape`. +// dtype: The type of the output. +// +// Returns Random values with specified shape. +func StatelessRandomPoisson(scope *Scope, shape tf.Output, seed tf.Output, lam tf.Output, dtype tf.DataType) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "StatelessRandomPoisson", + Input: []tf.Input{ + shape, seed, lam, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the truth value of `NOT x` element-wise. +// +// Arguments: +// x: A `Tensor` of type `bool`. +// +// Returns A `Tensor` of type `bool` with the same shape as `x`. The logical negation of `x`. +func LogicalNot(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LogicalNot", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ImageProjectiveTransformV2Attr is an optional argument to ImageProjectiveTransformV2. +type ImageProjectiveTransformV2Attr func(optionalAttr) + +// ImageProjectiveTransformV2FillMode sets the optional fill_mode attribute to value. +// +// value: Fill mode, "REFLECT", "WRAP", or "CONSTANT". +// If not specified, defaults to "CONSTANT" +func ImageProjectiveTransformV2FillMode(value string) ImageProjectiveTransformV2Attr { + return func(m optionalAttr) { + m["fill_mode"] = value + } +} + +// Applies the given transform to each of the images. +// +// If one row of `transforms` is `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps +// the *output* point `(x, y)` to a transformed *input* point +// `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where +// `k = c0 x + c1 y + 1`. If the transformed point lays outside of the input +// image, the output pixel is set to 0. +// +// Arguments: +// images: 4-D with shape `[batch, height, width, channels]`. +// transforms: 2-D Tensor, `[batch, 8]` or `[1, 8]` matrix, where each row corresponds to a 3 x 3 +// projective transformation matrix, with the last entry assumed to be 1. If there +// is one row, the same transformation will be applied to all images. +// output_shape: 1-D Tensor [new_height, new_width]. +// interpolation: Interpolation method, "NEAREST" or "BILINEAR". +// +// Returns 4-D with shape +// `[batch, new_height, new_width, channels]`. +func ImageProjectiveTransformV2(scope *Scope, images tf.Output, transforms tf.Output, output_shape tf.Output, interpolation string, optional ...ImageProjectiveTransformV2Attr) (transformed_images tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"interpolation": interpolation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ImageProjectiveTransformV2", + Input: []tf.Input{ + images, transforms, output_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes rectified linear gradients for a Relu operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding Relu operation. +// features: The features passed as input to the corresponding Relu operation, OR +// the outputs of that operation (both work equivalently). +// +// Returns `gradients * (features > 0)`. +func ReluGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReluGrad", + Input: []tf.Input{ + gradients, features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyMomentumAttr is an optional argument to ResourceApplyMomentum. +type ResourceApplyMomentumAttr func(optionalAttr) + +// ResourceApplyMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyMomentumUseLocking(value bool) ResourceApplyMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var - lr * momentum * accum, so in the end, the var you get is actually +// var - lr * momentum * accum. +// If not specified, defaults to false +func ResourceApplyMomentumUseNesterov(value bool) ResourceApplyMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update '*var' according to the momentum scheme. +// +// Set use_nesterov = True if you want to use Nesterov momentum. +// +// accum = accum * momentum + grad +// var -= lr * accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, momentum tf.Output, optional ...ResourceApplyMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// IRFFTAttr is an optional argument to IRFFT. +type IRFFTAttr func(optionalAttr) + +// IRFFTTreal sets the optional Treal attribute to value. +// If not specified, defaults to DT_FLOAT +func IRFFTTreal(value tf.DataType) IRFFTAttr { + return func(m optionalAttr) { + m["Treal"] = value + } +} + +// Inverse real-valued fast Fourier transform. +// +// Computes the inverse 1-dimensional discrete Fourier transform of a real-valued +// signal over the inner-most dimension of `input`. +// +// The inner-most dimension of `input` is assumed to be the result of `RFFT`: the +// `fft_length / 2 + 1` unique components of the DFT of a real-valued signal. If +// `fft_length` is not provided, it is computed from the size of the inner-most +// dimension of `input` (`fft_length = 2 * (inner - 1)`). If the FFT length used to +// compute `input` is odd, it should be provided since it cannot be inferred +// properly. +// +// Along the axis `IRFFT` is computed on, if `fft_length / 2 + 1` is smaller +// than the corresponding dimension of `input`, the dimension is cropped. If it is +// larger, the dimension is padded with zeros. +// +// Arguments: +// input: A complex tensor. +// fft_length: An int32 tensor of shape [1]. The FFT length. +// +// Returns A float32 tensor of the same rank as `input`. The inner-most +// dimension of `input` is replaced with the `fft_length` samples of its inverse +// 1D Fourier transform. +// +// @compatibility(numpy) +// Equivalent to np.fft.irfft +// @end_compatibility +func IRFFT(scope *Scope, input tf.Output, fft_length tf.Output, optional ...IRFFTAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "IRFFT", + Input: []tf.Input{ + input, fft_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnqueueTPUEmbeddingSparseBatchAttr is an optional argument to EnqueueTPUEmbeddingSparseBatch. +type EnqueueTPUEmbeddingSparseBatchAttr func(optionalAttr) + +// EnqueueTPUEmbeddingSparseBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. +// If not specified, defaults to -1 +func EnqueueTPUEmbeddingSparseBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingSparseBatchAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// EnqueueTPUEmbeddingSparseBatchCombiners sets the optional combiners attribute to value. +// +// value: A list of string scalars, one for each embedding table that specify +// how to normalize the embedding activations after weighted summation. +// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have +// the sum of the weights be 0 for 'mean' or the sum of the squared weights be +// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for +// all tables. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingSparseBatchCombiners(value []string) EnqueueTPUEmbeddingSparseBatchAttr { + return func(m optionalAttr) { + m["combiners"] = value + } +} + +// An op that enqueues TPUEmbedding input indices from a SparseTensor. +// +// This Op eases the porting of code that uses embedding_lookup_sparse(), +// although some Python preprocessing of the SparseTensor arguments to +// embedding_lookup_sparse() is required to produce the arguments to this Op, +// since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training +// step. +// +// The tensors at corresponding positions in the three input lists +// must have the same shape, i.e. rank 1 with dim_size() equal to the total +// number of lookups into the table described by the corresponding table_id. +// +// Arguments: +// sample_indices: A list of rank 1 Tensors specifying the training example and +// feature to which the corresponding embedding_indices and aggregation_weights +// values belong. sample_indices[i] must equal b * nf + f, where nf is the +// number of features from the corresponding table, f is in [0, nf), and +// b is in [0, batch size). +// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. +// aggregation_weights: A list of rank 1 Tensors containing per sample -- i.e. per +// (training example, feature) -- aggregation weights. +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// +// Returns the created operation. +func EnqueueTPUEmbeddingSparseBatch(scope *Scope, sample_indices []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, optional ...EnqueueTPUEmbeddingSparseBatchAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EnqueueTPUEmbeddingSparseBatch", + Input: []tf.Input{ + tf.OutputList(sample_indices), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ResourceScatterNdUpdateAttr is an optional argument to ResourceScatterNdUpdate. +type ResourceScatterNdUpdateAttr func(optionalAttr) + +// ResourceScatterNdUpdateUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdUpdateUseLocking(value bool) ResourceScatterNdUpdateAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Applies sparse `updates` to individual values or slices within a given +// +// variable according to `indices`. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. +// ``` +// +// For example, say we want to update 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that update would look like this: +// +// ```python +// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1] ,[7]]) +// updates = tf.constant([9, 10, 11, 12]) +// update = tf.scatter_nd_update(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(update) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 11, 3, 10, 9, 6, 7, 12] +// +// See `tf.scatter_nd` for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of updated +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdUpdateAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdUpdate", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes square root of x element-wise. +// +// I.e., \\(y = \sqrt{x} = x^{1/2}\\). +func Sqrt(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Sqrt", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor. +// +// This operation folds the padded areas of `input` by `MirrorPad` according to the +// `paddings` you specify. `paddings` must be the same as `paddings` argument +// given to the corresponding `MirrorPad` op. +// +// The folded size of each dimension D of the output is: +// +// `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)` +// +// For example: +// +// ``` +// # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. +// # 'paddings' is [[0, 1]], [0, 1]]. +// # 'mode' is SYMMETRIC. +// # rank of 't' is 2. +// pad(t, paddings) ==> [[ 1, 5] +// [11, 28]] +// ``` +// +// Arguments: +// input: The input tensor to be folded. +// paddings: A two-column matrix specifying the padding sizes. The number of +// rows must be the same as the rank of `input`. +// mode: The mode used in the `MirrorPad` op. +// +// Returns The folded tensor. +func MirrorPadGrad(scope *Scope, input tf.Output, paddings tf.Output, mode string) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"mode": mode} + opspec := tf.OpSpec{ + Type: "MirrorPadGrad", + Input: []tf.Input{ + input, paddings, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Produces the max pool of the input tensor for quantized types. +// +// Arguments: +// input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over. +// min_input: The float value that the lowest quantized input value represents. +// max_input: The float value that the highest quantized input value represents. +// ksize: The size of the window for each dimension of the input tensor. +// The length must be 4 to match the number of dimensions of the input. +// strides: The stride of the sliding window for each dimension of the input +// tensor. The length must be 4 to match the number of dimensions of the input. +// padding: The type of padding algorithm to use. +// +// Returns: +// output +// min_output: The float value that the lowest quantized output value represents. +// max_output: The float value that the highest quantized output value represents. +func QuantizedMaxPool(scope *Scope, input tf.Output, min_input tf.Output, max_input tf.Output, ksize []int64, strides []int64, padding string) (output tf.Output, min_output tf.Output, max_output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + opspec := tf.OpSpec{ + Type: "QuantizedMaxPool", + Input: []tf.Input{ + input, min_input, max_input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad. +type ResourceApplyAdagradAttr func(optionalAttr) + +// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyAdagradUpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceApplyAdagradUpdateSlots(value bool) ResourceApplyAdagradAttr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update '*var' according to the adagrad scheme. +// +// accum += grad * grad +// var -= lr * grad * (1 / sqrt(accum)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagrad", + Input: []tf.Input{ + var_, accum, lr, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// The gradient of SparseFillEmptyRows. +// +// Takes vectors reverse_index_map, shaped `[N]`, and grad_values, +// shaped `[N_full]`, where `N_full >= N` and copies data into either +// `d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and +// `d_default_value` is a scalar. +// +// d_values[j] = grad_values[reverse_index_map[j]] +// d_default_value = sum_{k : 0 .. N_full - 1} ( +// grad_values[k] * 1{k not in reverse_index_map}) +// +// Arguments: +// reverse_index_map: 1-D. The reverse index map from SparseFillEmptyRows. +// grad_values: 1-D. The gradients from backprop. +// +// Returns: +// d_values: 1-D. The backprop into values. +// d_default_value: 0-D. The backprop into default_value. +func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_values tf.Output) (d_values tf.Output, d_default_value tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseFillEmptyRowsGrad", + Input: []tf.Input{ + reverse_index_map, grad_values, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// MaxPool3DGradAttr is an optional argument to MaxPool3DGrad. +type MaxPool3DGradAttr func(optionalAttr) + +// MaxPool3DGradDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func MaxPool3DGradDataFormat(value string) MaxPool3DGradAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes gradients of 3D max pooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +func MaxPool3DGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DGradAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPool3DGrad", + Input: []tf.Input{ + orig_input, orig_output, grad, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyRMSPropAttr is an optional argument to ResourceApplyRMSProp. +type ResourceApplyRMSPropAttr func(optionalAttr) + +// ResourceApplyRMSPropUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, ms, and mom tensors is protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyRMSPropUseLocking(value bool) ResourceApplyRMSPropAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the RMSProp algorithm. +// +// Note that in dense implementation of this algorithm, ms and mom will +// update even if the grad is zero, but in this sparse implementation, ms +// and mom will not update in iterations during which the grad is zero. +// +// mean_square = decay * mean_square + (1-decay) * gradient ** 2 +// Delta = learning_rate * gradient / sqrt(mean_square + epsilon) +// +// ms <- rho * ms_{t-1} + (1-rho) * grad * grad +// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) +// var <- var - mom +// +// Arguments: +// var_: Should be from a Variable(). +// ms: Should be from a Variable(). +// mom: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// rho: Decay rate. Must be a scalar. +// +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyRMSProp(scope *Scope, var_ tf.Output, ms tf.Output, mom tf.Output, lr tf.Output, rho tf.Output, momentum tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyRMSPropAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyRMSProp", + Input: []tf.Input{ + var_, ms, mom, lr, rho, momentum, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Reshapes a SparseTensor to represent values in a new dense shape. +// +// This operation has the same semantics as reshape on the represented dense +// tensor. The `input_indices` are recomputed based on the requested `new_shape`. +// +// If one component of `new_shape` is the special value -1, the size of that +// dimension is computed so that the total dense size remains constant. At +// most one component of `new_shape` can be -1. The number of dense elements +// implied by `new_shape` must be the same as the number of dense elements +// originally implied by `input_shape`. +// +// Reshaping does not affect the order of values in the SparseTensor. +// +// If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape` +// has length `R_out`, then `input_indices` has shape `[N, R_in]`, +// `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and +// `output_shape` has length `R_out`. +// +// Arguments: +// input_indices: 2-D. `N x R_in` matrix with the indices of non-empty values in a +// SparseTensor. +// input_shape: 1-D. `R_in` vector with the input SparseTensor's dense shape. +// new_shape: 1-D. `R_out` vector with the requested new dense shape. +// +// Returns: +// output_indices: 2-D. `N x R_out` matrix with the updated indices of non-empty +// values in the output SparseTensor. +// output_shape: 1-D. `R_out` vector with the full dense shape of the output +// SparseTensor. This is the same as `new_shape` but with any -1 dimensions +// filled in. +func SparseReshape(scope *Scope, input_indices tf.Output, input_shape tf.Output, new_shape tf.Output) (output_indices tf.Output, output_shape tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseReshape", + Input: []tf.Input{ + input_indices, input_shape, new_shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Elementwise computes the bitwise left-shift of `x` and `y`. +// +// If `y` is negative, or greater than or equal to the width of `x` in bits the +// result is implementation defined. +// +// Example: +// +// ```python +// import tensorflow as tf +// from tensorflow.python.ops import bitwise_ops +// import numpy as np +// dtype_list = [tf.int8, tf.int16, tf.int32, tf.int64] +// +// for dtype in dtype_list: +// lhs = tf.constant([-1, -5, -3, -14], dtype=dtype) +// rhs = tf.constant([5, 0, 7, 11], dtype=dtype) +// +// left_shift_result = bitwise_ops.left_shift(lhs, rhs) +// +// print(left_shift_result) +// +// # This will print: +// # tf.Tensor([ -32 -5 -128 0], shape=(4,), dtype=int8) +// # tf.Tensor([ -32 -5 -384 -28672], shape=(4,), dtype=int16) +// # tf.Tensor([ -32 -5 -384 -28672], shape=(4,), dtype=int32) +// # tf.Tensor([ -32 -5 -384 -28672], shape=(4,), dtype=int64) +// +// lhs = np.array([-2, 64, 101, 32], dtype=np.int8) +// rhs = np.array([-1, -5, -3, -14], dtype=np.int8) +// bitwise_ops.left_shift(lhs, rhs) +// # +// ``` +// +func LeftShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LeftShift", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Generates a feature cross from a list of tensors, and returns it as a +// RaggedTensor. See `tf.ragged.cross` for more details. +// +// Arguments: +// ragged_values: The values tensor for each RaggedTensor input. +// ragged_row_splits: The row_splits tensor for each RaggedTensor input. +// sparse_indices: The indices tensor for each SparseTensor input. +// sparse_values: The values tensor for each SparseTensor input. +// sparse_shape: The dense_shape tensor for each SparseTensor input. +// dense_inputs: The tf.Tensor inputs. +// input_order: String specifying the tensor type for each input. The `i`th character in +// this string specifies the type of the `i`th input, and is one of: 'R' (ragged), +// 'D' (dense), or 'S' (sparse). This attr is used to ensure that the crossed +// values are combined in the order of the inputs from the call to tf.ragged.cross. +// +// +// +// +// +// +// Returns: +// output_values: The `values` for the returned `RaggedTensor`. +// output_row_splits: The `row_splits` for the returned `RaggedTensor`. +func RaggedCross(scope *Scope, ragged_values []tf.Output, ragged_row_splits []tf.Output, sparse_indices []tf.Output, sparse_values []tf.Output, sparse_shape []tf.Output, dense_inputs []tf.Output, input_order string, hashed_output bool, num_buckets int64, hash_key int64, out_values_type tf.DataType, out_row_splits_type tf.DataType) (output_values tf.Output, output_row_splits tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"input_order": input_order, "hashed_output": hashed_output, "num_buckets": num_buckets, "hash_key": hash_key, "out_values_type": out_values_type, "out_row_splits_type": out_row_splits_type} + opspec := tf.OpSpec{ + Type: "RaggedCross", + Input: []tf.Input{ + tf.OutputList(ragged_values), tf.OutputList(ragged_row_splits), tf.OutputList(sparse_indices), tf.OutputList(sparse_values), tf.OutputList(sparse_shape), tf.OutputList(dense_inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Output a fact about factorials. +func Fact(scope *Scope) (fact tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Fact", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softmax cross entropy cost and gradients to backpropagate. +// +// Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept +// a matrix of label probabilities, but rather a single label per row +// of features. This label is considered to have probability 1.0 for the +// given row. +// +// Inputs are the logits, not probabilities. +// +// Arguments: +// features: batch_size x num_classes matrix +// labels: batch_size vector with values in [0, num_classes). +// This is the label for the given minibatch entry. +// +// Returns: +// loss: Per example loss (batch_size vector). +// backprop: backpropagated gradients (batch_size x num_classes matrix). +func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSoftmaxCrossEntropyWithLogits", + Input: []tf.Input{ + features, labels, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Worker heartbeat op. +// +// Heartbeats may be sent periodically to indicate the coordinator is still active, +// to retrieve the current worker status and to expedite shutdown when necessary. +// +// Arguments: +// request: A string tensor containing a serialized WorkerHeartbeatRequest +// +// Returns A string tensor containing a serialized WorkerHeartbeatResponse +func WorkerHeartbeat(scope *Scope, request tf.Output) (response tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "WorkerHeartbeat", + Input: []tf.Input{ + request, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyProximalGradientDescentAttr is an optional argument to ResourceApplyProximalGradientDescent. +type ResourceApplyProximalGradientDescentAttr func(optionalAttr) + +// ResourceApplyProximalGradientDescentUseLocking sets the optional use_locking attribute to value. +// +// value: If True, the subtraction will be protected by a lock; +// otherwise the behavior is undefined, but may exhibit less contention. +// If not specified, defaults to false +func ResourceApplyProximalGradientDescentUseLocking(value bool) ResourceApplyProximalGradientDescentAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' as FOBOS algorithm with fixed learning rate. +// +// prox_v = var - alpha * delta +// var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} +// +// Arguments: +// var_: Should be from a Variable(). +// alpha: Scaling factor. Must be a scalar. +// l1: L1 regularization. Must be a scalar. +// l2: L2 regularization. Must be a scalar. +// delta: The change. +// +// Returns the created operation. +func ResourceApplyProximalGradientDescent(scope *Scope, var_ tf.Output, alpha tf.Output, l1 tf.Output, l2 tf.Output, delta tf.Output, optional ...ResourceApplyProximalGradientDescentAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyProximalGradientDescent", + Input: []tf.Input{ + var_, alpha, l1, l2, delta, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// RandomUniformAttr is an optional argument to RandomUniform. +type RandomUniformAttr func(optionalAttr) + +// RandomUniformSeed sets the optional seed attribute to value. +// +// value: If either `seed` or `seed2` are set to be non-zero, the random number +// generator is seeded by the given seed. Otherwise, it is seeded by a +// random seed. +// If not specified, defaults to 0 +func RandomUniformSeed(value int64) RandomUniformAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// RandomUniformSeed2 sets the optional seed2 attribute to value. +// +// value: A second seed to avoid seed collision. +// If not specified, defaults to 0 +func RandomUniformSeed2(value int64) RandomUniformAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Outputs random values from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// Arguments: +// shape: The shape of the output tensor. +// dtype: The type of the output. +// +// Returns A tensor of the specified shape filled with uniform random values. +func RandomUniform(scope *Scope, shape tf.Output, dtype tf.DataType, optional ...RandomUniformAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RandomUniform", + Input: []tf.Input{ + shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug. +type RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve Adadelta embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the Adadelta optimization algorithm. +// accumulators: Parameter accumulators updated by the Adadelta optimization algorithm. +// updates: Parameter updates updated by the Adadelta optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the Adadelta optimization algorithm. +func RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, updates tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Retrieves the tree ensemble resource stamp token, number of trees and growing statistics. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble. +// +// Returns: +// stamp_token: Stamp token of the tree ensemble resource. +// num_trees: The number of trees in the tree ensemble resource. +// num_finalized_trees: The number of trees that were finished successfully. +// num_attempted_layers: The number of layers we attempted to build (but not necessarily succeeded). +// last_layer_nodes_range: Rank size 2 tensor that contains start and end ids of the nodes in the latest +// layer. +func BoostedTreesGetEnsembleStates(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, num_trees tf.Output, num_finalized_trees tf.Output, num_attempted_layers tf.Output, last_layer_nodes_range tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesGetEnsembleStates", + Input: []tf.Input{ + tree_ensemble_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + +// ResourceScatterNdAddAttr is an optional argument to ResourceScatterNdAdd. +type ResourceScatterNdAddAttr func(optionalAttr) + +// ResourceScatterNdAddUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdAddUseLocking(value bool) ResourceScatterNdAddAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Applies sparse addition to individual values or slices in a Variable. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] +// ``` +// +// For example, say we want to add 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that addition would look like this: +// +// ```python +// ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) +// indices = tf.constant([[4], [3], [1], [7]]) +// updates = tf.constant([9, 10, 11, 12]) +// add = tf.scatter_nd_add(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(add) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 13, 3, 14, 14, 6, 7, 20] +// +// See `tf.scatter_nd` for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdAdd(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdAddAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdAdd", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// CropAndResizeGradImageAttr is an optional argument to CropAndResizeGradImage. +type CropAndResizeGradImageAttr func(optionalAttr) + +// CropAndResizeGradImageMethod sets the optional method attribute to value. +// +// value: A string specifying the interpolation method. Only 'bilinear' is +// supported for now. +// If not specified, defaults to "bilinear" +func CropAndResizeGradImageMethod(value string) CropAndResizeGradImageAttr { + return func(m optionalAttr) { + m["method"] = value + } +} + +// Computes the gradient of the crop_and_resize op wrt the input image tensor. +// +// Arguments: +// grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]` +// containing the original image size. Both `image_height` and `image_width` need +// to be positive. +// +// +// Returns A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +func CropAndResizeGradImage(scope *Scope, grads tf.Output, boxes tf.Output, box_ind tf.Output, image_size tf.Output, T tf.DataType, optional ...CropAndResizeGradImageAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"T": T} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CropAndResizeGradImage", + Input: []tf.Input{ + grads, boxes, box_ind, image_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// OutfeedDequeueAttr is an optional argument to OutfeedDequeue. +type OutfeedDequeueAttr func(optionalAttr) + +// OutfeedDequeueDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func OutfeedDequeueDeviceOrdinal(value int64) OutfeedDequeueAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// Retrieves a single tensor from the computation outfeed. +// +// This operation will block indefinitely until data is available. +// +// Arguments: +// dtype: The type of elements in the tensor. +// shape: The shape of the tensor. +// +// Returns A tensor that will be read from the device outfeed. +func OutfeedDequeue(scope *Scope, dtype tf.DataType, shape tf.Shape, optional ...OutfeedDequeueAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "OutfeedDequeue", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An Op to sum inputs across replicated TPU instances. +// +// Each instance supplies its own input. +// +// For example, suppose there are 8 TPU instances: `[A, B, C, D, E, F, G, H]`. +// Passing group_assignment=`[[0,2,4,6],[1,3,5,7]]` sets `A, C, E, G` as group 0, +// and `B, D, F, H` as group 1. Thus we get the outputs: +// `[A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]`. +// +// Arguments: +// input: The local input to the sum. +// group_assignment: An int32 tensor with shape +// [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the +// replica ids in the ith subgroup. +// +// Returns The sum of all the distributed inputs. +func CrossReplicaSum(scope *Scope, input tf.Output, group_assignment tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "CrossReplicaSum", + Input: []tf.Input{ + input, group_assignment, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnqueueTPUEmbeddingRaggedTensorBatchAttr is an optional argument to EnqueueTPUEmbeddingRaggedTensorBatch. +type EnqueueTPUEmbeddingRaggedTensorBatchAttr func(optionalAttr) + +// EnqueueTPUEmbeddingRaggedTensorBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. +// If not specified, defaults to -1 +func EnqueueTPUEmbeddingRaggedTensorBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingRaggedTensorBatchAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// EnqueueTPUEmbeddingRaggedTensorBatchCombiners sets the optional combiners attribute to value. +// +// value: A list of string scalars, one for each embedding table that specify +// how to normalize the embedding activations after weighted summation. +// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have +// the sum of the weights be 0 for 'mean' or the sum of the squared weights be +// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for +// all tables. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingRaggedTensorBatchCombiners(value []string) EnqueueTPUEmbeddingRaggedTensorBatchAttr { + return func(m optionalAttr) { + m["combiners"] = value + } +} + +// EnqueueTPUEmbeddingRaggedTensorBatchMaxSequenceLengths sets the optional max_sequence_lengths attribute to value. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingRaggedTensorBatchMaxSequenceLengths(value []int64) EnqueueTPUEmbeddingRaggedTensorBatchAttr { + return func(m optionalAttr) { + m["max_sequence_lengths"] = value + } +} + +// Eases the porting of code that uses tf.nn.embedding_lookup(). +// +// sample_splits[i], embedding_indices[i] and aggregation_weights[i] correspond +// to the ith feature. table_ids[i] indicates which embedding table to look up ith +// feature. +// +// The tensors at corresponding positions in two of the input lists, +// embedding_indices and aggregation_weights, must have the same shape, i.e. rank 1 +// with dim_size() equal to the total number of lookups into the table described by +// the corresponding feature. +// +// Arguments: +// sample_splits: A list of rank 1 Tensors specifying the break points for splitting +// embedding_indices and aggregation_weights into rows. +// It corresponds to ids.row_splits in embedding_lookup(), when ids is a +// RaggedTensor. +// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. +// It corresponds to ids.values in embedding_lookup(), when ids is a RaggedTensor. +// aggregation_weights: A list of rank 1 Tensors containing per training example +// aggregation weights. It corresponds to the values field of a RaggedTensor +// with the same row_splits as ids in embedding_lookup(), when ids is a +// RaggedTensor. +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// table_ids: A list of integers specifying the identifier of the embedding table +// (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the +// corresponding input. The ith input is looked up using table_ids[i]. The size +// of the table_ids list must be equal to that of sample_indices, +// embedding_indices and aggregation_weights. +// +// Returns the created operation. +func EnqueueTPUEmbeddingRaggedTensorBatch(scope *Scope, sample_splits []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, table_ids []int64, optional ...EnqueueTPUEmbeddingRaggedTensorBatchAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"table_ids": table_ids} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EnqueueTPUEmbeddingRaggedTensorBatch", + Input: []tf.Input{ + tf.OutputList(sample_splits), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars. +type FakeQuantWithMinMaxVarsAttr func(optionalAttr) + +// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor of type float via global float scalars +// +// Fake-quantize the `inputs` tensor of type float via global float scalars +// `min` and `max` to `outputs` tensor of same shape as `inputs`. +// +// Attributes +// +// * `[min; max]` define the clamping range for the `inputs` data. +// * `inputs` values are quantized into the quantization range ( +// `[0; 2^num_bits - 1]` when `narrow_range` is false and `[1; 2^num_bits - 1]` +// when it is true) and then de-quantized and output as floats in `[min; max]` +// interval. +// * `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// +// Before quantization, `min` and `max` values are adjusted with the following +// logic. +// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values, +// the behavior can be unexpected: +// +// * If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`. +// * If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`. +// * If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `, +// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`. +// +// This operation has a gradient and thus allows for training `min` and `max` +// values. +func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxVars", + Input: []tf.Input{ + inputs, min, max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Enqueue multiple Tensor values on the computation outfeed. +// +// Arguments: +// inputs: A list of tensors that will be inserted into the outfeed queue as an +// XLA tuple. +// +// Returns the created operation. +func OutfeedEnqueueTuple(scope *Scope, inputs []tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OutfeedEnqueueTuple", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingADAMParametersAttr is an optional argument to LoadTPUEmbeddingADAMParameters. +type LoadTPUEmbeddingADAMParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingADAMParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingADAMParametersTableId(value int64) LoadTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingADAMParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingADAMParametersTableName(value string) LoadTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingADAMParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingADAMParametersConfig(value string) LoadTPUEmbeddingADAMParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load ADAM embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the ADAM optimization algorithm. +// momenta: Value of momenta used in the ADAM optimization algorithm. +// velocities: Value of velocities used in the ADAM optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingADAMParameters(scope *Scope, parameters tf.Output, momenta tf.Output, velocities tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingADAMParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingADAMParameters", + Input: []tf.Input{ + parameters, momenta, velocities, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Records the latency of producing `input_dataset` elements in a StatsAggregator. +func LatencyStatsDataset(scope *Scope, input_dataset tf.Output, tag tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "LatencyStatsDataset", + Input: []tf.Input{ + input_dataset, tag, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes the power of one value to another. +// +// Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for +// corresponding elements in `x` and `y`. For example: +// +// ``` +// # tensor 'x' is [[2, 2]], [3, 3]] +// # tensor 'y' is [[8, 16], [2, 3]] +// tf.pow(x, y) ==> [[256, 65536], [9, 27]] +// ``` +func Pow(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Pow", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Element-wise multiplication of a sparse matrix with a dense tensor. +// +// Returns a sparse matrix. +// +// The dense tensor `b` may be either a scalar; otherwise `a` must be a rank-3 +// `SparseMatrix`; in this case `b` must be shaped `[batch_size, 1, 1]` and the +// multiply operation broadcasts. +// +// **NOTE** even if `b` is zero, the sparsity structure of the output does not +// change. +// +// Arguments: +// a: A CSRSparseMatrix. +// b: A dense tensor. +// +// Returns A dense output tensor. +func SparseMatrixMul(scope *Scope, a tf.Output, b tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseMatrixMul", + Input: []tf.Input{ + a, b, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the element-wise sum of a list of tensors. +// +// `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not +// wait for all of its inputs to be ready before beginning to sum. This can +// save memory if inputs are ready at different times, since minimum temporary +// storage is proportional to the output size rather than the inputs size. +// +// Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable. +// +// Returns a `Tensor` of same shape and type as the elements of `inputs`. +// +// Arguments: +// inputs: A list of `Tensor` objects, each with same shape and type. +// shape: Shape of elements of `inputs`. +func AccumulateNV2(scope *Scope, inputs []tf.Output, shape tf.Shape) (sum tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "AccumulateNV2", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// An op enabling differentiation of TPU Embeddings. +// +// This op simply returns its first input, which is assumed to have been sliced +// from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of +// this op, and its first argument being a trainable Variable, enables automatic +// differentiation of graphs containing embeddings via the TPU Embedding Python +// libraries. +// +// Arguments: +// embedding_variable: A trainable variable, enabling optimizers to find this op. +// sliced_activations: The embedding activations Tensor to return. +// table_id: The id of the table in the embedding layer configuration from which +// these activations were computed. +// lookup_id: Identifier of the set of embedding indices which produced these +// activations. +func TPUEmbeddingActivations(scope *Scope, embedding_variable tf.Output, sliced_activations tf.Output, table_id int64, lookup_id int64) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"table_id": table_id, "lookup_id": lookup_id} + opspec := tf.OpSpec{ + Type: "TPUEmbeddingActivations", + Input: []tf.Input{ + embedding_variable, sliced_activations, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// QuantizeAndDequantizeV3Attr is an optional argument to QuantizeAndDequantizeV3. +type QuantizeAndDequantizeV3Attr func(optionalAttr) + +// QuantizeAndDequantizeV3SignedInput sets the optional signed_input attribute to value. +// If not specified, defaults to true +func QuantizeAndDequantizeV3SignedInput(value bool) QuantizeAndDequantizeV3Attr { + return func(m optionalAttr) { + m["signed_input"] = value + } +} + +// QuantizeAndDequantizeV3RangeGiven sets the optional range_given attribute to value. +// If not specified, defaults to true +func QuantizeAndDequantizeV3RangeGiven(value bool) QuantizeAndDequantizeV3Attr { + return func(m optionalAttr) { + m["range_given"] = value + } +} + +// QuantizeAndDequantizeV3NarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func QuantizeAndDequantizeV3NarrowRange(value bool) QuantizeAndDequantizeV3Attr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// QuantizeAndDequantizeV3Axis sets the optional axis attribute to value. +// If not specified, defaults to -1 +func QuantizeAndDequantizeV3Axis(value int64) QuantizeAndDequantizeV3Attr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Quantizes then dequantizes a tensor. +// +// This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a +// tensor, so its value can change during training. +func QuantizeAndDequantizeV3(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, num_bits tf.Output, optional ...QuantizeAndDequantizeV3Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizeAndDequantizeV3", + Input: []tf.Input{ + input, input_min, input_max, num_bits, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns x * y element-wise. +// +// *NOTE*: `Multiply` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Mul(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Mul", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Computes softplus gradients for a softplus operation. +// +// Arguments: +// gradients: The backpropagated gradients to the corresponding softplus operation. +// features: The features passed as input to the corresponding softplus operation. +// +// Returns The gradients: `gradients / (1 + exp(-features))`. +func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backprops tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SoftplusGrad", + Input: []tf.Input{ + gradients, features, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the item in the list with the given index. +// +// input_handle: the list +// index: the position in the list from which an element will be retrieved +// item: the element at that position +// +// +func TensorListGetItem(scope *Scope, input_handle tf.Output, index tf.Output, element_shape tf.Output, element_dtype tf.DataType) (item tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "TensorListGetItem", + Input: []tf.Input{ + input_handle, index, element_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug. +type RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve proximal Adagrad embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the proximal Adagrad optimization algorithm. +// accumulators: Parameter accumulators updated by the proximal Adagrad optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the proximal Adagrad optimization algorithm. +func RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr) (parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the value stored in an Optional variant or raises an error if none exists. +func OptionalGetValue(scope *Scope, optional tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (components []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "OptionalGetValue", + Input: []tf.Input{ + optional, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if components, idx, err = makeOutputList(op, idx, "components"); err != nil { + scope.UpdateErr("OptionalGetValue", err) + return + } + return components +} + +// Determine the script codes of a given tensor of Unicode integer code points. +// +// This operation converts Unicode code points to script codes corresponding to +// each code point. Script codes correspond to International Components for +// Unicode (ICU) UScriptCode values. See http://icu-project.org/apiref/icu4c/uscript_8h.html. +// Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints. Output shape will +// match input shape. +// +// Examples: +// +// >>> tf.strings.unicode_script([1, 31, 38]) +// +// +// Arguments: +// input: A Tensor of int32 Unicode code points. +// +// Returns A Tensor of int32 script codes corresponding to each input code point. +func UnicodeScript(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "UnicodeScript", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CropAndResizeAttr is an optional argument to CropAndResize. +type CropAndResizeAttr func(optionalAttr) + +// CropAndResizeMethod sets the optional method attribute to value. +// +// value: A string specifying the sampling method for resizing. It can be either +// `"bilinear"` or `"nearest"` and default to `"bilinear"`. Currently two sampling +// methods are supported: Bilinear and Nearest Neighbor. +// If not specified, defaults to "bilinear" +func CropAndResizeMethod(value string) CropAndResizeAttr { + return func(m optionalAttr) { + m["method"] = value + } +} + +// CropAndResizeExtrapolationValue sets the optional extrapolation_value attribute to value. +// +// value: Value used for extrapolation, when applicable. +// If not specified, defaults to 0 +func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr { + return func(m optionalAttr) { + m["extrapolation_value"] = value + } +} + +// Extracts crops from the input image tensor and resizes them. +// +// Extracts crops from the input image tensor and resizes them using bilinear +// sampling or nearest neighbor sampling (possibly with aspect ratio change) to a +// common output size specified by `crop_size`. This is more general than the +// `crop_to_bounding_box` op which extracts a fixed size slice from the input image +// and does not allow resizing or aspect ratio change. +// +// Returns a tensor with `crops` from the input `image` at positions defined at the +// bounding box locations in `boxes`. The cropped boxes are all resized (with +// bilinear or nearest neighbor interpolation) to a fixed +// `size = [crop_height, crop_width]`. The result is a 4-D tensor +// `[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned. +// In particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical +// results to using `tf.image.resize_bilinear()` or +// `tf.image.resize_nearest_neighbor()`(depends on the `method` argument) with +// `align_corners=True`. +// +// Arguments: +// image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. +// Both `image_height` and `image_width` need to be positive. +// boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor +// specifies the coordinates of a box in the `box_ind[i]` image and is specified +// in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of +// `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the +// `[0, 1]` interval of normalized image height is mapped to +// `[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in +// which case the sampled crop is an up-down flipped version of the original +// image. The width dimension is treated similarly. Normalized coordinates +// outside the `[0, 1]` range are allowed, in which case we use +// `extrapolation_value` to extrapolate the input image values. +// box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. +// The value of `box_ind[i]` specifies the image that the `i`-th box refers to. +// crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All +// cropped image patches are resized to this size. The aspect ratio of the image +// content is not preserved. Both `crop_height` and `crop_width` need to be +// positive. +// +// Returns A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. +func CropAndResize(scope *Scope, image tf.Output, boxes tf.Output, box_ind tf.Output, crop_size tf.Output, optional ...CropAndResizeAttr) (crops tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CropAndResize", + Input: []tf.Input{ + image, boxes, box_ind, crop_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// DepthwiseConv2dNativeBackpropFilterAttr is an optional argument to DepthwiseConv2dNativeBackpropFilter. +type DepthwiseConv2dNativeBackpropFilterAttr func(optionalAttr) + +// DepthwiseConv2dNativeBackpropFilterExplicitPaddings sets the optional explicit_paddings attribute to value. +// If not specified, defaults to <> +func DepthwiseConv2dNativeBackpropFilterExplicitPaddings(value []int64) DepthwiseConv2dNativeBackpropFilterAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// DepthwiseConv2dNativeBackpropFilterDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2dNativeBackpropFilterAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeBackpropFilterDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes the gradients of depthwise convolution with respect to the filter. +// +// Arguments: +// input: 4-D with shape based on `data_format`. For example, if +// `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height, +// in_width, in_channels]` tensor. +// filter_sizes: An integer vector representing the tensor shape of `filter`, +// where `filter` is a 4-D +// `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor. +// out_backprop: 4-D with shape based on `data_format`. +// For example, if `data_format` is 'NHWC' then +// out_backprop shape is `[batch, out_height, out_width, out_channels]`. +// Gradients w.r.t. the output of the convolution. +// strides: The stride of the sliding window for each dimension of the input +// of the convolution. +// padding: The type of padding algorithm to use. +// +// Returns 4-D with shape +// `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. +// the `filter` input of the convolution. +func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_sizes tf.Output, out_backprop tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeBackpropFilterAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthwiseConv2dNativeBackpropFilter", + Input: []tf.Input{ + input, filter_sizes, out_backprop, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that zips together `input_datasets`. +// +// The elements of the resulting dataset are created by zipping corresponding +// elements from each of the input datasets. +// +// The size of the resulting dataset will match the size of the smallest input +// dataset, and no error will be raised if input datasets have different sizes. +// +// Arguments: +// input_datasets: List of `N` variant Tensors representing datasets to be zipped together. +// +// +func ZipDataset(scope *Scope, input_datasets []tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "ZipDataset", + Input: []tf.Input{ + tf.OutputList(input_datasets), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Rounds the values of a tensor to the nearest integer, element-wise. +// +// Rounds half to even. Also known as bankers rounding. If you want to round +// according to the current system rounding mode use std::cint. +func Round(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Round", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a tree ensemble model and returns a handle to it. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble resource to be created. +// stamp_token: Token to use as the initial value of the resource stamp. +// tree_ensemble_serialized: Serialized proto of the tree ensemble. +// +// Returns the created operation. +func BoostedTreesCreateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, stamp_token tf.Output, tree_ensemble_serialized tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCreateEnsemble", + Input: []tf.Input{ + tree_ensemble_handle, stamp_token, tree_ensemble_serialized, + }, + } + return scope.AddOperation(opspec) +} + +// Calculates the softmax of a CSRSparseMatrix. +// +// Calculate the softmax of the innermost dimensions of a SparseMatrix. +// +// Missing values are treated as `-inf` (i.e., logits of zero probability); and +// the output has the same sparsity structure as the input (though missing values +// in the output may now be treated as having probability zero). +// +// Arguments: +// logits: A CSRSparseMatrix. +// +// +// Returns A CSRSparseMatrix. +func SparseMatrixSoftmax(scope *Scope, logits tf.Output, type_ tf.DataType) (softmax tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "SparseMatrixSoftmax", + Input: []tf.Input{ + logits, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RestoreAttr is an optional argument to Restore. +type RestoreAttr func(optionalAttr) + +// RestorePreferredShard sets the optional preferred_shard attribute to value. +// +// value: Index of file to open first if multiple files match +// `file_pattern`. +// If not specified, defaults to -1 +func RestorePreferredShard(value int64) RestoreAttr { + return func(m optionalAttr) { + m["preferred_shard"] = value + } +} + +// Restores a tensor from checkpoint files. +// +// Reads a tensor stored in one or several files. If there are several files (for +// instance because a tensor was saved as slices), `file_pattern` may contain +// wildcard symbols (`*` and `?`) in the filename portion only, not in the +// directory portion. +// +// If a `file_pattern` matches several files, `preferred_shard` can be used to hint +// in which file the requested tensor is likely to be found. This op will first +// open the file at index `preferred_shard` in the list of matching files and try +// to restore tensors from that file. Only if some tensors or tensor slices are +// not found in that first file, then the Op opens all the files. Setting +// `preferred_shard` to match the value passed as the `shard` input +// of a matching `Save` Op may speed up Restore. This attribute only affects +// performance, not correctness. The default value -1 means files are processed in +// order. +// +// See also `RestoreSlice`. +// +// Arguments: +// file_pattern: Must have a single element. The pattern of the files from +// which we read the tensor. +// tensor_name: Must have a single element. The name of the tensor to be +// restored. +// dt: The type of the tensor to be restored. +// +// Returns The restored tensor. +func Restore(scope *Scope, file_pattern tf.Output, tensor_name tf.Output, dt tf.DataType, optional ...RestoreAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dt": dt} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Restore", + Input: []tf.Input{ + file_pattern, tensor_name, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the next record (key, value pair) produced by a Reader. +// +// Will dequeue from the input queue if necessary (e.g. when the +// Reader needs to start reading from a new file since it has finished +// with the previous file). +// +// Arguments: +// reader_handle: Handle to a Reader. +// queue_handle: Handle to a Queue, with string work items. +// +// Returns: +// key: A scalar. +// value: A scalar. +func ReaderReadV2(scope *Scope, reader_handle tf.Output, queue_handle tf.Output) (key tf.Output, value tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ReaderReadV2", + Input: []tf.Input{ + reader_handle, queue_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// CumprodAttr is an optional argument to Cumprod. +type CumprodAttr func(optionalAttr) + +// CumprodExclusive sets the optional exclusive attribute to value. +// +// value: If `True`, perform exclusive cumprod. +// If not specified, defaults to false +func CumprodExclusive(value bool) CumprodAttr { + return func(m optionalAttr) { + m["exclusive"] = value + } +} + +// CumprodReverse sets the optional reverse attribute to value. +// +// value: A `bool` (default: False). +// If not specified, defaults to false +func CumprodReverse(value bool) CumprodAttr { + return func(m optionalAttr) { + m["reverse"] = value + } +} + +// Compute the cumulative product of the tensor `x` along `axis`. +// +// By default, this op performs an inclusive cumprod, which means that the first +// element of the input is identical to the first element of the output: +// +// ```python +// tf.cumprod([a, b, c]) # => [a, a * b, a * b * c] +// ``` +// +// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is +// performed instead: +// +// ```python +// tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b] +// ``` +// +// By setting the `reverse` kwarg to `True`, the cumprod is performed in the +// opposite direction: +// +// ```python +// tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c] +// ``` +// +// This is more efficient than using separate `tf.reverse` ops. +// +// The `reverse` and `exclusive` kwargs can also be combined: +// +// ```python +// tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] +// ``` +// +// Arguments: +// x: A `Tensor`. Must be one of the following types: `float32`, `float64`, +// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, +// `complex128`, `qint8`, `quint8`, `qint32`, `half`. +// axis: A `Tensor` of type `int32` (default: 0). Must be in the range +// `[-rank(x), rank(x))`. +func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Cumprod", + Input: []tf.Input{ + x, axis, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug. +type LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load SGD embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the stochastic gradient descent optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Adadelta optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug(scope *Scope, parameters tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug", + Input: []tf.Input{ + parameters, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// FakeQuantWithMinMaxArgsAttr is an optional argument to FakeQuantWithMinMaxArgs. +type FakeQuantWithMinMaxArgsAttr func(optionalAttr) + +// FakeQuantWithMinMaxArgsMin sets the optional min attribute to value. +// If not specified, defaults to -6 +func FakeQuantWithMinMaxArgsMin(value float32) FakeQuantWithMinMaxArgsAttr { + return func(m optionalAttr) { + m["min"] = value + } +} + +// FakeQuantWithMinMaxArgsMax sets the optional max attribute to value. +// If not specified, defaults to 6 +func FakeQuantWithMinMaxArgsMax(value float32) FakeQuantWithMinMaxArgsAttr { + return func(m optionalAttr) { + m["max"] = value + } +} + +// FakeQuantWithMinMaxArgsNumBits sets the optional num_bits attribute to value. +// If not specified, defaults to 8 +func FakeQuantWithMinMaxArgsNumBits(value int64) FakeQuantWithMinMaxArgsAttr { + return func(m optionalAttr) { + m["num_bits"] = value + } +} + +// FakeQuantWithMinMaxArgsNarrowRange sets the optional narrow_range attribute to value. +// If not specified, defaults to false +func FakeQuantWithMinMaxArgsNarrowRange(value bool) FakeQuantWithMinMaxArgsAttr { + return func(m optionalAttr) { + m["narrow_range"] = value + } +} + +// Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. +// +// Attributes +// +// * `[min; max]` define the clamping range for the `inputs` data. +// * `inputs` values are quantized into the quantization range ( +// `[0; 2^num_bits - 1]` when `narrow_range` is false and `[1; 2^num_bits - 1]` +// when it is true) and then de-quantized and output as floats in `[min; max]` +// interval. +// * `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive. +// +// Before quantization, `min` and `max` values are adjusted with the following +// logic. +// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values, +// the behavior can be unexpected: +// +// * If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`. +// * If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`. +// * If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `, +// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`. +// +// Quantization is called fake since the output is still in floating point. +func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "FakeQuantWithMinMaxArgs", + Input: []tf.Input{ + inputs, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Batch normalization. +// +// DEPRECATED at GraphDef version 9: Use tf.nn.batch_normalization() +// +// This op is deprecated. Prefer `tf.nn.batch_normalization`. +// +// Arguments: +// t: A 4D input Tensor. +// m: A 1D mean Tensor with size matching the last dimension of t. +// This is the first output from tf.nn.moments, +// or a saved moving average thereof. +// v: A 1D variance Tensor with size matching the last dimension of t. +// This is the second output from tf.nn.moments, +// or a saved moving average thereof. +// beta: A 1D beta Tensor with size matching the last dimension of t. +// An offset to be added to the normalized tensor. +// gamma: A 1D gamma Tensor with size matching the last dimension of t. +// If "scale_after_normalization" is true, this tensor will be multiplied +// with the normalized tensor. +// variance_epsilon: A small float number to avoid dividing by 0. +// scale_after_normalization: A bool indicating whether the resulted tensor +// needs to be multiplied with gamma. +func BatchNormWithGlobalNormalization(scope *Scope, t tf.Output, m tf.Output, v tf.Output, beta tf.Output, gamma tf.Output, variance_epsilon float32, scale_after_normalization bool) (result tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"variance_epsilon": variance_epsilon, "scale_after_normalization": scale_after_normalization} + opspec := tf.OpSpec{ + Type: "BatchNormWithGlobalNormalization", + Input: []tf.Input{ + t, m, v, beta, gamma, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EncodeBase64Attr is an optional argument to EncodeBase64. +type EncodeBase64Attr func(optionalAttr) + +// EncodeBase64Pad sets the optional pad attribute to value. +// +// value: Bool whether padding is applied at the ends. +// If not specified, defaults to false +func EncodeBase64Pad(value bool) EncodeBase64Attr { + return func(m optionalAttr) { + m["pad"] = value + } +} + +// Encode strings into web-safe base64 format. +// +// Refer to the following article for more information on base64 format: +// en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the +// end so that the encoded has length multiple of 4. See Padding section of the +// link above. +// +// Web-safe means that the encoder uses - and _ instead of + and /. +// +// Arguments: +// input: Strings to be encoded. +// +// Returns Input strings encoded in base64. +func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EncodeBase64", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that batches input elements into a SparseTensor. +// +// Arguments: +// input_dataset: A handle to an input dataset. Must have a single component. +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// row_shape: A vector representing the dense shape of each row in the produced +// SparseTensor. The shape may be partially specified, using `-1` to indicate +// that a particular dimension should use the maximum size of all batch elements. +// +// +func DenseToSparseBatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, row_shape tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "DenseToSparseBatchDataset", + Input: []tf.Input{ + input_dataset, batch_size, row_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingAdadeltaParametersGradAccumDebug. +type LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingAdadeltaParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load Adadelta parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adadelta optimization algorithm. +// accumulators: Value of accumulators used in the Adadelta optimization algorithm. +// updates: Value of updates used in the Adadelta optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Adadelta optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdadeltaParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, updates tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdadeltaParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, updates, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns x / y element-wise. +// +// *NOTE*: `Div` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func Div(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Div", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Enqueue a Tensor on the computation outfeed. +// +// Arguments: +// input: A tensor that will be inserted into the outfeed queue. +// +// Returns the created operation. +func OutfeedEnqueue(scope *Scope, input tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OutfeedEnqueue", + Input: []tf.Input{ + input, + }, + } + return scope.AddOperation(opspec) +} + +// DecodeJpegAttr is an optional argument to DecodeJpeg. +type DecodeJpegAttr func(optionalAttr) + +// DecodeJpegChannels sets the optional channels attribute to value. +// +// value: Number of color channels for the decoded image. +// If not specified, defaults to 0 +func DecodeJpegChannels(value int64) DecodeJpegAttr { + return func(m optionalAttr) { + m["channels"] = value + } +} + +// DecodeJpegRatio sets the optional ratio attribute to value. +// +// value: Downscaling ratio. +// If not specified, defaults to 1 +func DecodeJpegRatio(value int64) DecodeJpegAttr { + return func(m optionalAttr) { + m["ratio"] = value + } +} + +// DecodeJpegFancyUpscaling sets the optional fancy_upscaling attribute to value. +// +// value: If true use a slower but nicer upscaling of the +// chroma planes (yuv420/422 only). +// If not specified, defaults to true +func DecodeJpegFancyUpscaling(value bool) DecodeJpegAttr { + return func(m optionalAttr) { + m["fancy_upscaling"] = value + } +} + +// DecodeJpegTryRecoverTruncated sets the optional try_recover_truncated attribute to value. +// +// value: If true try to recover an image from truncated input. +// If not specified, defaults to false +func DecodeJpegTryRecoverTruncated(value bool) DecodeJpegAttr { + return func(m optionalAttr) { + m["try_recover_truncated"] = value + } +} + +// DecodeJpegAcceptableFraction sets the optional acceptable_fraction attribute to value. +// +// value: The minimum required fraction of lines before a truncated +// input is accepted. +// If not specified, defaults to 1 +func DecodeJpegAcceptableFraction(value float32) DecodeJpegAttr { + return func(m optionalAttr) { + m["acceptable_fraction"] = value + } +} + +// DecodeJpegDctMethod sets the optional dct_method attribute to value. +// +// value: string specifying a hint about the algorithm used for +// decompression. Defaults to "" which maps to a system-specific +// default. Currently valid values are ["INTEGER_FAST", +// "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal +// jpeg library changes to a version that does not have that specific +// option.) +// If not specified, defaults to "" +func DecodeJpegDctMethod(value string) DecodeJpegAttr { + return func(m optionalAttr) { + m["dct_method"] = value + } +} + +// Decode a JPEG-encoded image to a uint8 tensor. +// +// The attr `channels` indicates the desired number of color channels for the +// decoded image. +// +// Accepted values are: +// +// * 0: Use the number of channels in the JPEG-encoded image. +// * 1: output a grayscale image. +// * 3: output an RGB image. +// +// If needed, the JPEG-encoded image is transformed to match the requested number +// of color channels. +// +// The attr `ratio` allows downscaling the image by an integer factor during +// decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +// downscaling the image later. +// +// +// This op also supports decoding PNGs and non-animated GIFs since the interface is +// the same, though it is cleaner to use `tf.io.decode_image`. +// +// Arguments: +// contents: 0-D. The JPEG-encoded image. +// +// Returns 3-D with shape `[height, width, channels]`.. +func DecodeJpeg(scope *Scope, contents tf.Output, optional ...DecodeJpegAttr) (image tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DecodeJpeg", + Input: []tf.Input{ + contents, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns the number of nonzeroes of `sparse_matrix`. +// +// Arguments: +// sparse_matrix: A CSRSparseMatrix. +// +// Returns The number of nonzeroes of `sparse_matrix`. +func SparseMatrixNNZ(scope *Scope, sparse_matrix tf.Output) (nnz tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseMatrixNNZ", + Input: []tf.Input{ + sparse_matrix, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug. +type LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load proximal Adagrad embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the proximal Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the proximal Adagrad optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the proximal Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(scope *Scope, parameters tf.Output, accumulators tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingProximalAdagradParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug", + Input: []tf.Input{ + parameters, accumulators, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Serializes the tree ensemble to a proto. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble. +// +// Returns: +// stamp_token: Stamp token of the tree ensemble resource. +// tree_ensemble_serialized: Serialized proto of the ensemble. +func BoostedTreesSerializeEnsemble(scope *Scope, tree_ensemble_handle tf.Output) (stamp_token tf.Output, tree_ensemble_serialized tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesSerializeEnsemble", + Input: []tf.Input{ + tree_ensemble_handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// Computes inverse hyperbolic cosine of x element-wise. +// +// Given an input tensor, the function computes inverse hyperbolic cosine of every element. +// Input range is `[1, inf]`. It returns `nan` if the input lies outside the range. +// +// ```python +// x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")]) +// tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf] +// ``` +func Acosh(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Acosh", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs deterministic pseudorandom random numbers from a gamma distribution. +// +// Outputs random values from a gamma distribution. +// +// The outputs are a deterministic function of `shape`, `seed`, and `alpha`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// alpha: The concentration of the gamma distribution. Shape must match the rightmost +// dimensions of `shape`. +// +// Returns Random values with specified shape. +func StatelessRandomGammaV2(scope *Scope, shape tf.Output, seed tf.Output, alpha tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatelessRandomGammaV2", + Input: []tf.Input{ + shape, seed, alpha, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates a dataset that executes a SQL query and emits rows of the result set. +// +// Arguments: +// driver_name: The database type. Currently, the only supported type is 'sqlite'. +// data_source_name: A connection string to connect to the database. +// query: A SQL query to execute. +// +// +func SqlDataset(scope *Scope, driver_name tf.Output, data_source_name tf.Output, query tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "SqlDataset", + Input: []tf.Input{ + driver_name, data_source_name, query, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Outputs deterministic pseudorandom random integers from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[minval, maxval)`. +// +// The outputs are a deterministic function of `shape`, `seed`, `minval`, and `maxval`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// minval: Minimum value (inclusive, scalar). +// maxval: Maximum value (exclusive, scalar). +// +// Returns Random values with specified shape. +func StatelessRandomUniformInt(scope *Scope, shape tf.Output, seed tf.Output, minval tf.Output, maxval tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StatelessRandomUniformInt", + Input: []tf.Input{ + shape, seed, minval, maxval, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Returns a batched diagonal tensor with a given batched diagonal values. +// +// Given a `diagonal`, this operation returns a tensor with the `diagonal` and +// everything else padded with zeros. The diagonal is computed as follows: +// +// Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a +// tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where: +// +// `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`. +// +// For example: +// +// ``` +// # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]] +// +// and diagonal.shape = (2, 4) +// +// tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0] +// [0, 2, 0, 0] +// [0, 0, 3, 0] +// [0, 0, 0, 4]], +// [[5, 0, 0, 0] +// [0, 6, 0, 0] +// [0, 0, 7, 0] +// [0, 0, 0, 8]]] +// +// which has shape (2, 4, 4) +// ``` +// +// Arguments: +// diagonal: Rank `k`, where `k >= 1`. +// +// Returns Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`. +func MatrixDiag(scope *Scope, diagonal tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixDiag", + Input: []tf.Input{ + diagonal, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatelessTruncatedNormalAttr is an optional argument to StatelessTruncatedNormal. +type StatelessTruncatedNormalAttr func(optionalAttr) + +// StatelessTruncatedNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessTruncatedNormalDtype(value tf.DataType) StatelessTruncatedNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a truncated normal distribution. +// +// The generated values follow a normal distribution with mean 0 and standard +// deviation 1, except that values whose magnitude is more than 2 standard +// deviations from the mean are dropped and re-picked. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessTruncatedNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessTruncatedNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessTruncatedNormal", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug. +type RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve SGD embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the stochastic gradient descent optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the Adadelta optimization algorithm. +func RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebugAttr) (parameters tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. +type StatelessRandomUniformAttr func(optionalAttr) + +// StatelessRandomUniformDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomUniformDtype(value tf.DataType) StatelessRandomUniformAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom random values from a uniform distribution. +// +// The generated values follow a uniform distribution in the range `[0, 1)`. The +// lower bound 0 is included in the range, while the upper bound 1 is excluded. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessRandomUniform(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessRandomUniform", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StatelessRandomUniformFullIntAttr is an optional argument to StatelessRandomUniformFullInt. +type StatelessRandomUniformFullIntAttr func(optionalAttr) + +// StatelessRandomUniformFullIntDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_UINT64 +func StatelessRandomUniformFullIntDtype(value tf.DataType) StatelessRandomUniformFullIntAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom random integers from a uniform distribution. +// +// The generated values are uniform integers covering the whole range of `dtype`. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessRandomUniformFullInt(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomUniformFullIntAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessRandomUniformFullInt", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr is an optional argument to RetrieveTPUEmbeddingMomentumParametersGradAccumDebug. +type RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr func(optionalAttr) + +// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableId(value int64) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugTableName(value string) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingMomentumParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMomentumParametersGradAccumDebugConfig(value string) RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve Momentum embedding parameters with debug support. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the Momentum optimization algorithm. +// momenta: Parameter momenta updated by the Momentum optimization algorithm. +// gradient_accumulators: Parameter gradient_accumulators updated by the Momentum optimization algorithm. +func RetrieveTPUEmbeddingMomentumParametersGradAccumDebug(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMomentumParametersGradAccumDebugAttr) (parameters tf.Output, momenta tf.Output, gradient_accumulators tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// EqualAttr is an optional argument to Equal. +type EqualAttr func(optionalAttr) + +// EqualIncompatibleShapeError sets the optional incompatible_shape_error attribute to value. +// If not specified, defaults to true +func EqualIncompatibleShapeError(value bool) EqualAttr { + return func(m optionalAttr) { + m["incompatible_shape_error"] = value + } +} + +// Returns the truth value of (x == y) element-wise. +// +// *NOTE*: `Equal` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +// +// ```python +// x = tf.constant([2, 4]) +// y = tf.constant(2) +// tf.math.equal(x, y) ==> array([True, False]) +// +// x = tf.constant([2, 4]) +// y = tf.constant([2, 4]) +// tf.math.equal(x, y) ==> array([True, True]) +// ``` +func Equal(scope *Scope, x tf.Output, y tf.Output, optional ...EqualAttr) (z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Equal", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SparseToSparseSetOperationAttr is an optional argument to SparseToSparseSetOperation. +type SparseToSparseSetOperationAttr func(optionalAttr) + +// SparseToSparseSetOperationValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func SparseToSparseSetOperationValidateIndices(value bool) SparseToSparseSetOperationAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Applies set operation along last dimension of 2 `SparseTensor` inputs. +// +// See SetOperationOp::SetOperationFromContext for values of `set_operation`. +// +// If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the +// order and range of `set1` and `set2` indices. +// +// Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, +// and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same +// as `set2`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, +// and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same +// as `set1`. Dimension `n` contains values in a set, duplicates are allowed but +// ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set1` +// and `set2` indices. +// +// Output `result` is a `SparseTensor` represented by `result_indices`, +// `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this +// has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` +// dimension contains the result of `set_operation` applied to the corresponding +// `[0...n-1]` dimension of `set`. +// +// Arguments: +// set1_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set1_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set1_shape: 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must +// be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the +// max set size across `0...n-1` dimensions. +// set2_indices: 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major +// order. +// set2_values: 1D `Tensor`, values of a `SparseTensor`. Must be in row-major +// order. +// set2_shape: 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must +// be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the +// max set size across `0...n-1` dimensions. +// +// +// Returns: +// result_indices: 2D indices of a `SparseTensor`. +// result_values: 1D values of a `SparseTensor`. +// result_shape: 1D `Tensor` shape of a `SparseTensor`. `result_shape[0...n-1]` is +// the same as the 1st `n-1` dimensions of `set1` and `set2`, `result_shape[n]` +// is the max result set size across all `0...n-1` dimensions. +func SparseToSparseSetOperation(scope *Scope, set1_indices tf.Output, set1_values tf.Output, set1_shape tf.Output, set2_indices tf.Output, set2_values tf.Output, set2_shape tf.Output, set_operation string, optional ...SparseToSparseSetOperationAttr) (result_indices tf.Output, result_values tf.Output, result_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"set_operation": set_operation} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SparseToSparseSetOperation", + Input: []tf.Input{ + set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// InfeedEnqueueTupleAttr is an optional argument to InfeedEnqueueTuple. +type InfeedEnqueueTupleAttr func(optionalAttr) + +// InfeedEnqueueTupleLayouts sets the optional layouts attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence for +// all the tuple shapes, in the order the shapes appear in the "shapes" input. +// The layout elements for a sub-shape can be set to -1, in which case the +// corresponding layout will be computed by the infeed operation. +// If not specified, defaults to <> +func InfeedEnqueueTupleLayouts(value []int64) InfeedEnqueueTupleAttr { + return func(m optionalAttr) { + m["layouts"] = value + } +} + +// InfeedEnqueueTupleDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func InfeedEnqueueTupleDeviceOrdinal(value int64) InfeedEnqueueTupleAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// Feeds multiple Tensor values into the computation as an XLA tuple. +// +// Arguments: +// inputs: A list of tensors that will be provided using the infeed mechanism. +// shapes: The shapes of each tensor in `inputs`. +// +// Returns the created operation. +func InfeedEnqueueTuple(scope *Scope, inputs []tf.Output, shapes []tf.Shape, optional ...InfeedEnqueueTupleAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shapes": shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InfeedEnqueueTuple", + Input: []tf.Input{ + tf.OutputList(inputs), + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// LoadTPUEmbeddingRMSPropParametersAttr is an optional argument to LoadTPUEmbeddingRMSPropParameters. +type LoadTPUEmbeddingRMSPropParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingRMSPropParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingRMSPropParametersTableId(value int64) LoadTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingRMSPropParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingRMSPropParametersTableName(value string) LoadTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingRMSPropParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingRMSPropParametersConfig(value string) LoadTPUEmbeddingRMSPropParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load RMSProp embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the RMSProp optimization algorithm. +// ms: Value of ms used in the RMSProp optimization algorithm. +// mom: Value of mom used in the RMSProp optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingRMSPropParameters(scope *Scope, parameters tf.Output, ms tf.Output, mom tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingRMSPropParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingRMSPropParameters", + Input: []tf.Input{ + parameters, ms, mom, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// StatefulUniformFullIntAttr is an optional argument to StatefulUniformFullInt. +type StatefulUniformFullIntAttr func(optionalAttr) + +// StatefulUniformFullIntDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_UINT64 +func StatefulUniformFullIntDtype(value tf.DataType) StatefulUniformFullIntAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs random integers from a uniform distribution. +// +// The generated values are uniform integers covering the whole range of `dtype`. +// +// Arguments: +// resource: The handle of the resource variable that stores the state of the RNG. +// algorithm: The RNG algorithm. +// shape: The shape of the output tensor. +// +// Returns Random values with specified shape. +func StatefulUniformFullInt(scope *Scope, resource tf.Output, algorithm tf.Output, shape tf.Output, optional ...StatefulUniformFullIntAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatefulUniformFullInt", + Input: []tf.Input{ + resource, algorithm, shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersAttr is an optional argument to LoadTPUEmbeddingStochasticGradientDescentParameters. +type LoadTPUEmbeddingStochasticGradientDescentParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingStochasticGradientDescentParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingStochasticGradientDescentParametersTableId(value int64) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingStochasticGradientDescentParametersTableName(value string) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingStochasticGradientDescentParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingStochasticGradientDescentParametersConfig(value string) LoadTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load SGD embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the stochastic gradient descent optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, parameters tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingStochasticGradientDescentParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingStochasticGradientDescentParameters", + Input: []tf.Input{ + parameters, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// RequantizePerChannelAttr is an optional argument to RequantizePerChannel. +type RequantizePerChannelAttr func(optionalAttr) + +// RequantizePerChannelOutType sets the optional out_type attribute to value. +// +// value: The quantized type of output tensor that needs to be converted. +// If not specified, defaults to DT_QUINT8 +func RequantizePerChannelOutType(value tf.DataType) RequantizePerChannelAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Requantizes input with min and max values known per channel. +// +// Arguments: +// input: The original input tensor. +// input_min: The minimum value of the input tensor +// input_max: The maximum value of the input tensor. +// requested_output_min: The minimum value of the output tensor requested. +// requested_output_max: The maximum value of the output tensor requested. +// +// Returns: +// output: Output tensor. +// output_min: The minimum value of the final output tensor +// output_max: The maximum value of the final output tensor. +func RequantizePerChannel(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, requested_output_min tf.Output, requested_output_max tf.Output, optional ...RequantizePerChannelAttr) (output tf.Output, output_min tf.Output, output_max tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RequantizePerChannel", + Input: []tf.Input{ + input, input_min, input_max, requested_output_min, requested_output_max, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// LeakyReluAttr is an optional argument to LeakyRelu. +type LeakyReluAttr func(optionalAttr) + +// LeakyReluAlpha sets the optional alpha attribute to value. +// If not specified, defaults to 0.2 +func LeakyReluAlpha(value float32) LeakyReluAttr { + return func(m optionalAttr) { + m["alpha"] = value + } +} + +// Computes rectified linear: `max(features, features * alpha)`. +func LeakyRelu(scope *Scope, features tf.Output, optional ...LeakyReluAttr) (activations tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LeakyRelu", + Input: []tf.Input{ + features, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Component-wise divides a SparseTensor by a dense Tensor. +// +// *Limitation*: this Op only broadcasts the dense side to the sparse side, but not +// the other direction. +// +// Arguments: +// sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a +// SparseTensor, possibly not in canonical ordering. +// sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. +// sp_shape: 1-D. Shape of the input SparseTensor. +// dense: `R`-D. The dense Tensor operand. +// +// Returns 1-D. The `N` values that are operated on. +func SparseDenseCwiseDiv(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output, dense tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseDenseCwiseDiv", + Input: []tf.Input{ + sp_indices, sp_values, sp_shape, dense, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnqueueTPUEmbeddingIntegerBatchAttr is an optional argument to EnqueueTPUEmbeddingIntegerBatch. +type EnqueueTPUEmbeddingIntegerBatchAttr func(optionalAttr) + +// EnqueueTPUEmbeddingIntegerBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. +// If not specified, defaults to -1 +func EnqueueTPUEmbeddingIntegerBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingIntegerBatchAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// An op that enqueues a list of input batch tensors to TPUEmbedding. +// +// Arguments: +// batch: A list of 1D tensors, one for each embedding table, containing the +// indices into the tables. +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// +// Returns the created operation. +func EnqueueTPUEmbeddingIntegerBatch(scope *Scope, batch []tf.Output, mode_override tf.Output, optional ...EnqueueTPUEmbeddingIntegerBatchAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EnqueueTPUEmbeddingIntegerBatch", + Input: []tf.Input{ + tf.OutputList(batch), mode_override, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MapClearAttr is an optional argument to MapClear. +type MapClearAttr func(optionalAttr) + +// MapClearCapacity sets the optional capacity attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapClearCapacity(value int64) MapClearAttr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// MapClearMemoryLimit sets the optional memory_limit attribute to value. +// If not specified, defaults to 0 +// +// REQUIRES: value >= 0 +func MapClearMemoryLimit(value int64) MapClearAttr { + return func(m optionalAttr) { + m["memory_limit"] = value + } +} + +// MapClearContainer sets the optional container attribute to value. +// If not specified, defaults to "" +func MapClearContainer(value string) MapClearAttr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// MapClearSharedName sets the optional shared_name attribute to value. +// If not specified, defaults to "" +func MapClearSharedName(value string) MapClearAttr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// Op removes all elements in the underlying container. +// +// Returns the created operation. +func MapClear(scope *Scope, dtypes []tf.DataType, optional ...MapClearAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MapClear", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Deserialize `SparseTensor` objects. +// +// The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where +// the last dimension stores serialized `SparseTensor` objects and the other N +// dimensions (N >= 0) correspond to a batch. The ranks of the original +// `SparseTensor` objects must all match. When the final `SparseTensor` is +// created, its rank is the rank of the incoming `SparseTensor` objects plus N; +// the sparse tensors have been concatenated along new dimensions, one for each +// batch. +// +// The output `SparseTensor` object's shape values for the original dimensions +// are the max across the input `SparseTensor` objects' shape values for the +// corresponding dimensions. The new dimensions match the size of the batch. +// +// The input `SparseTensor` objects' indices are assumed ordered in +// standard lexicographic order. If this is not the case, after this +// step run `SparseReorder` to restore index ordering. +// +// For example, if the serialized input is a `[2 x 3]` matrix representing two +// original `SparseTensor` objects: +// +// index = [ 0] +// [10] +// [20] +// values = [1, 2, 3] +// shape = [50] +// +// and +// +// index = [ 2] +// [10] +// values = [4, 5] +// shape = [30] +// +// then the final deserialized `SparseTensor` will be: +// +// index = [0 0] +// [0 10] +// [0 20] +// [1 2] +// [1 10] +// values = [1, 2, 3, 4, 5] +// shape = [2 50] +// +// Arguments: +// serialized_sparse: The serialized `SparseTensor` objects. The last dimension +// must have 3 columns. +// dtype: The `dtype` of the serialized `SparseTensor` objects. +func DeserializeSparse(scope *Scope, serialized_sparse tf.Output, dtype tf.DataType) (sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "DeserializeSparse", + Input: []tf.Input{ + serialized_sparse, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Decode web-safe base64-encoded strings. +// +// Input may or may not have padding at the end. See EncodeBase64 for padding. +// Web-safe means that input must use - and _ instead of + and /. +// +// Arguments: +// input: Base64 strings to decode. +// +// Returns Decoded strings. +func DecodeBase64(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DecodeBase64", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingAdagradParametersAttr is an optional argument to LoadTPUEmbeddingAdagradParameters. +type LoadTPUEmbeddingAdagradParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingAdagradParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingAdagradParametersTableId(value int64) LoadTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingAdagradParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdagradParametersTableName(value string) LoadTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingAdagradParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingAdagradParametersConfig(value string) LoadTPUEmbeddingAdagradParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load Adagrad embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Adagrad optimization algorithm. +// accumulators: Value of accumulators used in the Adagrad optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingAdagradParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingAdagradParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingAdagradParameters", + Input: []tf.Input{ + parameters, accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns the gradient of `Tile`. +// +// DEPRECATED at GraphDef version 3: TileGrad has been replaced with reduce_sum +// +// Since `Tile` takes an input and repeats the input `multiples` times +// along each dimension, `TileGrad` takes in `multiples` and aggregates +// each repeated tile of `input` into `output`. +func TileGrad(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TileGrad", + Input: []tf.Input{ + input, multiples, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AudioSummaryAttr is an optional argument to AudioSummary. +type AudioSummaryAttr func(optionalAttr) + +// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value. +// +// value: Max number of batch elements to generate audio for. +// If not specified, defaults to 3 +// +// REQUIRES: value >= 1 +func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr { + return func(m optionalAttr) { + m["max_outputs"] = value + } +} + +// Outputs a `Summary` protocol buffer with audio. +// +// DEPRECATED at GraphDef version 15: Use AudioSummaryV2. +// +// The summary has up to `max_outputs` summary values containing audio. The +// audio is built from `tensor` which must be 3-D with shape `[batch_size, +// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are +// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. +// +// The `tag` argument is a scalar `Tensor` of type `string`. It is used to +// build the `tag` of the summary values: +// +// * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. +// * If `max_outputs` is greater than 1, the summary value tags are +// generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. +// +// Arguments: +// tag: Scalar. Used to build the `tag` attribute of the summary values. +// tensor: 2-D of shape `[batch_size, frames]`. +// sample_rate: The sample rate of the signal in hertz. +// +// Returns Scalar. Serialized `Summary` protocol buffer. +func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"sample_rate": sample_rate} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSummary", + Input: []tf.Input{ + tag, tensor, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingFTRLParametersAttr is an optional argument to LoadTPUEmbeddingFTRLParameters. +type LoadTPUEmbeddingFTRLParametersAttr func(optionalAttr) + +// LoadTPUEmbeddingFTRLParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingFTRLParametersTableId(value int64) LoadTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingFTRLParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingFTRLParametersTableName(value string) LoadTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingFTRLParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingFTRLParametersConfig(value string) LoadTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load FTRL embedding parameters. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the FTRL optimization algorithm. +// accumulators: Value of accumulators used in the FTRL optimization algorithm. +// linears: Value of linears used in the FTRL optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingFTRLParameters(scope *Scope, parameters tf.Output, accumulators tf.Output, linears tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingFTRLParametersAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingFTRLParameters", + Input: []tf.Input{ + parameters, accumulators, linears, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative. +type DepthwiseConv2dNativeAttr func(optionalAttr) + +// DepthwiseConv2dNativeExplicitPaddings sets the optional explicit_paddings attribute to value. +// If not specified, defaults to <> +func DepthwiseConv2dNativeExplicitPaddings(value []int64) DepthwiseConv2dNativeAttr { + return func(m optionalAttr) { + m["explicit_paddings"] = value + } +} + +// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, height, width, channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, channels, height, width]. +// If not specified, defaults to "NHWC" +func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value. +// +// value: 1-D tensor of length 4. The dilation factor for each dimension of +// `input`. If set to k > 1, there will be k-1 skipped cells between each filter +// element on that dimension. The dimension order is determined by the value of +// `data_format`, see above for details. Dilations in the batch and depth +// dimensions must be 1. +// If not specified, defaults to +func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr { + return func(m optionalAttr) { + m["dilations"] = value + } +} + +// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors. +// +// Given an input tensor of shape `[batch, in_height, in_width, in_channels]` +// and a filter / kernel tensor of shape +// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing +// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies +// a different filter to each input channel (expanding from 1 channel to +// `channel_multiplier` channels for each), then concatenates the results +// together. Thus, the output has `in_channels * channel_multiplier` channels. +// +// ``` +// for k in 0..in_channels-1 +// for q in 0..channel_multiplier-1 +// output[b, i, j, k * channel_multiplier + q] = +// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] * +// filter[di, dj, k, q] +// ``` +// +// Must have `strides[0] = strides[3] = 1`. For the most common case of the same +// horizontal and vertices strides, `strides = [1, stride, stride, 1]`. +// +// Arguments: +// +// +// strides: 1-D of length 4. The stride of the sliding window for each dimension +// of `input`. +// padding: The type of padding algorithm to use. +func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "DepthwiseConv2dNative", + Input: []tf.Input{ + input, filter, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates an all-zeros CSRSparseMatrix with shape `dense_shape`. +// +// Arguments: +// dense_shape: The desired matrix shape. +// +// +// Returns An empty CSR matrix with shape `dense_shape`. +func SparseMatrixZeros(scope *Scope, dense_shape tf.Output, type_ tf.DataType) (sparse_matrix tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"type": type_} + opspec := tf.OpSpec{ + Type: "SparseMatrixZeros", + Input: []tf.Input{ + dense_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PaddingFIFOQueueV2Attr is an optional argument to PaddingFIFOQueueV2. +type PaddingFIFOQueueV2Attr func(optionalAttr) + +// PaddingFIFOQueueV2Shapes sets the optional shapes attribute to value. +// +// value: The shape of each component in a value. The length of this attr must +// be either 0 or the same as the length of component_types. +// Shapes of fixed rank but variable size are allowed by setting +// any shape dimension to -1. In this case, the inputs' shape may vary along +// the given dimension, and DequeueMany will pad the given dimension with +// zeros up to the maximum shape of all elements in the given batch. +// If the length of this attr is 0, different queue elements may have +// different ranks and shapes, but only one element may be dequeued at a time. +// If not specified, defaults to <> +// +// REQUIRES: len(value) >= 0 +func PaddingFIFOQueueV2Shapes(value []tf.Shape) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["shapes"] = value + } +} + +// PaddingFIFOQueueV2Capacity sets the optional capacity attribute to value. +// +// value: The upper bound on the number of elements in this queue. +// Negative numbers mean no limit. +// If not specified, defaults to -1 +func PaddingFIFOQueueV2Capacity(value int64) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["capacity"] = value + } +} + +// PaddingFIFOQueueV2Container sets the optional container attribute to value. +// +// value: If non-empty, this queue is placed in the given container. +// Otherwise, a default container is used. +// If not specified, defaults to "" +func PaddingFIFOQueueV2Container(value string) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["container"] = value + } +} + +// PaddingFIFOQueueV2SharedName sets the optional shared_name attribute to value. +// +// value: If non-empty, this queue will be shared under the given name +// across multiple sessions. +// If not specified, defaults to "" +func PaddingFIFOQueueV2SharedName(value string) PaddingFIFOQueueV2Attr { + return func(m optionalAttr) { + m["shared_name"] = value + } +} + +// A queue that produces elements in first-in first-out order. +// +// Variable-size shapes are allowed by setting the corresponding shape dimensions +// to 0 in the shape attr. In this case DequeueMany will pad up to the maximum +// size of any given element in the minibatch. See below for details. +// +// Arguments: +// component_types: The type of each component in a value. +// +// Returns The handle to the queue. +func PaddingFIFOQueueV2(scope *Scope, component_types []tf.DataType, optional ...PaddingFIFOQueueV2Attr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"component_types": component_types} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "PaddingFIFOQueueV2", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr is an optional argument to LoadTPUEmbeddingMomentumParametersGradAccumDebug. +type LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr func(optionalAttr) + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func LoadTPUEmbeddingMomentumParametersGradAccumDebugTableId(value int64) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMomentumParametersGradAccumDebugTableName(value string) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// LoadTPUEmbeddingMomentumParametersGradAccumDebugConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func LoadTPUEmbeddingMomentumParametersGradAccumDebugConfig(value string) LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Load Momentum embedding parameters with debug support. +// +// An op that loads optimization parameters into HBM for embedding. Must be +// preceded by a ConfigureTPUEmbeddingHost op that sets up the correct +// embedding table configuration. For example, this op is used to install +// parameters that are loaded from a checkpoint before a training loop is +// executed. +// +// Arguments: +// parameters: Value of parameters used in the Momentum optimization algorithm. +// momenta: Value of momenta used in the Momentum optimization algorithm. +// gradient_accumulators: Value of gradient_accumulators used in the Momentum optimization algorithm. +// +// +// +// Returns the created operation. +func LoadTPUEmbeddingMomentumParametersGradAccumDebug(scope *Scope, parameters tf.Output, momenta tf.Output, gradient_accumulators tf.Output, num_shards int64, shard_id int64, optional ...LoadTPUEmbeddingMomentumParametersGradAccumDebugAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "LoadTPUEmbeddingMomentumParametersGradAccumDebug", + Input: []tf.Input{ + parameters, momenta, gradient_accumulators, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Constructs a tensor by tiling a given tensor. +// +// This operation creates a new tensor by replicating `input` `multiples` times. +// The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements, +// and the values of `input` are replicated `multiples[i]` times along the 'i'th +// dimension. For example, tiling `[a b c d]` by `[2]` produces +// `[a b c d a b c d]`. +// +// >>> a = tf.constant([[1,2,3],[4,5,6]], tf.int32) +// >>> b = tf.constant([1,2], tf.int32) +// >>> tf.tile(a, b) +// +// >>> c = tf.constant([2,1], tf.int32) +// >>> tf.tile(a, c) +// +// >>> d = tf.constant([2,2], tf.int32) +// >>> tf.tile(a, d) +// +// +// Arguments: +// input: 1-D or higher. +// multiples: 1-D. Length must be the same as the number of dimensions in `input` +func Tile(scope *Scope, input tf.Output, multiples tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "Tile", + Input: []tf.Input{ + input, multiples, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SerializeSparseAttr is an optional argument to SerializeSparse. +type SerializeSparseAttr func(optionalAttr) + +// SerializeSparseOutType sets the optional out_type attribute to value. +// +// value: The `dtype` to use for serialization; the supported types are `string` +// (default) and `variant`. +// If not specified, defaults to DT_STRING +func SerializeSparseOutType(value tf.DataType) SerializeSparseAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Serialize a `SparseTensor` into a `[3]` `Tensor` object. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the `SparseTensor`. +// sparse_values: 1-D. The `values` of the `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the `SparseTensor`. +func SerializeSparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeSparseAttr) (serialized_sparse tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SerializeSparse", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Extracts the average gradient in the given ConditionalAccumulator. +// +// The op blocks until sufficient (i.e., more than num_required) +// gradients have been accumulated. If the accumulator has already +// aggregated more than num_required gradients, it returns the average of +// the accumulated gradients. Also automatically increments the recorded +// global_step in the accumulator by 1, and resets the aggregate to 0. +// +// Arguments: +// handle: The handle to an accumulator. +// num_required: Number of gradients required before we return an aggregate. +// dtype: The data type of accumulated gradients. Needs to correspond to the type +// of the accumulator. +// +// Returns The average of the accumulated gradients. +func ResourceAccumulatorTakeGradient(scope *Scope, handle tf.Output, num_required tf.Output, dtype tf.DataType) (average tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype} + opspec := tf.OpSpec{ + Type: "ResourceAccumulatorTakeGradient", + Input: []tf.Input{ + handle, num_required, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// InfeedEnqueueAttr is an optional argument to InfeedEnqueue. +type InfeedEnqueueAttr func(optionalAttr) + +// InfeedEnqueueShape sets the optional shape attribute to value. +// +// value: The shape of the tensor. +// If not specified, defaults to <> +func InfeedEnqueueShape(value tf.Shape) InfeedEnqueueAttr { + return func(m optionalAttr) { + m["shape"] = value + } +} + +// InfeedEnqueueLayout sets the optional layout attribute to value. +// +// value: A vector holding the requested layout in minor-to-major sequence. +// If a layout attribute is passed, but its values are all -1, the layout will +// be computed by the infeed operation. +// If not specified, defaults to <> +func InfeedEnqueueLayout(value []int64) InfeedEnqueueAttr { + return func(m optionalAttr) { + m["layout"] = value + } +} + +// InfeedEnqueueDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. This should be -1 when the Op +// is running on a TPU device, and >= 0 when the Op is running on the CPU +// device. +// If not specified, defaults to -1 +func InfeedEnqueueDeviceOrdinal(value int64) InfeedEnqueueAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// An op which feeds a single Tensor value into the computation. +// +// Arguments: +// input: A tensor that will be provided using the infeed mechanism. +// +// Returns the created operation. +func InfeedEnqueue(scope *Scope, input tf.Output, optional ...InfeedEnqueueAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "InfeedEnqueue", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Computes the mean along segments of a tensor. +// +// Read +// [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation) +// for an explanation of segments. +// +// Computes a tensor such that +// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is +// over `j` such that `segment_ids[j] == i` and `N` is the total number of +// values summed. +// +// If the mean is empty for a given segment ID `i`, `output[i] = 0`. +// +//
+// +//
+// +// For example: +// +// ``` +// c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]]) +// tf.segment_mean(c, tf.constant([0, 0, 1])) +// # ==> [[2.5, 2.5, 2.5, 2.5], +// # [5, 6, 7, 8]] +// ``` +// +// +// Arguments: +// +// segment_ids: A 1-D tensor whose size is equal to the size of `data`'s +// first dimension. Values should be sorted and can be repeated. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `k`, the number of segments. +func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SegmentMean", + Input: []tf.Input{ + data, segment_ids, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CTCLossV2Attr is an optional argument to CTCLossV2. +type CTCLossV2Attr func(optionalAttr) + +// CTCLossV2PreprocessCollapseRepeated sets the optional preprocess_collapse_repeated attribute to value. +// +// value: Scalar, if true then repeated labels are +// collapsed prior to the CTC calculation. +// If not specified, defaults to false +func CTCLossV2PreprocessCollapseRepeated(value bool) CTCLossV2Attr { + return func(m optionalAttr) { + m["preprocess_collapse_repeated"] = value + } +} + +// CTCLossV2CtcMergeRepeated sets the optional ctc_merge_repeated attribute to value. +// +// value: Scalar. If set to false, *during* CTC calculation +// repeated non-blank labels will not be merged and are interpreted as +// individual labels. This is a simplified version of CTC. +// If not specified, defaults to true +func CTCLossV2CtcMergeRepeated(value bool) CTCLossV2Attr { + return func(m optionalAttr) { + m["ctc_merge_repeated"] = value + } +} + +// CTCLossV2IgnoreLongerOutputsThanInputs sets the optional ignore_longer_outputs_than_inputs attribute to value. +// +// value: Scalar. If set to true, during CTC +// calculation, items that have longer output sequences than input sequences +// are skipped: they don't contribute to the loss term and have zero-gradient. +// If not specified, defaults to false +func CTCLossV2IgnoreLongerOutputsThanInputs(value bool) CTCLossV2Attr { + return func(m optionalAttr) { + m["ignore_longer_outputs_than_inputs"] = value + } +} + +// Calculates the CTC Loss (log probability) for each batch entry. Also calculates +// +// the gradient. This class performs the softmax operation for you, so inputs +// should be e.g. linear projections of outputs by an LSTM. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. Default blank +// label is 0 rather num_classes - 1. +// labels_indices: The indices of a `SparseTensor`. +// `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for +// `(batch b, time t)`. +// labels_values: The values (labels) associated with the given batch and time. +// sequence_length: A vector containing sequence lengths (batch). +// +// Returns: +// loss: A vector (batch) containing log-probabilities. +// gradient: The gradient of `loss`. 3-D, shape: +// `(max_time x batch_size x num_classes)`. +func CTCLossV2(scope *Scope, inputs tf.Output, labels_indices tf.Output, labels_values tf.Output, sequence_length tf.Output, optional ...CTCLossV2Attr) (loss tf.Output, gradient tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCLossV2", + Input: []tf.Input{ + inputs, labels_indices, labels_values, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ResourceSparseApplyKerasMomentumAttr is an optional argument to ResourceSparseApplyKerasMomentum. +type ResourceSparseApplyKerasMomentumAttr func(optionalAttr) + +// ResourceSparseApplyKerasMomentumUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceSparseApplyKerasMomentumUseLocking(value bool) ResourceSparseApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceSparseApplyKerasMomentumUseNesterov sets the optional use_nesterov attribute to value. +// +// value: If `True`, the tensor passed to compute grad will be +// var + momentum * accum, so in the end, the var you get is actually +// var + momentum * accum. +// If not specified, defaults to false +func ResourceSparseApplyKerasMomentumUseNesterov(value bool) ResourceSparseApplyKerasMomentumAttr { + return func(m optionalAttr) { + m["use_nesterov"] = value + } +} + +// Update relevant entries in '*var' and '*accum' according to the momentum scheme. +// +// Set use_nesterov = True if you want to use Nesterov momentum. +// +// That is for rows we have grad for, we update var and accum as follows: +// +// accum = accum * momentum - lr * grad +// var += accum +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Learning rate. Must be a scalar. +// grad: The gradient. +// indices: A vector of indices into the first dimension of var and accum. +// momentum: Momentum. Must be a scalar. +// +// Returns the created operation. +func ResourceSparseApplyKerasMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, indices tf.Output, momentum tf.Output, optional ...ResourceSparseApplyKerasMomentumAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceSparseApplyKerasMomentum", + Input: []tf.Input{ + var_, accum, lr, grad, indices, momentum, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// MaxPoolGradGradV2Attr is an optional argument to MaxPoolGradGradV2. +type MaxPoolGradGradV2Attr func(optionalAttr) + +// MaxPoolGradGradV2DataFormat sets the optional data_format attribute to value. +// +// value: Specify the data format of the input and output data. With the +// default format "NHWC", the data is stored in the order of: +// [batch, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCHW", the data storage order of: +// [batch, in_channels, in_height, in_width]. +// If not specified, defaults to "NHWC" +func MaxPoolGradGradV2DataFormat(value string) MaxPoolGradGradV2Attr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Computes second-order gradients of the maxpooling function. +// +// Arguments: +// orig_input: The original input tensor. +// orig_output: The original output tensor. +// grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. +// ksize: The size of the window for each dimension of the input tensor. +// strides: The stride of the sliding window for each dimension of the +// input tensor. +// padding: The type of padding algorithm to use. +// +// Returns Gradients of gradients w.r.t. the input to `max_pool`. +func MaxPoolGradGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradGradV2Attr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "MaxPoolGradGradV2", + Input: []tf.Input{ + orig_input, orig_output, grad, ksize, strides, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingMomentumParametersAttr is an optional argument to RetrieveTPUEmbeddingMomentumParameters. +type RetrieveTPUEmbeddingMomentumParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingMomentumParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingMomentumParametersTableId(value int64) RetrieveTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingMomentumParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMomentumParametersTableName(value string) RetrieveTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingMomentumParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingMomentumParametersConfig(value string) RetrieveTPUEmbeddingMomentumParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve Momentum embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the Momentum optimization algorithm. +// momenta: Parameter momenta updated by the Momentum optimization algorithm. +func RetrieveTPUEmbeddingMomentumParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingMomentumParametersAttr) (parameters tf.Output, momenta tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingMomentumParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + +// ConfigureDistributedTPUAttr is an optional argument to ConfigureDistributedTPU. +type ConfigureDistributedTPUAttr func(optionalAttr) + +// ConfigureDistributedTPUEmbeddingConfig sets the optional embedding_config attribute to value. +// +// value: Reserved. Do not use. +// If not specified, defaults to "" +func ConfigureDistributedTPUEmbeddingConfig(value string) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["embedding_config"] = value + } +} + +// ConfigureDistributedTPUTpuEmbeddingConfig sets the optional tpu_embedding_config attribute to value. +// +// value: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that +// describes the embedding lookups of the program. +// If not specified, defaults to "" +func ConfigureDistributedTPUTpuEmbeddingConfig(value string) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["tpu_embedding_config"] = value + } +} + +// ConfigureDistributedTPUIsGlobalInit sets the optional is_global_init attribute to value. +// +// value: Reserved. Do not use. +// If not specified, defaults to false +func ConfigureDistributedTPUIsGlobalInit(value bool) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["is_global_init"] = value + } +} + +// ConfigureDistributedTPUEnableWholeMeshCompilations sets the optional enable_whole_mesh_compilations attribute to value. +// If not specified, defaults to false +func ConfigureDistributedTPUEnableWholeMeshCompilations(value bool) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["enable_whole_mesh_compilations"] = value + } +} + +// ConfigureDistributedTPUCompilationFailureClosesChips sets the optional compilation_failure_closes_chips attribute to value. +// If not specified, defaults to true +func ConfigureDistributedTPUCompilationFailureClosesChips(value bool) ConfigureDistributedTPUAttr { + return func(m optionalAttr) { + m["compilation_failure_closes_chips"] = value + } +} + +// Sets up the centralized structures for a distributed TPU system. +// +// Returns A serialized tensorflow.tpu.TopologyProto that describes the TPU +// topology. +func ConfigureDistributedTPU(scope *Scope, optional ...ConfigureDistributedTPUAttr) (topology tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ConfigureDistributedTPU", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Combines (nests of) input elements into a dataset of (nests of) windows. +// +// A "window" is a finite dataset of flat elements of size `size` (or possibly +// fewer if there are not enough input elements to fill the window and +// `drop_remainder` evaluates to false). +// +// The `shift` argument determines the number of input elements by which +// the window moves on each iteration. The first element in the `k`th window +// will be element +// +// ``` +// 1 + (k-1) * shift +// ``` +// +// of the input dataset. In particular, the first element of the first window +// will always be the first element of the input dataset. +// +// If the `stride` parameter is greater than 1, then each window will skip +// `(stride - 1)` input elements between each element that appears in the +// window. Output windows will still contain `size` elements regardless of +// the value of `stride`. +// +// The `stride` argument determines the stride of the input elements, and the +// `shift` argument determines the shift of the window. +// +// For example, letting `{...}` to represent a Dataset: +// +// - `tf.data.Dataset.range(7).window(2)` produces +// `{{0, 1}, {2, 3}, {4, 5}, {6}}` +// - `tf.data.Dataset.range(7).window(3, 2, 1, True)` produces +// `{{0, 1, 2}, {2, 3, 4}, {4, 5, 6}}` +// - `tf.data.Dataset.range(7).window(3, 1, 2, True)` produces +// `{{0, 2, 4}, {1, 3, 5}, {2, 4, 6}}` +// +// Note that when the `window` transformation is applied to a dataset of +// nested elements, it produces a dataset of nested windows. +// +// For example: +// +// - `tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2)` +// produces `{({0, 1}, {0, 1}), ({2, 3}, {2, 3})}` +// - `tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2)` +// produces `{{"a": {0, 1}}, {"a": {2, 3}}}` +// +// Arguments: +// +// size: An integer scalar, representing the number of elements +// of the input dataset to combine into a window. Must be positive. +// shift: An integer scalar, representing the number of input elements +// by which the window moves in each iteration. Defaults to `size`. +// Must be positive. +// stride: An integer scalar, representing the stride of the input elements +// in the sliding window. Must be positive. The default value of 1 means +// "retain every input element". +// drop_remainder: A Boolean scalar, representing whether the last window should be +// dropped if its size is smaller than `window_size`. +// +// +func WindowDataset(scope *Scope, input_dataset tf.Output, size tf.Output, shift tf.Output, stride tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "WindowDataset", + Input: []tf.Input{ + input_dataset, size, shift, stride, drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// SetSizeAttr is an optional argument to SetSize. +type SetSizeAttr func(optionalAttr) + +// SetSizeValidateIndices sets the optional validate_indices attribute to value. +// If not specified, defaults to true +func SetSizeValidateIndices(value bool) SetSizeAttr { + return func(m optionalAttr) { + m["validate_indices"] = value + } +} + +// Number of unique elements along last dimension of input `set`. +// +// Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`, +// and `set_shape`. The last dimension contains values in a set, duplicates are +// allowed but ignored. +// +// If `validate_indices` is `True`, this op validates the order and range of `set` +// indices. +// +// Arguments: +// set_indices: 2D `Tensor`, indices of a `SparseTensor`. +// set_values: 1D `Tensor`, values of a `SparseTensor`. +// set_shape: 1D `Tensor`, shape of a `SparseTensor`. +// +// Returns For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st +// `n-1` dimensions as `set`. Each value is the number of unique elements in +// the corresponding `[0...n-1]` dimension of `set`. +func SetSize(scope *Scope, set_indices tf.Output, set_values tf.Output, set_shape tf.Output, optional ...SetSizeAttr) (size tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SetSize", + Input: []tf.Input{ + set_indices, set_values, set_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// AutoShardDatasetAttr is an optional argument to AutoShardDataset. +type AutoShardDatasetAttr func(optionalAttr) + +// AutoShardDatasetAutoShardPolicy sets the optional auto_shard_policy attribute to value. +// If not specified, defaults to 0 +func AutoShardDatasetAutoShardPolicy(value int64) AutoShardDatasetAttr { + return func(m optionalAttr) { + m["auto_shard_policy"] = value + } +} + +// Creates a dataset that shards the input dataset. +// +// Creates a dataset that shards the input dataset by num_workers, returning a +// sharded dataset for the index-th worker. This attempts to automatically shard +// a dataset by examining the Dataset graph and inserting a shard op before the +// inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset). +// +// This dataset will throw a NotFound error if we cannot shard the dataset +// automatically. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// num_workers: A scalar representing the number of workers to distribute this dataset across. +// index: A scalar representing the index of the current worker out of num_workers. +// +// +func AutoShardDataset(scope *Scope, input_dataset tf.Output, num_workers tf.Output, index tf.Output, output_types []tf.DataType, output_shapes []tf.Shape, optional ...AutoShardDatasetAttr) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AutoShardDataset", + Input: []tf.Input{ + input_dataset, num_workers, index, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingFTRLParametersAttr is an optional argument to RetrieveTPUEmbeddingFTRLParameters. +type RetrieveTPUEmbeddingFTRLParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingFTRLParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingFTRLParametersTableId(value int64) RetrieveTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingFTRLParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingFTRLParametersTableName(value string) RetrieveTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingFTRLParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingFTRLParametersConfig(value string) RetrieveTPUEmbeddingFTRLParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve FTRL embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns: +// parameters: Parameter parameters updated by the FTRL optimization algorithm. +// accumulators: Parameter accumulators updated by the FTRL optimization algorithm. +// linears: Parameter linears updated by the FTRL optimization algorithm. +func RetrieveTPUEmbeddingFTRLParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingFTRLParametersAttr) (parameters tf.Output, accumulators tf.Output, linears tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingFTRLParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Returns the result of a TPU compilation. +// +// This operation returns the result of a TPU compilation as a serialized +// CompilationResultProto, which holds a status and an error message if an error +// occurred during compilation. +func TPUCompilationResult(scope *Scope) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "TPUCompilationResult", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// ResourceApplyAdagradV2Attr is an optional argument to ResourceApplyAdagradV2. +type ResourceApplyAdagradV2Attr func(optionalAttr) + +// ResourceApplyAdagradV2UseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var and accum tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdagradV2UseLocking(value bool) ResourceApplyAdagradV2Attr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// ResourceApplyAdagradV2UpdateSlots sets the optional update_slots attribute to value. +// If not specified, defaults to true +func ResourceApplyAdagradV2UpdateSlots(value bool) ResourceApplyAdagradV2Attr { + return func(m optionalAttr) { + m["update_slots"] = value + } +} + +// Update '*var' according to the adagrad scheme. +// +// accum += grad * grad +// var -= lr * grad * (1 / (sqrt(accum) + epsilon)) +// +// Arguments: +// var_: Should be from a Variable(). +// accum: Should be from a Variable(). +// lr: Scaling factor. Must be a scalar. +// epsilon: Constant factor. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdagradV2(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdagradV2Attr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdagradV2", + Input: []tf.Input{ + var_, accum, lr, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// QuantizedMatMulWithBiasAndReluAttr is an optional argument to QuantizedMatMulWithBiasAndRelu. +type QuantizedMatMulWithBiasAndReluAttr func(optionalAttr) + +// QuantizedMatMulWithBiasAndReluToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedMatMulWithBiasAndReluToutput(value tf.DataType) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["Toutput"] = value + } +} + +// QuantizedMatMulWithBiasAndReluTransposeA sets the optional transpose_a attribute to value. +// +// value: If true, `a` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasAndReluTransposeA(value bool) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["transpose_a"] = value + } +} + +// QuantizedMatMulWithBiasAndReluTransposeB sets the optional transpose_b attribute to value. +// +// value: If true, `b` is transposed before multiplication. +// If not specified, defaults to false +func QuantizedMatMulWithBiasAndReluTransposeB(value bool) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["transpose_b"] = value + } +} + +// QuantizedMatMulWithBiasAndReluInputQuantMode sets the optional input_quant_mode attribute to value. +// +// value: Input data quantization mode. Either MIN_FIRST(default) or SCALED. +// If not specified, defaults to "MIN_FIRST" +func QuantizedMatMulWithBiasAndReluInputQuantMode(value string) QuantizedMatMulWithBiasAndReluAttr { + return func(m optionalAttr) { + m["input_quant_mode"] = value + } +} + +// Perform a quantized matrix multiplication of `a` by the matrix `b` with bias +// add and relu fusion. +// +// The inputs must be two-dimensional matrices and 1D bias vector. And the inner +// dimension of `a` (after being transposed if `transpose_a` is non-zero) must +// match the outer dimension of `b` (after being transposed if `transposed_b` is +// non-zero). Then do broadcast add operation with bias values on the matrix +// multiplication result. The bias size must match inner dimension of `b`. Then do +// relu activation to get non-negative result. +// +// Arguments: +// a: A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. +// b: A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. +// bias: A 1D bias tensor with size matching with inner dimension of `b` (after being +// transposed if `transposed_b` is non-zero). +// min_a: The float value that the lowest quantized `a` value represents. +// max_a: The float value that the highest quantized `a` value represents. +// min_b: The float value that the lowest quantized `b` value represents. +// max_b: The float value that the highest quantized `b` value represents. +// +// Returns: +// out +// min_out: The float value that the lowest quantized output value represents. +// max_out: The float value that the highest quantized output value represents. +func QuantizedMatMulWithBiasAndRelu(scope *Scope, a tf.Output, b tf.Output, bias tf.Output, min_a tf.Output, max_a tf.Output, min_b tf.Output, max_b tf.Output, optional ...QuantizedMatMulWithBiasAndReluAttr) (out tf.Output, min_out tf.Output, max_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "QuantizedMatMulWithBiasAndRelu", + Input: []tf.Input{ + a, b, bias, min_a, max_a, min_b, max_b, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) +} + +// Shuts down a running distributed TPU system. +// +// The op returns an error if no system is running. +// +// Returns the created operation. +func ShutdownDistributedTPU(scope *Scope) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ShutdownDistributedTPU", + } + return scope.AddOperation(opspec) +} + +// SerializeManySparseAttr is an optional argument to SerializeManySparse. +type SerializeManySparseAttr func(optionalAttr) + +// SerializeManySparseOutType sets the optional out_type attribute to value. +// +// value: The `dtype` to use for serialization; the supported types are `string` +// (default) and `variant`. +// If not specified, defaults to DT_STRING +func SerializeManySparseOutType(value tf.DataType) SerializeManySparseAttr { + return func(m optionalAttr) { + m["out_type"] = value + } +} + +// Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object. +// +// The `SparseTensor` must have rank `R` greater than 1, and the first dimension +// is treated as the minibatch dimension. Elements of the `SparseTensor` +// must be sorted in increasing order of this first dimension. The serialized +// `SparseTensor` objects going into each row of `serialized_sparse` will have +// rank `R-1`. +// +// The minibatch size `N` is extracted from `sparse_shape[0]`. +// +// Arguments: +// sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. +// sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. +// sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. +func SerializeManySparse(scope *Scope, sparse_indices tf.Output, sparse_values tf.Output, sparse_shape tf.Output, optional ...SerializeManySparseAttr) (serialized_sparse tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "SerializeManySparse", + Input: []tf.Input{ + sparse_indices, sparse_values, sparse_shape, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Says whether the targets are in the top `K` predictions. +// +// This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the +// prediction for the target class is among the top `k` predictions among +// all predictions for example `i`. Note that the behavior of `InTopK` differs +// from the `TopK` op in its handling of ties; if multiple classes have the +// same prediction value and straddle the top-`k` boundary, all of those +// classes are considered to be in the top `k`. +// +// More formally, let +// +// \\(predictions_i\\) be the predictions for all classes for example `i`, +// \\(targets_i\\) be the target class for example `i`, +// \\(out_i\\) be the output for example `i`, +// +// $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ +// +// Arguments: +// predictions: A `batch_size` x `classes` tensor. +// targets: A `batch_size` vector of class ids. +// k: Number of top elements to look at for computing precision. +// +// Returns Computed precision at `k` as a `bool Tensor`. +func InTopKV2(scope *Scope, predictions tf.Output, targets tf.Output, k tf.Output) (precision tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InTopKV2", + Input: []tf.Input{ + predictions, targets, k, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Creates an Optional variant with no value. +func OptionalNone(scope *Scope) (optional tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "OptionalNone", + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr is an optional argument to RetrieveTPUEmbeddingStochasticGradientDescentParameters. +type RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr func(optionalAttr) + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersTableId sets the optional table_id attribute to value. +// If not specified, defaults to -1 +func RetrieveTPUEmbeddingStochasticGradientDescentParametersTableId(value int64) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_id"] = value + } +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersTableName sets the optional table_name attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingStochasticGradientDescentParametersTableName(value string) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["table_name"] = value + } +} + +// RetrieveTPUEmbeddingStochasticGradientDescentParametersConfig sets the optional config attribute to value. +// If not specified, defaults to "" +func RetrieveTPUEmbeddingStochasticGradientDescentParametersConfig(value string) RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr { + return func(m optionalAttr) { + m["config"] = value + } +} + +// Retrieve SGD embedding parameters. +// +// An op that retrieves optimization parameters from embedding to host +// memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up +// the correct embedding table configuration. For example, this op is +// used to retrieve updated parameters before saving a checkpoint. +// +// Returns Parameter parameters updated by the stochastic gradient descent optimization algorithm. +func RetrieveTPUEmbeddingStochasticGradientDescentParameters(scope *Scope, num_shards int64, shard_id int64, optional ...RetrieveTPUEmbeddingStochasticGradientDescentParametersAttr) (parameters tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_shards": num_shards, "shard_id": shard_id} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "RetrieveTPUEmbeddingStochasticGradientDescentParameters", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CudnnRNNAttr is an optional argument to CudnnRNN. +type CudnnRNNAttr func(optionalAttr) + +// CudnnRNNRnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNRnnMode(value string) CudnnRNNAttr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNInputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNInputMode(value string) CudnnRNNAttr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNDirection sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNDirection(value string) CudnnRNNAttr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNDropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNDropout(value float32) CudnnRNNAttr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNSeed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNSeed(value int64) CudnnRNNAttr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNSeed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNSeed2(value int64) CudnnRNNAttr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNIsTraining sets the optional is_training attribute to value. +// If not specified, defaults to true +func CudnnRNNIsTraining(value bool) CudnnRNNAttr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// A RNN backed by cuDNN. +// +// Computes the RNN from the input and initial states, with respect to the params +// buffer. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicate whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// is_training: Indicates whether this operation is used for inference or +// training. +// reserve_space: An opaque tensor that can be used in backprop calculation. It +// is only produced if is_training is false. +func CudnnRNN(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNAttr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNN", + Input: []tf.Input{ + input, input_h, input_c, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + +// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// +// +func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "BatchDataset", + Input: []tf.Input{ + input_dataset, batch_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// EnqueueTPUEmbeddingSparseTensorBatchAttr is an optional argument to EnqueueTPUEmbeddingSparseTensorBatch. +type EnqueueTPUEmbeddingSparseTensorBatchAttr func(optionalAttr) + +// EnqueueTPUEmbeddingSparseTensorBatchDeviceOrdinal sets the optional device_ordinal attribute to value. +// +// value: The TPU device to use. Should be >= 0 and less than the number +// of TPU cores in the task on which the node is placed. +// If not specified, defaults to -1 +func EnqueueTPUEmbeddingSparseTensorBatchDeviceOrdinal(value int64) EnqueueTPUEmbeddingSparseTensorBatchAttr { + return func(m optionalAttr) { + m["device_ordinal"] = value + } +} + +// EnqueueTPUEmbeddingSparseTensorBatchCombiners sets the optional combiners attribute to value. +// +// value: A list of string scalars, one for each embedding table that specify +// how to normalize the embedding activations after weighted summation. +// Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have +// the sum of the weights be 0 for 'mean' or the sum of the squared weights be +// 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for +// all tables. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingSparseTensorBatchCombiners(value []string) EnqueueTPUEmbeddingSparseTensorBatchAttr { + return func(m optionalAttr) { + m["combiners"] = value + } +} + +// EnqueueTPUEmbeddingSparseTensorBatchMaxSequenceLengths sets the optional max_sequence_lengths attribute to value. +// If not specified, defaults to <> +func EnqueueTPUEmbeddingSparseTensorBatchMaxSequenceLengths(value []int64) EnqueueTPUEmbeddingSparseTensorBatchAttr { + return func(m optionalAttr) { + m["max_sequence_lengths"] = value + } +} + +// Eases the porting of code that uses tf.nn.embedding_lookup_sparse(). +// +// sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond +// to the ith feature. table_ids[i] indicates which embedding table to look up ith +// feature. +// +// The tensors at corresponding positions in the three input lists (sample_indices, +// embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 +// with dim_size() equal to the total number of lookups into the table described by +// the corresponding feature. +// +// Arguments: +// sample_indices: A list of rank 1 Tensors specifying the training example to +// which the corresponding embedding_indices and aggregation_weights values +// belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse(). +// embedding_indices: A list of rank 1 Tensors, indices into the embedding tables. +// It corresponds to sp_ids.values in embedding_lookup_sparse(). +// aggregation_weights: A list of rank 1 Tensors containing per training example +// aggregation weights. It corresponds to sp_weights.values in +// embedding_lookup_sparse(). +// mode_override: A string input that overrides the mode specified in the +// TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', +// 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set +// in TPUEmbeddingConfiguration is used, otherwise mode_override is used. +// table_ids: A list of integers specifying the identifier of the embedding table +// (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the +// corresponding input. The ith input is looked up using table_ids[i]. The size +// of the table_ids list must be equal to that of sample_indices, +// embedding_indices and aggregation_weights. +// +// Returns the created operation. +func EnqueueTPUEmbeddingSparseTensorBatch(scope *Scope, sample_indices []tf.Output, embedding_indices []tf.Output, aggregation_weights []tf.Output, mode_override tf.Output, table_ids []int64, optional ...EnqueueTPUEmbeddingSparseTensorBatchAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"table_ids": table_ids} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "EnqueueTPUEmbeddingSparseTensorBatch", + Input: []tf.Input{ + tf.OutputList(sample_indices), tf.OutputList(embedding_indices), tf.OutputList(aggregation_weights), mode_override, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// ReverseSequenceAttr is an optional argument to ReverseSequence. +type ReverseSequenceAttr func(optionalAttr) + +// ReverseSequenceBatchDim sets the optional batch_dim attribute to value. +// +// value: The dimension along which reversal is performed. +// If not specified, defaults to 0 +func ReverseSequenceBatchDim(value int64) ReverseSequenceAttr { + return func(m optionalAttr) { + m["batch_dim"] = value + } +} + +// Reverses variable length slices. +// +// This op first slices `input` along the dimension `batch_dim`, and for each +// slice `i`, reverses the first `seq_lengths[i]` elements along +// the dimension `seq_dim`. +// +// The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`, +// and `seq_lengths` must be a vector of length `input.dims[batch_dim]`. +// +// The output slice `i` along dimension `batch_dim` is then given by input +// slice `i`, with the first `seq_lengths[i]` slices along dimension +// `seq_dim` reversed. +// +// For example: +// +// ``` +// # Given this: +// batch_dim = 0 +// seq_dim = 1 +// input.dims = (4, 8, ...) +// seq_lengths = [7, 2, 3, 5] +// +// # then slices of input are reversed on seq_dim, but only up to seq_lengths: +// output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...] +// output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...] +// output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...] +// output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...] +// +// # while entries past seq_lens are copied through: +// output[0, 7:, :, ...] = input[0, 7:, :, ...] +// output[1, 2:, :, ...] = input[1, 2:, :, ...] +// output[2, 3:, :, ...] = input[2, 3:, :, ...] +// output[3, 2:, :, ...] = input[3, 2:, :, ...] +// ``` +// +// In contrast, if: +// +// ``` +// # Given this: +// batch_dim = 2 +// seq_dim = 0 +// input.dims = (8, ?, 4, ...) +// seq_lengths = [7, 2, 3, 5] +// +// # then slices of input are reversed on seq_dim, but only up to seq_lengths: +// output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...] +// output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...] +// output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...] +// output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...] +// +// # while entries past seq_lens are copied through: +// output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...] +// output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...] +// output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...] +// output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...] +// ``` +// +// Arguments: +// input: The input to reverse. +// seq_lengths: 1-D with length `input.dims(batch_dim)` and +// `max(seq_lengths) <= input.dims(seq_dim)` +// seq_dim: The dimension which is partially reversed. +// +// Returns The partially reversed input. It has the same shape as `input`. +func ReverseSequence(scope *Scope, input tf.Output, seq_lengths tf.Output, seq_dim int64, optional ...ReverseSequenceAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"seq_dim": seq_dim} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ReverseSequence", + Input: []tf.Input{ + input, seq_lengths, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Fetches multiple values from infeed as an XLA tuple. +// +// Arguments: +// dtypes: The element types of each element in `outputs`. +// shapes: The shapes of each tensor in `outputs`. +// +// Returns A list of tensors that will be provided using the infeed mechanism. +func InfeedDequeueTuple(scope *Scope, dtypes []tf.DataType, shapes []tf.Shape) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtypes": dtypes, "shapes": shapes} + opspec := tf.OpSpec{ + Type: "InfeedDequeueTuple", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("InfeedDequeueTuple", err) + return + } + return outputs +} + +// Creates and returns an empty tensor list. +// +// All list elements must be tensors of dtype element_dtype and shape compatible +// with element_shape. +// +// handle: an empty tensor list. +// element_dtype: the type of elements in the list. +// element_shape: a shape compatible with that of elements in the list. +func EmptyTensorList(scope *Scope, element_shape tf.Output, max_num_elements tf.Output, element_dtype tf.DataType) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + opspec := tf.OpSpec{ + Type: "EmptyTensorList", + Input: []tf.Input{ + element_shape, max_num_elements, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Sets up TPUEmbedding in a distributed TPU system. +// +// Arguments: +// config: Serialized tensorflow.tpu.TPUEmbeddingConfiguration that +// describes the embedding lookups of the program. +// +// Returns the created operation. +func ConfigureTPUEmbedding(scope *Scope, config string) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"config": config} + opspec := tf.OpSpec{ + Type: "ConfigureTPUEmbedding", + + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + +// Returns the number of gradients aggregated in the given accumulators. +// +// Arguments: +// handle: The handle to an accumulator. +// +// Returns The number of gradients aggregated in the given accumulator. +func ResourceAccumulatorNumAccumulated(scope *Scope, handle tf.Output) (num_accumulated tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "ResourceAccumulatorNumAccumulated", + Input: []tf.Input{ + handle, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Connects N outputs from an N-way replicated TPU computation. +// +// This operation holds a replicated output from a `tpu.replicate()` computation subgraph. +// Each replicated output has the same shape and type alongside the input. +// +// For example: +// ``` +// %computation = "tf.Computation"() +// %replicated_output:2 = "tf.TPUReplicatedOutput"(%computation) +// ``` +// The above computation has a replicated output of two replicas. +func TPUReplicatedOutput(scope *Scope, input tf.Output, num_replicas int64) (outputs []tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"num_replicas": num_replicas} + opspec := tf.OpSpec{ + Type: "TPUReplicatedOutput", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil { + scope.UpdateErr("TPUReplicatedOutput", err) + return + } + return outputs +} + +// Removes keys and its associated values from a table. +// +// The tensor `keys` must of the same type as the keys of the table. Keys not +// already in the table are silently ignored. +// +// Arguments: +// table_handle: Handle to the table. +// keys: Any shape. Keys of the elements to remove. +// +// Returns the created operation. +func LookupTableRemoveV2(scope *Scope, table_handle tf.Output, keys tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LookupTableRemoveV2", + Input: []tf.Input{ + table_handle, keys, + }, + } + return scope.AddOperation(opspec) +} + +// NotEqualAttr is an optional argument to NotEqual. +type NotEqualAttr func(optionalAttr) + +// NotEqualIncompatibleShapeError sets the optional incompatible_shape_error attribute to value. +// If not specified, defaults to true +func NotEqualIncompatibleShapeError(value bool) NotEqualAttr { + return func(m optionalAttr) { + m["incompatible_shape_error"] = value + } +} + +// Returns the truth value of (x != y) element-wise. +// +// *NOTE*: `NotEqual` supports broadcasting. More about broadcasting +// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func NotEqual(scope *Scope, x tf.Output, y tf.Output, optional ...NotEqualAttr) (z tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "NotEqual", + Input: []tf.Input{ + x, y, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} diff --git a/tensorflow/go/operation.go b/tensorflow/go/operation.go new file mode 100644 index 0000000..d6a37e0 --- /dev/null +++ b/tensorflow/go/operation.go @@ -0,0 +1,216 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +import "C" + +import "unsafe" + +// Operation that has been added to the graph. +type Operation struct { + c *C.TF_Operation + // A reference to the Graph to prevent it from + // being GCed while the Operation is still alive. + g *Graph +} + +// Name returns the name of the operation. +func (op *Operation) Name() string { + return C.GoString(C.TF_OperationName(op.c)) +} + +// Type returns the name of the operator used by this operation. +func (op *Operation) Type() string { + return C.GoString(C.TF_OperationOpType(op.c)) +} + +// NumOutputs returns the number of outputs of op. +func (op *Operation) NumOutputs() int { + return int(C.TF_OperationNumOutputs(op.c)) +} + +// Device returns a specification of the device on which this operation +// will be executed, or the empty string if there is no such specification. +func (op *Operation) Device() string { + return C.GoString(C.TF_OperationDevice(op.c)) +} + +// OutputListSize returns the size of the list of Outputs that is produced by a +// named output of op. +// +// An Operation has multiple named outputs, each of which produces either +// a single tensor or a list of tensors. This method returns the size of +// the list of tensors for a specific output of the operation, identified +// by its name. +func (op *Operation) OutputListSize(output string) (int, error) { + cname := C.CString(output) + defer C.free(unsafe.Pointer(cname)) + status := newStatus() + n := C.TF_OperationOutputListLength(op.c, cname, status.c) + return int(n), status.Err() +} + +// Output returns the i-th output of op. +func (op *Operation) Output(i int) Output { + return Output{op, i} +} + +// NumInputs returns the number of inputs of op. +func (op *Operation) NumInputs() int { + return int(C.TF_OperationNumInputs(op.c)) +} + +// Output represents one of the outputs of an operation in the graph. Has a +// DataType (and eventually a Shape). May be passed as an input argument to a +// function for adding operations to a graph, or to a Session's Run() method to +// fetch that output as a tensor. +type Output struct { + // Op is the Operation that produces this Output. + Op *Operation + + // Index specifies the index of the output within the Operation. + Index int +} + +// DataType returns the type of elements in the tensor produced by p. +func (p Output) DataType() DataType { + return DataType(C.TF_OperationOutputType(p.c())) +} + +// Shape returns the (possibly incomplete) shape of the tensor produced p. +func (p Output) Shape() Shape { + status := newStatus() + port := p.c() + ndims := C.TF_GraphGetTensorNumDims(p.Op.g.c, port, status.c) + if err := status.Err(); err != nil { + // This should not be possible since an error only occurs if + // the operation does not belong to the graph. It should not + // be possible to construct such an Operation object. + return Shape{} + } + if ndims < 0 { + return Shape{} + } + if ndims == 0 { + return ScalarShape() + } + dims := make([]C.int64_t, ndims) + C.TF_GraphGetTensorShape(p.Op.g.c, port, &dims[0], ndims, status.c) + if err := status.Err(); err != nil { + // Same as above, should not be possible. + return Shape{} + } + ret := Shape{dims: make([]int64, ndims)} + for i := 0; i < int(ndims); i++ { + ret.dims[i] = int64(dims[i]) + } + return ret +} + +func (p Output) c() C.TF_Output { + if p.Op == nil { + // Attempt to provide a more useful panic message than "nil + // pointer dereference". + panic("nil-Operation. If the Output was created with a Scope object, see Scope.Err() for details.") + } + return C.TF_Output{oper: p.Op.c, index: C.int(p.Index)} +} + +func (p Output) canBeAnInput() {} + +// Consumers returns the inputs that consume this output. +func (p Output) Consumers() []Consumer { + max := int(C.TF_OperationOutputNumConsumers(p.c())) + if max == 0 { + return nil + } + inputs := make([]C.TF_Input, max) + n := C.TF_OperationOutputConsumers(p.c(), (*C.TF_Input)(unsafe.Pointer(&inputs[0])), C.int(max)) + inputs = inputs[:int(n)] + + var consumers []Consumer + for _, consumer := range inputs { + consumers = append(consumers, Consumer{ + Index: int(consumer.index), + Op: &Operation{ + c: consumer.oper, + g: p.Op.g, + }, + }) + } + + return consumers +} + +// Consumer identifies a specific input of an operation that consumes the output +// of another operation. +type Consumer struct { + // Op is the Operation that is consuming the output of another operation. + Op *Operation + + // Index is the index of the input within Op that the output of another + // operation is connected to. + Index int +} + +func (p Consumer) c() C.TF_Input { + if p.Op == nil { + // Attempt to provide a more useful panic message than "nil + // pointer dereference". + panic("nil-Operation. Consumer objects should only be created by a call to Output.Consumers") + } + return C.TF_Input{oper: p.Op.c, index: C.int(p.Index)} +} + +// DataType returns the type of the input. +func (p Consumer) DataType() DataType { + return DataType(C.TF_OperationInputType(p.c())) +} + +// Producer returns the Output that is connected to this Consumer. +func (p Consumer) Producer() Output { + output := C.TF_OperationInput(p.c()) + return Output{ + Op: &Operation{ + c: output.oper, + g: p.Op.g, + }, + Index: int(output.index), + } +} + +// Input is the interface for specifying inputs to an operation being added to +// a Graph. +// +// Operations can have multiple inputs, each of which could be either a tensor +// produced by another operation (an Output object), or a list of tensors +// produced by other operations (an OutputList). Thus, this interface is +// implemented by both Output and OutputList. +// +// See OpSpec.Input for more information. +type Input interface { + // Unexported to preclude implementations outside this package. + canBeAnInput() +} + +// OutputList represents a list of Outputs that can be provided as input to +// another operation. +type OutputList []Output + +func (l OutputList) canBeAnInput() {} diff --git a/tensorflow/go/operation_test.go b/tensorflow/go/operation_test.go new file mode 100644 index 0000000..4af9e33 --- /dev/null +++ b/tensorflow/go/operation_test.go @@ -0,0 +1,269 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "runtime" + "runtime/debug" + "testing" +) + +// createGraphAndOp creates an Operation but loses the reference to the Graph. +func createGraphAndOp() (*Operation, error) { + t, err := NewTensor(int64(1)) + if err != nil { + return nil, err + } + g := NewGraph() + output, err := Placeholder(g, "my_placeholder", t.DataType()) + if err != nil { + return nil, err + } + return output.Op, nil +} + +func TestOperationLifetime(t *testing.T) { + // Ensure that the Graph is not garbage collected while the program + // still has access to the Operation. + op, err := createGraphAndOp() + if err != nil { + t.Fatal(err) + } + forceGC() + if got, want := op.Name(), "my_placeholder"; got != want { + t.Errorf("Got '%s', want '%s'", got, want) + } + if got, want := op.Type(), "Placeholder"; got != want { + t.Errorf("Got '%s', want '%s'", got, want) + } +} + +func TestOperationOutputListSize(t *testing.T) { + graph := NewGraph() + c1, err := Const(graph, "c1", int64(1)) + if err != nil { + t.Fatal(err) + } + c2, err := Const(graph, "c2", [][]int64{{1, 2}, {3, 4}}) + if err != nil { + t.Fatal(err) + } + // The ShapeN op takes a list of tensors as input and a list as output. + op, err := graph.AddOperation(OpSpec{ + Type: "ShapeN", + Input: []Input{OutputList{c1, c2}}, + }) + if err != nil { + t.Fatal(err) + } + n, err := op.OutputListSize("output") + if err != nil { + t.Fatal(err) + } + if got, want := n, 2; got != want { + t.Errorf("Got %d, want %d", got, want) + } + if got, want := op.NumOutputs(), 2; got != want { + t.Errorf("Got %d, want %d", got, want) + } +} + +func TestOperationShapeAttribute(t *testing.T) { + g := NewGraph() + _, err := g.AddOperation(OpSpec{ + Type: "Placeholder", + Attrs: map[string]interface{}{ + "dtype": Float, + "shape": MakeShape(-1, 3), + }, + }) + if err != nil { + t.Fatal(err) + } + // If and when the API to get attributes is added, check that here. +} + +func TestOutputDataTypeAndShape(t *testing.T) { + graph := NewGraph() + testdata := []struct { + Value interface{} + Shape []int64 + dtype DataType + }{ + { // Scalar + int64(0), + []int64{}, + Int64, + }, + { // Vector + []int32{1, 2, 3}, + []int64{3}, + Int32, + }, + { // Matrix + [][]float64{ + {1, 2, 3}, + {4, 5, 6}, + }, + []int64{2, 3}, + Double, + }, + { // Matrix of Uint64 + [][]uint64{ + {1, 2, 3}, + {4, 5, 6}, + }, + []int64{2, 3}, + Uint64, + }, + } + for idx, test := range testdata { + t.Run(fmt.Sprintf("#%d Value %T", idx, test.Value), func(t *testing.T) { + c, err := Const(graph, fmt.Sprintf("const%d", idx), test.Value) + if err != nil { + t.Fatal(err) + } + if got, want := c.DataType(), test.dtype; got != want { + t.Errorf("Got DataType %v, want %v", got, want) + } + shape := c.Shape() + if got, want := shape.NumDimensions(), len(test.Shape); got != want { + t.Fatalf("Got a shape with %d dimensions, want %d", got, want) + } + for i := 0; i < len(test.Shape); i++ { + if got, want := shape.Size(i), test.Shape[i]; got != want { + t.Errorf("Got %d, want %d for dimension #%d/%d", got, want, i, len(test.Shape)) + } + } + }) + } + // Unknown number of dimensions + dummyTensor, err := NewTensor(float64(0)) + if err != nil { + t.Fatal(err) + } + placeholder, err := Placeholder(graph, "placeholder", dummyTensor.DataType()) + if err != nil { + t.Fatal(err) + } + if shape := placeholder.Shape(); shape.NumDimensions() != -1 { + t.Errorf("Got shape %v, wanted an unknown number of dimensions", shape) + } +} + +func TestOperationInputs(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + if err != nil { + t.Fatal(err) + } + y, err := Placeholder(g, "y", Float) + if err != nil { + t.Fatal(err) + } + add, err := Add(g, "add", x, y) + if err != nil { + t.Fatal(err) + } + addOp := add.Op + + if out := addOp.NumInputs(); out != 2 { + t.Fatalf("Got %d inputs, wanted 2", out) + } +} + +func TestOperationConsumers(t *testing.T) { + g := NewGraph() + x, err := Placeholder(g, "x", Float) + if err != nil { + t.Fatal(err) + } + a, err := Neg(g, "a", x) + if err != nil { + t.Fatal(err) + } + b, err := Neg(g, "b", x) + if err != nil { + t.Fatal(err) + } + + consumers := []*Operation{a.Op, b.Op} + + xConsumers := x.Consumers() + if out := len(xConsumers); out != 2 { + t.Fatalf("Got %d consumers, wanted 2", out) + } + + for i, consumer := range xConsumers { + got := consumer.Op.Name() + want := consumers[i].Name() + if got != want { + t.Fatalf("%d. Got op name %q, wanted %q", i, got, want) + } + + got = consumer.Producer().Op.Name() + want = x.Op.Name() + if got != want { + t.Fatalf("%d. Got op name %q, wanted %q", i, got, want) + } + } + + if len(b.Consumers()) != 0 { + t.Fatalf("expected %+v to have no consumers", b) + } +} + +func TestOperationDevice(t *testing.T) { + graph := NewGraph() + v, err := NewTensor(float32(1.0)) + if err != nil { + t.Fatal(err) + } + op, err := graph.AddOperation(OpSpec{ + Type: "Const", + Name: "Const", + Attrs: map[string]interface{}{ + "dtype": v.DataType(), + "value": v, + }, + Device: "/device:GPU:0", + }) + if err != nil { + t.Fatal(err) + } + if got, want := op.Device(), "/device:GPU:0"; got != want { + t.Errorf("Got %q, want %q", got, want) + } +} + +func forceGC() { + var mem runtime.MemStats + runtime.ReadMemStats(&mem) + // It was empirically observed that without this extra allocation + // TestOperationLifetime would fail only 50% of the time if + // Operation did not hold on to a reference to Graph. With this + // additional allocation, and with the bug where Operation does + // not hold onto a Graph, the test failed 90+% of the time. + // + // The author is aware that this technique is potentially fragile + // and fishy. Suggestions for alternatives are welcome. + bytesTillGC := mem.NextGC - mem.HeapAlloc + 1 + _ = make([]byte, bytesTillGC) + runtime.GC() + debug.FreeOSMemory() +} diff --git a/tensorflow/go/saved_model.go b/tensorflow/go/saved_model.go new file mode 100644 index 0000000..64ae82e --- /dev/null +++ b/tensorflow/go/saved_model.go @@ -0,0 +1,100 @@ +/* +Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "runtime" + "unsafe" + + "github.com/golang/protobuf/proto" + corepb "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto" +) + +// #include +// #include "tensorflow/c/c_api.h" +import "C" + +// SavedModel represents the contents of loaded SavedModel. +// TODO(jhseu): Add and document metagraphdef when we pregenerate protobufs. +type SavedModel struct { + Session *Session + Graph *Graph + Signatures map[string]Signature +} + +// LoadSavedModel creates a new SavedModel from a model previously +// exported to a directory on disk. +// +// Exported models contain a set of graphs and, optionally, variable values. +// Tags in the model identify a single graph. LoadSavedModel initializes a +// session with the identified graph and with variables initialized to from the +// checkpoints on disk. +// +// The tensorflow package currently does not have the ability to export a model +// to a directory from Go. This function thus currently targets loading models +// exported in other languages, such as using tf.saved_model.builder in Python. +// See: +// https://www.tensorflow.org/code/tensorflow/python/saved_model/ +func LoadSavedModel(exportDir string, tags []string, options *SessionOptions) (*SavedModel, error) { + status := newStatus() + cOpt, doneOpt, err := options.c() + defer doneOpt() + if err != nil { + return nil, err + } + cExportDir := C.CString(exportDir) + if len(tags) == 0 { + return nil, fmt.Errorf("empty tags are not allowed") + } + cTags := make([]*C.char, len(tags)) + for i := range tags { + cTags[i] = C.CString(tags[i]) + } + graph := NewGraph() + metaGraphDefBuf := C.TF_NewBuffer() + defer C.TF_DeleteBuffer(metaGraphDefBuf) + // TODO(jhseu): Add support for run_options and meta_graph_def. + cSess := C.TF_LoadSessionFromSavedModel(cOpt, nil, cExportDir, (**C.char)(unsafe.Pointer(&cTags[0])), C.int(len(cTags)), graph.c, metaGraphDefBuf, status.c) + for i := range cTags { + C.free(unsafe.Pointer(cTags[i])) + } + C.free(unsafe.Pointer(cExportDir)) + + metaGraphDefBytes := C.GoBytes(metaGraphDefBuf.data, C.int(metaGraphDefBuf.length)) + metaGraphDef := new(corepb.MetaGraphDef) + if err := proto.Unmarshal(metaGraphDefBytes, metaGraphDef); err != nil { + return nil, err + } + + signatures := generateSignatures(metaGraphDef.GetSignatureDef()) + + if err := status.Err(); err != nil { + return nil, err + } + s := &Session{c: cSess} + runtime.SetFinalizer(s, func(s *Session) { s.Close() }) + return &SavedModel{Session: s, Graph: graph, Signatures: signatures}, nil +} + +func generateSignatures(pb map[string]*corepb.SignatureDef) map[string]Signature { + signatures := make(map[string]Signature) + for name, signature := range pb { + signatures[name] = signatureDefFromProto(signature) + } + return signatures +} diff --git a/tensorflow/go/saved_model_test.go b/tensorflow/go/saved_model_test.go new file mode 100644 index 0000000..24811d6 --- /dev/null +++ b/tensorflow/go/saved_model_test.go @@ -0,0 +1,41 @@ +/* +Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import "testing" + +func TestSavedModel(t *testing.T) { + tags := []string{"serve"} + bundle, err := LoadSavedModel("../cc/saved_model/testdata/half_plus_two/00000123", tags, nil) + if err != nil { + t.Fatalf("LoadSavedModel(): %v", err) + } + if op := bundle.Graph.Operation("y"); op == nil { + t.Fatalf("\"y\" not found in graph") + } + t.Logf("SavedModel: %+v", bundle) + // TODO(jhseu): half_plus_two has a tf.Example proto dependency to run. Add a + // more thorough test when the generated protobufs are available. +} + +func TestSavedModelWithEmptyTags(t *testing.T) { + tags := []string{} + _, err := LoadSavedModel("../cc/saved_model/testdata/half_plus_two/00000123", tags, nil) + if err == nil { + t.Fatalf("LoadSavedModel() should return an error if tags are empty") + } +} diff --git a/tensorflow/go/session.go b/tensorflow/go/session.go new file mode 100644 index 0000000..48909ff --- /dev/null +++ b/tensorflow/go/session.go @@ -0,0 +1,408 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +import "C" + +import ( + "errors" + "fmt" + "runtime" + "sync" + "unsafe" +) + +// Session drives a TensorFlow graph computation. +// +// When a Session is created with a given target, a new Session object is bound +// to the universe of resources specified by that target. Those resources are +// available to this session to perform computation described in the GraphDef. +// After creating the session with a graph, the caller uses the Run() API to +// perform the computation and potentially fetch outputs as Tensors. +// A Session allows concurrent calls to Run(). +type Session struct { + c *C.TF_Session + + // For ensuring that: + // - Close() blocks on all Run() calls to complete. + // - Close() can be called multiple times. + wg sync.WaitGroup + mu sync.Mutex +} + +// NewSession creates a new execution session with the associated graph. +// options may be nil to use the default options. +func NewSession(graph *Graph, options *SessionOptions) (*Session, error) { + status := newStatus() + cOpt, doneOpt, err := options.c() + defer doneOpt() + if err != nil { + return nil, err + } + cSess := C.TF_NewSession(graph.c, cOpt, status.c) + if err := status.Err(); err != nil { + return nil, err + } + + s := &Session{c: cSess} + runtime.SetFinalizer(s, func(s *Session) { s.Close() }) + return s, nil +} + +// Device structure contains information about a device associated with a session, as returned by ListDevices() +type Device struct { + Name, Type string + MemoryLimitBytes int64 +} + +// String describes d and implements fmt.Stringer. +func (d Device) String() string { + memStr := "no memory limit" + if d.MemoryLimitBytes >= 0 { + memStr = fmt.Sprintf("memory limit %d bytes", d.MemoryLimitBytes) + } + return fmt.Sprintf("(Device: name \"%s\", type %s, %s)", d.Name, d.Type, memStr) +} + +func deviceSliceFromDeviceList(list *C.TF_DeviceList) ([]Device, error) { + var devices []Device + status := newStatus() + + for i := 0; i < int(C.TF_DeviceListCount(list)); i++ { + name := C.TF_DeviceListName(list, C.int(i), status.c) + if err := status.Err(); err != nil { + return nil, fmt.Errorf("DeviceListName(index=%d) failed: %v", i, err) + } + + deviceType := C.TF_DeviceListType(list, C.int(i), status.c) + if err := status.Err(); err != nil { + return nil, fmt.Errorf("DeviceListType(index=%d) failed: %v", i, err) + } + + memoryLimitBytes := C.TF_DeviceListMemoryBytes(list, C.int(i), status.c) + if err := status.Err(); err != nil { + return nil, fmt.Errorf("DeviceListMemoryBytes(index=%d) failed: %v", i, err) + } + + device := Device{ + Name: C.GoString(name), + Type: C.GoString(deviceType), + MemoryLimitBytes: int64(memoryLimitBytes), + } + + devices = append(devices, device) + } + + return devices, nil +} + +// ListDevices returns the list of devices associated with a Session. +func (s *Session) ListDevices() ([]Device, error) { + status := newStatus() + devicesList := C.TF_SessionListDevices(s.c, status.c) + if err := status.Err(); err != nil { + return nil, fmt.Errorf("SessionListDevices() failed: %v", err) + } + defer C.TF_DeleteDeviceList(devicesList) + return deviceSliceFromDeviceList(devicesList) +} + +// Run the graph with the associated session starting with the supplied feeds +// to compute the value of the requested fetches. Runs, but does not return +// Tensors for operations specified in targets. +// +// On success, returns the fetched Tensors in the same order as supplied in +// the fetches argument. If fetches is set to nil, the returned Tensor fetches +// is empty. +func (s *Session) Run(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) ([]*Tensor, error) { + s.mu.Lock() + if s.c == nil { + s.mu.Unlock() + return nil, errors.New("session is closed") + } + s.wg.Add(1) + s.mu.Unlock() + defer s.wg.Done() + + c := newCRunArgs(feeds, fetches, targets) + status := newStatus() + C.TF_SessionRun(s.c, nil, + ptrOutput(c.feeds), ptrTensor(c.feedTensors), C.int(len(feeds)), + ptrOutput(c.fetches), ptrTensor(c.fetchTensors), C.int(len(fetches)), + ptrOperation(c.targets), C.int(len(targets)), + nil, status.c) + + // Make sure GC won't harvest input tensors until SessionRun() is finished + runtime.KeepAlive(feeds) + + if err := status.Err(); err != nil { + return nil, err + } + return c.toGo(), nil +} + +// PartialRun enables incremental evaluation of graphs. +// +// PartialRun allows the caller to pause the evaluation of a graph, run +// arbitrary code that depends on the intermediate computation of the graph, +// and then resume graph execution. The results of the arbitrary code can be +// fed into the graph when resuming execution. In contrast, Session.Run +// executes the graph to compute the requested fetches using the provided feeds +// and discards all intermediate state (e.g., value of intermediate tensors) +// when it returns. +// +// For example, consider a graph for unsupervised training of a neural network +// model. PartialRun can be used to pause execution after the forward pass of +// the network, let the caller actuate the output (e.g., play a game, actuate a +// robot etc.), determine the error/loss and then feed this calculated loss +// when resuming the backward pass of the graph. +type PartialRun struct { + session *Session + handle *C.char +} + +// Run resumes execution of the graph to compute the requested fetches and +// targets with the provided feeds. +func (pr *PartialRun) Run(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) ([]*Tensor, error) { + var ( + c = newCRunArgs(feeds, fetches, targets) + status = newStatus() + s = pr.session + ) + s.mu.Lock() + if s.c == nil { + s.mu.Unlock() + return nil, errors.New("session is closed") + } + s.wg.Add(1) + s.mu.Unlock() + defer s.wg.Done() + + C.TF_SessionPRun(s.c, pr.handle, + ptrOutput(c.feeds), ptrTensor(c.feedTensors), C.int(len(feeds)), + ptrOutput(c.fetches), ptrTensor(c.fetchTensors), C.int(len(fetches)), + ptrOperation(c.targets), C.int(len(targets)), + status.c) + if err := status.Err(); err != nil { + return nil, err + } + return c.toGo(), nil +} + +// NewPartialRun sets up the graph for incremental evaluation. +// +// All values of feeds, fetches and targets that may be provided to Run calls +// on the returned PartialRun need to be provided to NewPartialRun. +// +// See documentation for the PartialRun type. +func (s *Session) NewPartialRun(feeds, fetches []Output, targets []*Operation) (*PartialRun, error) { + var ( + cfeeds = make([]C.TF_Output, len(feeds)) + cfetches = make([]C.TF_Output, len(fetches)) + ctargets = make([]*C.TF_Operation, len(targets)) + + pcfeeds *C.TF_Output + pcfetches *C.TF_Output + pctargets **C.TF_Operation + + status = newStatus() + ) + if len(feeds) > 0 { + pcfeeds = &cfeeds[0] + for i, o := range feeds { + cfeeds[i] = o.c() + } + } + if len(fetches) > 0 { + pcfetches = &cfetches[0] + for i, o := range fetches { + cfetches[i] = o.c() + } + } + if len(targets) > 0 { + pctargets = &ctargets[0] + for i, o := range targets { + ctargets[i] = o.c + } + } + + s.mu.Lock() + if s.c == nil { + s.mu.Unlock() + return nil, errors.New("session is closed") + } + s.wg.Add(1) + s.mu.Unlock() + defer s.wg.Done() + + pr := &PartialRun{session: s} + C.TF_SessionPRunSetup(s.c, + pcfeeds, C.int(len(feeds)), + pcfetches, C.int(len(fetches)), + pctargets, C.int(len(targets)), + &pr.handle, status.c) + if err := status.Err(); err != nil { + return nil, err + } + runtime.SetFinalizer(pr, func(pr *PartialRun) { + C.TF_DeletePRunHandle(pr.handle) + }) + return pr, nil +} + +// Close a session. This contacts any other processes associated with this +// session, if applicable. Blocks until all previous calls to Run have returned. +func (s *Session) Close() error { + s.mu.Lock() + defer s.mu.Unlock() + s.wg.Wait() + if s.c == nil { + return nil + } + status := newStatus() + C.TF_CloseSession(s.c, status.c) + if err := status.Err(); err != nil { + return err + } + C.TF_DeleteSession(s.c, status.c) + s.c = nil + return status.Err() +} + +// SessionOptions contains configuration information for a session. +type SessionOptions struct { + // Target indicates the TensorFlow runtime to connect to. + // + // If 'target' is empty or unspecified, the local TensorFlow runtime + // implementation will be used. Otherwise, the TensorFlow engine + // defined by 'target' will be used to perform all computations. + // + // "target" can be either a single entry or a comma separated list + // of entries. Each entry is a resolvable address of one of the + // following formats: + // local + // ip:port + // host:port + // ... other system-specific formats to identify tasks and jobs ... + // + // NOTE: at the moment 'local' maps to an in-process service-based + // runtime. + // + // Upon creation, a single session affines itself to one of the + // remote processes, with possible load balancing choices when the + // "target" resolves to a list of possible processes. + // + // If the session disconnects from the remote process during its + // lifetime, session calls may fail immediately. + Target string + + // Config is a binary-serialized representation of the + // tensorflow.ConfigProto protocol message + // (https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto). + Config []byte +} + +// c converts the SessionOptions to the C API's TF_SessionOptions. Callers must +// deallocate by calling the returned done() closure. +func (o *SessionOptions) c() (ret *C.TF_SessionOptions, done func(), err error) { + opt := C.TF_NewSessionOptions() + if o == nil { + return opt, func() { C.TF_DeleteSessionOptions(opt) }, nil + } + t := C.CString(o.Target) + C.TF_SetTarget(opt, t) + C.free(unsafe.Pointer(t)) + + var cConfig unsafe.Pointer + if sz := len(o.Config); sz > 0 { + status := newStatus() + // Copying into C-memory is the simplest thing to do in terms + // of memory safety and cgo rules ("C code may not keep a copy + // of a Go pointer after the call returns" from + // https://golang.org/cmd/cgo/#hdr-Passing_pointers). + cConfig = C.CBytes(o.Config) + C.TF_SetConfig(opt, cConfig, C.size_t(sz), status.c) + if err := status.Err(); err != nil { + C.TF_DeleteSessionOptions(opt) + return nil, func() {}, fmt.Errorf("invalid SessionOptions.Config: %v", err) + } + } + return opt, func() { + C.TF_DeleteSessionOptions(opt) + C.free(cConfig) + }, nil +} + +// cRunArgs translates the arguments to Session.Run and PartialRun.Run into +// values suitable for C library calls. +type cRunArgs struct { + feeds []C.TF_Output + feedTensors []*C.TF_Tensor + fetches []C.TF_Output + fetchTensors []*C.TF_Tensor + targets []*C.TF_Operation +} + +func newCRunArgs(feeds map[Output]*Tensor, fetches []Output, targets []*Operation) *cRunArgs { + c := &cRunArgs{ + fetches: make([]C.TF_Output, len(fetches)), + fetchTensors: make([]*C.TF_Tensor, len(fetches)), + targets: make([]*C.TF_Operation, len(targets)), + } + for o, t := range feeds { + c.feeds = append(c.feeds, o.c()) + c.feedTensors = append(c.feedTensors, t.c) + } + for i, o := range fetches { + c.fetches[i] = o.c() + } + for i, t := range targets { + c.targets[i] = t.c + } + return c +} + +func (c *cRunArgs) toGo() []*Tensor { + ret := make([]*Tensor, len(c.fetchTensors)) + for i, ct := range c.fetchTensors { + ret[i] = newTensorFromC(ct) + } + return ret +} + +func ptrOutput(l []C.TF_Output) *C.TF_Output { + if len(l) == 0 { + return nil + } + return &l[0] +} + +func ptrTensor(l []*C.TF_Tensor) **C.TF_Tensor { + if len(l) == 0 { + return nil + } + return &l[0] +} + +func ptrOperation(l []*C.TF_Operation) **C.TF_Operation { + if len(l) == 0 { + return nil + } + return &l[0] +} diff --git a/tensorflow/go/session_test.go b/tensorflow/go/session_test.go new file mode 100644 index 0000000..c9bda00 --- /dev/null +++ b/tensorflow/go/session_test.go @@ -0,0 +1,319 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "reflect" + "testing" +) + +func createTestGraph(t *testing.T, dt DataType) (*Graph, Output, Output) { + g := NewGraph() + inp, err := Placeholder(g, "p1", dt) + if err != nil { + t.Fatalf("Placeholder() for %v: %v", dt, err) + } + out, err := Neg(g, "neg1", inp) + if err != nil { + t.Fatalf("Neg() for %v: %v", dt, err) + } + return g, inp, out +} + +func TestSessionRunNeg(t *testing.T) { + var tests = []struct { + input interface{} + expected interface{} + }{ + {int64(1), int64(-1)}, + {[]float64{-1, -2, 3}, []float64{1, 2, -3}}, + {[][]float32{{1, -2}, {-3, 4}}, [][]float32{{-1, 2}, {3, -4}}}, + } + + for _, test := range tests { + t.Run(fmt.Sprint(test.input), func(t *testing.T) { + t1, err := NewTensor(test.input) + if err != nil { + t.Fatal(err) + } + graph, inp, out := createTestGraph(t, t1.DataType()) + s, err := NewSession(graph, &SessionOptions{}) + if err != nil { + t.Fatal(err) + } + output, err := s.Run(map[Output]*Tensor{inp: t1}, []Output{out}, []*Operation{out.Op}) + if err != nil { + t.Fatal(err) + } + if len(output) != 1 { + t.Fatalf("got %d outputs, want 1", len(output)) + } + val := output[0].Value() + if !reflect.DeepEqual(test.expected, val) { + t.Errorf("got %v, want %v", val, test.expected) + } + if err := s.Close(); err != nil { + t.Error(err) + } + }) + } +} + +func TestSessionRunConcat(t *testing.T) { + // Runs the Concat operation on two matrices: m1 and m2, along the + // first dimension (dim1). + // This tests the use of both Output and OutputList as inputs to the + // Concat operation. + var ( + g = NewGraph() + dim1, _ = Const(g, "dim1", int32(1)) + m1, _ = Const(g, "m1", [][]int64{ + {1, 2, 3}, + {4, 5, 6}, + }) + m2, _ = Const(g, "m2", [][]int64{ + {7, 8, 9}, + {10, 11, 12}, + }) + want = [][]int64{ + {1, 2, 3, 7, 8, 9}, + {4, 5, 6, 10, 11, 12}, + } + ) + concat, err := g.AddOperation(OpSpec{ + Type: "Concat", + Input: []Input{ + dim1, + OutputList{m1, m2}, + }, + }) + if err != nil { + t.Fatal(err) + } + s, err := NewSession(g, &SessionOptions{}) + if err != nil { + t.Fatal(err) + } + output, err := s.Run(nil, []Output{concat.Output(0)}, nil) + if err != nil { + t.Fatal(err) + } + if len(output) != 1 { + t.Fatal(len(output)) + } + if got := output[0].Value(); !reflect.DeepEqual(got, want) { + t.Fatalf("Got %v, want %v", got, want) + } +} + +func TestSessionWithStringTensors(t *testing.T) { + // Construct the graph: + // AsString(StringToHashBucketFast("PleaseHashMe")) Will be much + // prettier if using the ops package, but in this package graphs are + // constructed from first principles. + var ( + g = NewGraph() + feed, _ = Const(g, "input", "PleaseHashMe") + hash, _ = g.AddOperation(OpSpec{ + Type: "StringToHashBucketFast", + Input: []Input{feed}, + Attrs: map[string]interface{}{ + "num_buckets": int64(1 << 32), + }, + }) + str, _ = g.AddOperation(OpSpec{ + Type: "AsString", + Input: []Input{hash.Output(0)}, + }) + ) + s, err := NewSession(g, nil) + if err != nil { + t.Fatal(err) + } + output, err := s.Run(nil, []Output{str.Output(0)}, nil) + if err != nil { + t.Fatal(err) + } + if len(output) != 1 { + t.Fatal(len(output)) + } + got, ok := output[0].Value().(string) + if !ok { + t.Fatalf("Got %T, wanted string", output[0].Value()) + } + if want := "1027741475"; got != want { + t.Fatalf("Got %q, want %q", got, want) + } +} + +func TestConcurrency(t *testing.T) { + tensor, err := NewTensor(int64(1)) + if err != nil { + t.Fatalf("NewTensor(): %v", err) + } + + graph, inp, out := createTestGraph(t, tensor.DataType()) + s, err := NewSession(graph, &SessionOptions{}) + if err != nil { + t.Fatalf("NewSession(): %v", err) + } + for i := 0; i < 100; i++ { + // Session may close before Run() starts, so we don't check the error. + go s.Run(map[Output]*Tensor{inp: tensor}, []Output{out}, []*Operation{out.Op}) + } + if err = s.Close(); err != nil { + t.Errorf("Close() 1: %v", err) + } + if err = s.Close(); err != nil { + t.Errorf("Close() 2: %v", err) + } +} + +func ExamplePartialRun() { + var ( + // Create a graph: a + 2 + 3 + b. + // + // Skipping error handling for brevity of this example. + // The 'op' package can be used to make graph construction code + // with error handling more succinct. + g = NewGraph() + a, _ = Placeholder(g, "a", Int32) + b, _ = Placeholder(g, "b", Int32) + two, _ = Const(g, "Two", int32(2)) + three, _ = Const(g, "Three", int32(3)) + + plus2, _ = Add(g, "plus2", a, two) // a + 2 + plus3, _ = Add(g, "plus3", plus2, three) // (a + 2) + 3 + plusB, _ = Add(g, "plusB", plus3, b) // ((a + 2) + 3) + b + + ) + sess, err := NewSession(g, nil) + if err != nil { + panic(err) + } + defer sess.Close() + + // All the feeds, fetches and targets for subsequent PartialRun.Run + // calls must be provided at setup. + pr, err := sess.NewPartialRun( + []Output{a, b}, + []Output{plus2, plusB}, + []*Operation{plus3.Op}, + ) + if err != nil { + panic(err) + } + + // Feed 'a=1', fetch 'plus2', and compute (but do not fetch) 'plus3'. + // Imagine this to be the forward pass of unsupervised neural network + // training of a robot. + val, _ := NewTensor(int32(1)) + fetches, err := pr.Run( + map[Output]*Tensor{a: val}, + []Output{plus2}, + nil) + if err != nil { + panic(err) + } + v1 := fetches[0].Value().(int32) + + // Now, feed 'b=4', fetch 'plusB=a+2+3+b' + // Imagine this to be the result of actuating the robot to determine + // the error produced by the current state of the neural network. + val, _ = NewTensor(int32(4)) + fetches, err = pr.Run( + map[Output]*Tensor{b: val}, + []Output{plusB}, + nil) + if err != nil { + panic(err) + } + v2 := fetches[0].Value().(int32) + + fmt.Println(v1, v2) + // Output: 3 10 +} + +func TestSessionConfig(t *testing.T) { + // Exercise SessionOptions. + // Arguably, a better API would be for SessionOptions.Config to be the + // type generated by the protocol buffer compiler. But for now, the + // tensorflow package continues to be independent of protocol buffers + // and this test exercises the option since the implementation has a + // nuanced conversion to C types. + // + // Till then, the []byte form of Config here was generated using a toy + // tensorflow Python program: + /* + import tensorflow + c = tensorflow.ConfigProto() + c.intra_op_parallelism_threads = 1 + print c.SerializeToString() + */ + graph := NewGraph() + c, err := Const(graph, "Const", int32(14)) + if err != nil { + t.Fatal(err) + } + opts := SessionOptions{Config: []byte("(\x01")} + s, err := NewSession(graph, &opts) + if err != nil { + t.Fatal(err) + } + output, err := s.Run(nil, []Output{c}, nil) + if err != nil { + t.Fatal(err) + } + if output[0].Value().(int32) != 14 { + t.Fatalf("Got %v, want -1", output[0].Value()) + } +} + +func TestListDevices(t *testing.T) { + s, err := NewSession(NewGraph(), nil) + if err != nil { + t.Fatalf("NewSession(): %v", err) + } + + devices, err := s.ListDevices() + if err != nil { + t.Fatalf("ListDevices(): %v", err) + } + + if len(devices) == 0 { + t.Fatalf("no devices detected") + } +} + +func TestDeviceString(t *testing.T) { + d := Device{Name: "foo", Type: "bar", MemoryLimitBytes: 12345} + got := d.String() + want := "(Device: name \"foo\", type bar, memory limit 12345 bytes)" + if got != want { + t.Errorf("Got \"%s\", want \"%s\"", got, want) + } +} + +func TestDeviceStringNoMemoryLimit(t *testing.T) { + d := Device{Name: "foo", Type: "bar", MemoryLimitBytes: -1} + got := d.String() + want := "(Device: name \"foo\", type bar, no memory limit)" + if got != want { + t.Errorf("Got \"%s\", want \"%s\"", got, want) + } +} diff --git a/tensorflow/go/shape.go b/tensorflow/go/shape.go new file mode 100644 index 0000000..8d000cb --- /dev/null +++ b/tensorflow/go/shape.go @@ -0,0 +1,104 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "strings" +) + +// Shape represents the (possibly partially known) shape of a tensor that will +// be produced by an operation. +// +// The zero-value of a Shape represents a shape with an unknown number of +// dimensions. +type Shape struct { + dims []int64 +} + +// ScalarShape returns a Shape representing a scalar. +func ScalarShape() Shape { + return Shape{dims: make([]int64, 0)} +} + +// MakeShape returns a Shape with the provided size of each dimension. +// +// A value of -1 implies that the size of the corresponding dimension is not +// known. +func MakeShape(shape ...int64) Shape { + cpy := make([]int64, len(shape)) + copy(cpy, shape) + return Shape{dims: cpy} +} + +// NumDimensions returns the number of dimensions represented by s, or -1 if +// unknown. +func (s Shape) NumDimensions() int { + if s.dims == nil { + return -1 + } + return len(s.dims) +} + +// Size returns the size of the dim-th dimension of the shape, or -1 if it +// is unknown. +// +// REQUIRES: 0 <= dim < s.NumDimensions() +func (s Shape) Size(dim int) int64 { + if dim < 0 || dim >= s.NumDimensions() { + return -1 + } + return s.dims[dim] +} + +// IsFullySpecified returns true iff the size of all the dimensions of s are +// known. +func (s Shape) IsFullySpecified() bool { + if s.dims == nil { + return false + } + for _, size := range s.dims { + if size <= 1 { + return false + } + } + return true +} + +// ToSlice returns the (possibly partially known) shape represented by s as a +// slice, or an error if the number of dimensions is not known. +func (s Shape) ToSlice() ([]int64, error) { + if s.dims == nil { + return nil, fmt.Errorf("cannot create a slice for a Shape with an unknown number of dimensions") + } + cpy := make([]int64, len(s.dims)) + copy(cpy, s.dims) + return cpy, nil +} + +func (s Shape) String() string { + if s.dims == nil { + return "?" + } + ret := fmt.Sprint(s.dims) + for _, size := range s.dims { + if size < 0 { + ret = strings.Replace(ret, fmt.Sprint(size), "?", 1) + } + } + return strings.Replace(ret, " ", ", ", -1) +} diff --git a/tensorflow/go/shape_test.go b/tensorflow/go/shape_test.go new file mode 100644 index 0000000..94ffd27 --- /dev/null +++ b/tensorflow/go/shape_test.go @@ -0,0 +1,85 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "reflect" + "testing" +) + +func TestShape(t *testing.T) { + tests := []struct { + shape Shape + slice []int64 + full bool + str string + }{ + { + shape: ScalarShape(), + slice: make([]int64, 0), + full: true, + str: "[]", + }, + { + shape: MakeShape(-1, 2, -1, 4), + slice: []int64{-1, 2, -1, 4}, + full: false, + str: "[?, 2, ?, 4]", + }, + { + shape: MakeShape(2, 3), + slice: []int64{2, 3}, + full: true, + str: "[2, 3]", + }, + } + for _, test := range tests { + t.Run(fmt.Sprintf("%#v", test.shape), func(t *testing.T) { + if got, want := test.shape.NumDimensions(), len(test.slice); got != want { + t.Errorf("Got %v, want %v", got, want) + } + if gotSlice, err := test.shape.ToSlice(); err != nil || !reflect.DeepEqual(gotSlice, test.slice) { + t.Errorf("Got (%#v, %v), want (%#v, nil)", gotSlice, err, test.slice) + } + if got, want := test.shape.IsFullySpecified(), test.full; got != want { + t.Errorf("Got %v, want %v", got, want) + } + if got, want := test.shape.String(), test.str; got != want { + t.Errorf("Got %v, want %v", got, want) + } + }) + } + +} + +func TestZeroShape(t *testing.T) { + var s Shape + if s.NumDimensions() != -1 { + t.Error(s.NumDimensions()) + } + if _, err := s.ToSlice(); err == nil { + t.Error("ToSlice() on a Shape of unknown number of dimensions should fail") + } + if s.IsFullySpecified() { + t.Error("Shape of unknown number of dimensions should not be fully specified") + } + if got, want := s.String(), "?"; got != want { + t.Errorf("Got %q, want %q", got, want) + } + +} diff --git a/tensorflow/go/signature.go b/tensorflow/go/signature.go new file mode 100644 index 0000000..c2db0c7 --- /dev/null +++ b/tensorflow/go/signature.go @@ -0,0 +1,119 @@ +/* +Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import corepb "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto" + +// #include "tensorflow/c/c_api.h" +import "C" + +// A Signature defines the signature of a computation supported by a TensorFlow +// graph. +// +// For example, a model with two loss computations, sharing a single input, +// might have the following signature_def map. +// +// Note that across the two Signatures "loss_A" and "loss_B", the input key, +// output key, and method_name are identical, and will be used by system(s) that +// implement or rely upon this particular loss method. The output tensor names +// differ, demonstrating how different outputs can exist for the same method. +// +// signature_def { +// key: "loss_A" +// value { +// inputs { +// key: "input" +// value { +// name: "input:0" +// dtype: DT_STRING +// tensor_shape: ... +// } +// } +// outputs { +// key: "loss_output" +// value { +// name: "loss_output_A:0" +// dtype: DT_FLOAT +// tensor_shape: ... +// } +// } +// } +// ... +// method_name: "some/package/compute_loss" +// } +// signature_def { +// key: "loss_B" +// value { +// inputs { +// key: "input" +// value { +// name: "input:0" +// dtype: DT_STRING +// tensor_shape: ... +// } +// } +// outputs { +// key: "loss_output" +// value { +// name: "loss_output_B:0" +// dtype: DT_FLOAT +// tensor_shape: ... +// } +// } +// } +// ... +// method_name: "some/package/compute_loss" +// } +type Signature struct { + Inputs, Outputs map[string]TensorInfo + MethodName string +} + +// A TensorInfo contains the information about a Tensor necessary for feeding or retrieval. +type TensorInfo struct { + Name string + DType DataType + Shape Shape +} + +func signatureDefFromProto(pb *corepb.SignatureDef) Signature { + inputs := make(map[string]TensorInfo) + for name, input := range pb.GetInputs() { + inputs[name] = tensorInfoFromProto(input) + } + outputs := make(map[string]TensorInfo) + for name, output := range pb.GetOutputs() { + outputs[name] = tensorInfoFromProto(output) + } + return Signature{ + Inputs: inputs, + Outputs: outputs, + MethodName: pb.GetMethodName(), + } +} + +func tensorInfoFromProto(pb *corepb.TensorInfo) TensorInfo { + var dims []int64 + for _, d := range pb.GetTensorShape().GetDim() { + dims = append(dims, d.GetSize()) + } + return TensorInfo{ + Name: pb.GetName(), + DType: DataType(C.TF_DataType(pb.GetDtype())), + Shape: MakeShape(dims...), + } +} diff --git a/tensorflow/go/signature_test.go b/tensorflow/go/signature_test.go new file mode 100644 index 0000000..f9fa842 --- /dev/null +++ b/tensorflow/go/signature_test.go @@ -0,0 +1,207 @@ +/* +Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "fmt" + "testing" + + tspb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto" + typb "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto" + corepb "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto" +) + +func TestSignatureFromProto(t *testing.T) { + got := signatureDefFromProto(&corepb.SignatureDef{ + Inputs: map[string]*corepb.TensorInfo{ + "input_1": &corepb.TensorInfo{ + Encoding: &corepb.TensorInfo_Name{ + Name: "tensor_1", + }, + Dtype: typb.DataType_DT_INT8, + TensorShape: &tspb.TensorShapeProto{ + Dim: []*tspb.TensorShapeProto_Dim{ + {Size: 1}, + {Size: 2}, + {Size: 3}, + }, + }, + }, + "input_2": &corepb.TensorInfo{ + Encoding: &corepb.TensorInfo_Name{ + Name: "tensor_2", + }, + Dtype: typb.DataType_DT_FLOAT, + TensorShape: &tspb.TensorShapeProto{ + Dim: []*tspb.TensorShapeProto_Dim{ + {Size: 4}, + {Size: 5}, + {Size: 6}, + }, + }, + }, + }, + Outputs: map[string]*corepb.TensorInfo{ + "output_1": &corepb.TensorInfo{ + Encoding: &corepb.TensorInfo_Name{ + Name: "tensor_3", + }, + Dtype: typb.DataType_DT_STRING, + TensorShape: &tspb.TensorShapeProto{ + Dim: []*tspb.TensorShapeProto_Dim{ + {Size: 1}, + {Size: 2}, + {Size: 3}, + }, + }, + }, + "output_2": &corepb.TensorInfo{ + Encoding: &corepb.TensorInfo_Name{ + Name: "tensor_4", + }, + Dtype: typb.DataType_DT_BOOL, + TensorShape: &tspb.TensorShapeProto{ + Dim: []*tspb.TensorShapeProto_Dim{ + {Size: 4}, + {Size: 5}, + {Size: 6}, + }, + }, + }, + }, + MethodName: "method", + }) + + want := Signature{ + Inputs: map[string]TensorInfo{ + "input_1": TensorInfo{ + Name: "tensor_1", + DType: Int8, + Shape: MakeShape(1, 2, 3), + }, + "input_2": TensorInfo{ + Name: "tensor_2", + DType: Float, + Shape: MakeShape(4, 5, 6), + }, + }, + Outputs: map[string]TensorInfo{ + "output_1": TensorInfo{ + Name: "tensor_3", + DType: String, + Shape: MakeShape(1, 2, 3), + }, + "output_2": TensorInfo{ + Name: "tensor_4", + DType: Bool, + Shape: MakeShape(4, 5, 6), + }, + }, + MethodName: "method", + } + + for k, input := range want.Inputs { + diff, err := diffTensorInfos(got.Inputs[k], input) + if err != nil { + t.Fatalf("Signature.Inputs[%s]: unable to diff TensorInfos: %v", k, err) + } + if diff != "" { + t.Errorf("Signature.Inputs[%s] diff:\n%s", k, diff) + } + } + + for k, output := range want.Outputs { + diff, err := diffTensorInfos(got.Outputs[k], output) + if err != nil { + t.Fatalf("Signature.Outputs[%s]: unable to diff TensorInfos: %v", k, err) + } + if diff != "" { + t.Errorf("Signature.Outputs[%s] diff:\n%s", k, diff) + } + } + + if got.MethodName != want.MethodName { + t.Errorf("Signature.MethodName: got %q, want %q", got.MethodName, want.MethodName) + } +} + +func TestTensorInfoFromProto(t *testing.T) { + got := tensorInfoFromProto(&corepb.TensorInfo{ + Encoding: &corepb.TensorInfo_Name{ + Name: "tensor", + }, + Dtype: typb.DataType_DT_INT8, + TensorShape: &tspb.TensorShapeProto{ + Dim: []*tspb.TensorShapeProto_Dim{ + {Size: 1}, + {Size: 2}, + {Size: 3}, + }, + }, + }) + want := TensorInfo{ + Name: "tensor", + DType: Int8, + Shape: MakeShape(1, 2, 3), + } + + diff, err := diffTensorInfos(got, want) + if err != nil { + t.Fatalf("Unable to diff TensorInfos: %v", err) + } + if diff != "" { + t.Errorf("tensorInfoFromProto produced a diff (got -> want): %s", diff) + } +} + +func diffTensorInfos(a, b TensorInfo) (string, error) { + diff := "" + if a.Name != b.Name { + diff += fmt.Sprintf("Name: %q -> %q\n", a.Name, b.Name) + } + if a.DType != b.DType { + diff += fmt.Sprintf("DType: %v -> %v\n", a.DType, b.DType) + } + + aShape, err := a.Shape.ToSlice() + if err != nil { + return "", err + } + bShape, err := b.Shape.ToSlice() + if err != nil { + return "", err + } + shapeLen := len(aShape) + if len(bShape) > shapeLen { + shapeLen = len(bShape) + } + for i := 0; i < shapeLen; i++ { + if i >= len(aShape) { + diff += fmt.Sprintf("+Shape[%d]: %d\n", i, bShape[i]) + continue + } + if i >= len(bShape) { + diff += fmt.Sprintf("-Shape[%d]: %d\n", i, aShape[i]) + continue + } + if aShape[i] != bShape[i] { + diff += fmt.Sprintf("Shape[%d]: %d -> %d\n", i, aShape[i], bShape[i]) + } + } + + return diff, nil +} diff --git a/tensorflow/go/status.go b/tensorflow/go/status.go new file mode 100644 index 0000000..b4df836 --- /dev/null +++ b/tensorflow/go/status.go @@ -0,0 +1,67 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include "tensorflow/c/c_api.h" +import "C" + +import "runtime" + +type code C.TF_Code + +// status holds error information returned by TensorFlow. We convert all +// TF statuses to Go errors. +type status struct { + c *C.TF_Status +} + +func newStatus() *status { + s := &status{C.TF_NewStatus()} + runtime.SetFinalizer(s, (*status).finalizer) + return s +} + +func (s *status) finalizer() { + C.TF_DeleteStatus(s.c) +} + +func (s *status) Code() code { + return code(C.TF_GetCode(s.c)) +} + +func (s *status) String() string { + return C.GoString(C.TF_Message(s.c)) +} + +// Err converts the status to a Go error and returns nil if the status is OK. +func (s *status) Err() error { + if s == nil || s.Code() == C.TF_OK { + return nil + } + return (*statusError)(s) +} + +// statusError is distinct from status because it fulfills the error interface. +// status itself may have a TF_OK code and is not always considered an error. +// +// TODO(jhseu): Make public, rename to Error, and provide a way for users to +// check status codes. +type statusError status + +func (s *statusError) Error() string { + return (*status)(s).String() +} diff --git a/tensorflow/go/stream_executor/dnn.pb.go b/tensorflow/go/stream_executor/dnn.pb.go new file mode 100644 index 0000000..d605b08 --- /dev/null +++ b/tensorflow/go/stream_executor/dnn.pb.go @@ -0,0 +1,588 @@ +// Code generated by protoc-gen-go. DO NOT EDIT. +// source: tensorflow/stream_executor/dnn.proto + +package stream_executor + +import ( + fmt "fmt" + proto "github.com/golang/protobuf/proto" + math "math" +) + +// Reference imports to suppress errors if they are not otherwise used. +var _ = proto.Marshal +var _ = fmt.Errorf +var _ = math.Inf + +// This is a compile-time assertion to ensure that this generated file +// is compatible with the proto package it is being compiled against. +// A compilation error at this line likely means your copy of the +// proto package needs to be updated. +const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package + +// Specifies the data type used by an operation. +type DataType int32 + +const ( + DataType_kFloat DataType = 0 + DataType_kDouble DataType = 1 + DataType_kHalf DataType = 2 + DataType_kInt8 DataType = 3 + DataType_kInt32 DataType = 4 + DataType_kComplexFloat DataType = 5 + DataType_kComplexDouble DataType = 6 +) + +var DataType_name = map[int32]string{ + 0: "kFloat", + 1: "kDouble", + 2: "kHalf", + 3: "kInt8", + 4: "kInt32", + 5: "kComplexFloat", + 6: "kComplexDouble", +} + +var DataType_value = map[string]int32{ + "kFloat": 0, + "kDouble": 1, + "kHalf": 2, + "kInt8": 3, + "kInt32": 4, + "kComplexFloat": 5, + "kComplexDouble": 6, +} + +func (x DataType) String() string { + return proto.EnumName(DataType_name, int32(x)) +} + +func (DataType) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{0} +} + +// Describes how a convolution input or output layer's data is formatted. +type DataLayout int32 + +const ( + // Naming convention: + // Y <-> row or height + // X <-> column or width + // Batch <-> batch, or N + // Depth <-> feature, or channel + // TODO(timshen): turn them into cuDNN names, e.g. kNCHW. + DataLayout_kYXDepthBatch DataLayout = 0 + DataLayout_kYXBatchDepth DataLayout = 1 + DataLayout_kBatchYXDepth DataLayout = 2 + DataLayout_kBatchDepthYX DataLayout = 3 + DataLayout_kBatchDepthYX4 DataLayout = 4 +) + +var DataLayout_name = map[int32]string{ + 0: "kYXDepthBatch", + 1: "kYXBatchDepth", + 2: "kBatchYXDepth", + 3: "kBatchDepthYX", + 4: "kBatchDepthYX4", +} + +var DataLayout_value = map[string]int32{ + "kYXDepthBatch": 0, + "kYXBatchDepth": 1, + "kBatchYXDepth": 2, + "kBatchDepthYX": 3, + "kBatchDepthYX4": 4, +} + +func (x DataLayout) String() string { + return proto.EnumName(DataLayout_name, int32(x)) +} + +func (DataLayout) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{1} +} + +// Describes how a convolution filter is laid out in the memory. +type FilterLayout int32 + +const ( + // Naming convention: + // Y <-> row or height + // X <-> column or width + // Output <-> output feature, or N + // Input <-> input feature, or N + // TODO(timshen): turn them into cuDNN names, e.g. kNCHW. + FilterLayout_kOutputInputYX FilterLayout = 0 + FilterLayout_kOutputYXInput FilterLayout = 1 + FilterLayout_kOutputInputYX4 FilterLayout = 2 + FilterLayout_kInputYXOutput FilterLayout = 3 + FilterLayout_kYXInputOutput FilterLayout = 4 +) + +var FilterLayout_name = map[int32]string{ + 0: "kOutputInputYX", + 1: "kOutputYXInput", + 2: "kOutputInputYX4", + 3: "kInputYXOutput", + 4: "kYXInputOutput", +} + +var FilterLayout_value = map[string]int32{ + "kOutputInputYX": 0, + "kOutputYXInput": 1, + "kOutputInputYX4": 2, + "kInputYXOutput": 3, + "kYXInputOutput": 4, +} + +func (x FilterLayout) String() string { + return proto.EnumName(FilterLayout_name, int32(x)) +} + +func (FilterLayout) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{2} +} + +// Describes a kind of non-linearity (threshold-like mathematical function). +type ActivationMode int32 + +const ( + ActivationMode_kNone ActivationMode = 0 + ActivationMode_kSigmoid ActivationMode = 1 + // Rectified linear activation: f(x) = x < 0 ? 0 : x + ActivationMode_kRelu ActivationMode = 2 + // Rectified linear activation; where upper maximum is 6.0. + ActivationMode_kRelu6 ActivationMode = 3 + // Rectified linear activation; where upper maximum specified by + // BatchDescriptor::value_max(). + ActivationMode_kReluX ActivationMode = 4 + ActivationMode_kTanh ActivationMode = 5 + // Like ReluX; but passes all values in the range [-X,X]. + ActivationMode_kBandPass ActivationMode = 6 +) + +var ActivationMode_name = map[int32]string{ + 0: "kNone", + 1: "kSigmoid", + 2: "kRelu", + 3: "kRelu6", + 4: "kReluX", + 5: "kTanh", + 6: "kBandPass", +} + +var ActivationMode_value = map[string]int32{ + "kNone": 0, + "kSigmoid": 1, + "kRelu": 2, + "kRelu6": 3, + "kReluX": 4, + "kTanh": 5, + "kBandPass": 6, +} + +func (x ActivationMode) String() string { + return proto.EnumName(ActivationMode_name, int32(x)) +} + +func (ActivationMode) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{3} +} + +// Describe the math definition for the conv op. The popular behavior is +// actually called cross-correlation in math, despite the operation is often +// referred as convolution. See cuDNN cudnnConvolutionMode_t. +type ConvolutionMode int32 + +const ( + ConvolutionMode_CROSS_CORRELATION ConvolutionMode = 0 + ConvolutionMode_CONVOLUTION ConvolutionMode = 1 +) + +var ConvolutionMode_name = map[int32]string{ + 0: "CROSS_CORRELATION", + 1: "CONVOLUTION", +} + +var ConvolutionMode_value = map[string]int32{ + "CROSS_CORRELATION": 0, + "CONVOLUTION": 1, +} + +func (x ConvolutionMode) String() string { + return proto.EnumName(ConvolutionMode_name, int32(x)) +} + +func (ConvolutionMode) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{4} +} + +type ConvolutionKind int32 + +const ( + ConvolutionKind_INVALID ConvolutionKind = 0 + ConvolutionKind_FORWARD ConvolutionKind = 1 + ConvolutionKind_BACKWARD_FILTER ConvolutionKind = 2 + ConvolutionKind_BACKWARD_DATA ConvolutionKind = 3 + ConvolutionKind_FORWARD_BIAS_ACTIVATION ConvolutionKind = 4 +) + +var ConvolutionKind_name = map[int32]string{ + 0: "INVALID", + 1: "FORWARD", + 2: "BACKWARD_FILTER", + 3: "BACKWARD_DATA", + 4: "FORWARD_BIAS_ACTIVATION", +} + +var ConvolutionKind_value = map[string]int32{ + "INVALID": 0, + "FORWARD": 1, + "BACKWARD_FILTER": 2, + "BACKWARD_DATA": 3, + "FORWARD_BIAS_ACTIVATION": 4, +} + +func (x ConvolutionKind) String() string { + return proto.EnumName(ConvolutionKind_name, int32(x)) +} + +func (ConvolutionKind) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{5} +} + +type AlgorithmProto_MathType int32 + +const ( + AlgorithmProto_DEFAULT_MATH AlgorithmProto_MathType = 0 + // The GPU may operate 4x4 matrix FMA. + // See cuDNN's documentation for CUDNN_TENSOR_OP_MATH. + AlgorithmProto_TENSOR_OP_MATH AlgorithmProto_MathType = 1 +) + +var AlgorithmProto_MathType_name = map[int32]string{ + 0: "DEFAULT_MATH", + 1: "TENSOR_OP_MATH", +} + +var AlgorithmProto_MathType_value = map[string]int32{ + "DEFAULT_MATH": 0, + "TENSOR_OP_MATH": 1, +} + +func (x AlgorithmProto_MathType) String() string { + return proto.EnumName(AlgorithmProto_MathType_name, int32(x)) +} + +func (AlgorithmProto_MathType) EnumDescriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{1, 0} +} + +// Generic tensor representation. +type TensorDescriptorProto struct { + Dimensions []int64 `protobuf:"varint,1,rep,packed,name=dimensions,proto3" json:"dimensions,omitempty"` + DataType DataType `protobuf:"varint,2,opt,name=data_type,json=dataType,proto3,enum=stream_executor.dnn.DataType" json:"data_type,omitempty"` + // Types that are valid to be assigned to LayoutOneof: + // *TensorDescriptorProto_DataLayout + // *TensorDescriptorProto_FilterLayout + LayoutOneof isTensorDescriptorProto_LayoutOneof `protobuf_oneof:"layout_oneof"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *TensorDescriptorProto) Reset() { *m = TensorDescriptorProto{} } +func (m *TensorDescriptorProto) String() string { return proto.CompactTextString(m) } +func (*TensorDescriptorProto) ProtoMessage() {} +func (*TensorDescriptorProto) Descriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{0} +} + +func (m *TensorDescriptorProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_TensorDescriptorProto.Unmarshal(m, b) +} +func (m *TensorDescriptorProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_TensorDescriptorProto.Marshal(b, m, deterministic) +} +func (m *TensorDescriptorProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_TensorDescriptorProto.Merge(m, src) +} +func (m *TensorDescriptorProto) XXX_Size() int { + return xxx_messageInfo_TensorDescriptorProto.Size(m) +} +func (m *TensorDescriptorProto) XXX_DiscardUnknown() { + xxx_messageInfo_TensorDescriptorProto.DiscardUnknown(m) +} + +var xxx_messageInfo_TensorDescriptorProto proto.InternalMessageInfo + +func (m *TensorDescriptorProto) GetDimensions() []int64 { + if m != nil { + return m.Dimensions + } + return nil +} + +func (m *TensorDescriptorProto) GetDataType() DataType { + if m != nil { + return m.DataType + } + return DataType_kFloat +} + +type isTensorDescriptorProto_LayoutOneof interface { + isTensorDescriptorProto_LayoutOneof() +} + +type TensorDescriptorProto_DataLayout struct { + DataLayout DataLayout `protobuf:"varint,3,opt,name=data_layout,json=dataLayout,proto3,enum=stream_executor.dnn.DataLayout,oneof"` +} + +type TensorDescriptorProto_FilterLayout struct { + FilterLayout FilterLayout `protobuf:"varint,4,opt,name=filter_layout,json=filterLayout,proto3,enum=stream_executor.dnn.FilterLayout,oneof"` +} + +func (*TensorDescriptorProto_DataLayout) isTensorDescriptorProto_LayoutOneof() {} + +func (*TensorDescriptorProto_FilterLayout) isTensorDescriptorProto_LayoutOneof() {} + +func (m *TensorDescriptorProto) GetLayoutOneof() isTensorDescriptorProto_LayoutOneof { + if m != nil { + return m.LayoutOneof + } + return nil +} + +func (m *TensorDescriptorProto) GetDataLayout() DataLayout { + if x, ok := m.GetLayoutOneof().(*TensorDescriptorProto_DataLayout); ok { + return x.DataLayout + } + return DataLayout_kYXDepthBatch +} + +func (m *TensorDescriptorProto) GetFilterLayout() FilterLayout { + if x, ok := m.GetLayoutOneof().(*TensorDescriptorProto_FilterLayout); ok { + return x.FilterLayout + } + return FilterLayout_kOutputInputYX +} + +// XXX_OneofWrappers is for the internal use of the proto package. +func (*TensorDescriptorProto) XXX_OneofWrappers() []interface{} { + return []interface{}{ + (*TensorDescriptorProto_DataLayout)(nil), + (*TensorDescriptorProto_FilterLayout)(nil), + } +} + +// Generic algorithm representation. +type AlgorithmProto struct { + AlgoId int64 `protobuf:"varint,1,opt,name=algo_id,json=algoId,proto3" json:"algo_id,omitempty"` + MathType AlgorithmProto_MathType `protobuf:"varint,2,opt,name=math_type,json=mathType,proto3,enum=stream_executor.dnn.AlgorithmProto_MathType" json:"math_type,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *AlgorithmProto) Reset() { *m = AlgorithmProto{} } +func (m *AlgorithmProto) String() string { return proto.CompactTextString(m) } +func (*AlgorithmProto) ProtoMessage() {} +func (*AlgorithmProto) Descriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{1} +} + +func (m *AlgorithmProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_AlgorithmProto.Unmarshal(m, b) +} +func (m *AlgorithmProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_AlgorithmProto.Marshal(b, m, deterministic) +} +func (m *AlgorithmProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_AlgorithmProto.Merge(m, src) +} +func (m *AlgorithmProto) XXX_Size() int { + return xxx_messageInfo_AlgorithmProto.Size(m) +} +func (m *AlgorithmProto) XXX_DiscardUnknown() { + xxx_messageInfo_AlgorithmProto.DiscardUnknown(m) +} + +var xxx_messageInfo_AlgorithmProto proto.InternalMessageInfo + +func (m *AlgorithmProto) GetAlgoId() int64 { + if m != nil { + return m.AlgoId + } + return 0 +} + +func (m *AlgorithmProto) GetMathType() AlgorithmProto_MathType { + if m != nil { + return m.MathType + } + return AlgorithmProto_DEFAULT_MATH +} + +// Convolution-specific parameters. +type ConvolutionDescriptorProto struct { + Paddings []int64 `protobuf:"varint,1,rep,packed,name=paddings,proto3" json:"paddings,omitempty"` + Strides []int64 `protobuf:"varint,2,rep,packed,name=strides,proto3" json:"strides,omitempty"` + Dilations []int64 `protobuf:"varint,3,rep,packed,name=dilations,proto3" json:"dilations,omitempty"` + // The "accumulator" type. For example, use F32 as an accumulator for F16 + // convolutions. + // See cuDNN's cudnnConvolutionMode_t. + ComputeMode DataType `protobuf:"varint,4,opt,name=compute_mode,json=computeMode,proto3,enum=stream_executor.dnn.DataType" json:"compute_mode,omitempty"` + // See cuDNN's group count. + GroupCount int32 `protobuf:"varint,5,opt,name=group_count,json=groupCount,proto3" json:"group_count,omitempty"` + ConvolutionMode ConvolutionMode `protobuf:"varint,6,opt,name=convolution_mode,json=convolutionMode,proto3,enum=stream_executor.dnn.ConvolutionMode" json:"convolution_mode,omitempty"` + // Tensorflow node name, same as in NodeDef, for debugging purposes. + Name string `protobuf:"bytes,7,opt,name=name,proto3" json:"name,omitempty"` + XXX_NoUnkeyedLiteral struct{} `json:"-"` + XXX_unrecognized []byte `json:"-"` + XXX_sizecache int32 `json:"-"` +} + +func (m *ConvolutionDescriptorProto) Reset() { *m = ConvolutionDescriptorProto{} } +func (m *ConvolutionDescriptorProto) String() string { return proto.CompactTextString(m) } +func (*ConvolutionDescriptorProto) ProtoMessage() {} +func (*ConvolutionDescriptorProto) Descriptor() ([]byte, []int) { + return fileDescriptor_768c61f2a579ee6a, []int{2} +} + +func (m *ConvolutionDescriptorProto) XXX_Unmarshal(b []byte) error { + return xxx_messageInfo_ConvolutionDescriptorProto.Unmarshal(m, b) +} +func (m *ConvolutionDescriptorProto) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { + return xxx_messageInfo_ConvolutionDescriptorProto.Marshal(b, m, deterministic) +} +func (m *ConvolutionDescriptorProto) XXX_Merge(src proto.Message) { + xxx_messageInfo_ConvolutionDescriptorProto.Merge(m, src) +} +func (m *ConvolutionDescriptorProto) XXX_Size() int { + return xxx_messageInfo_ConvolutionDescriptorProto.Size(m) +} +func (m *ConvolutionDescriptorProto) XXX_DiscardUnknown() { + xxx_messageInfo_ConvolutionDescriptorProto.DiscardUnknown(m) +} + +var xxx_messageInfo_ConvolutionDescriptorProto proto.InternalMessageInfo + +func (m *ConvolutionDescriptorProto) GetPaddings() []int64 { + if m != nil { + return m.Paddings + } + return nil +} + +func (m *ConvolutionDescriptorProto) GetStrides() []int64 { + if m != nil { + return m.Strides + } + return nil +} + +func (m *ConvolutionDescriptorProto) GetDilations() []int64 { + if m != nil { + return m.Dilations + } + return nil +} + +func (m *ConvolutionDescriptorProto) GetComputeMode() DataType { + if m != nil { + return m.ComputeMode + } + return DataType_kFloat +} + +func (m *ConvolutionDescriptorProto) GetGroupCount() int32 { + if m != nil { + return m.GroupCount + } + return 0 +} + +func (m *ConvolutionDescriptorProto) GetConvolutionMode() ConvolutionMode { + if m != nil { + return m.ConvolutionMode + } + return ConvolutionMode_CROSS_CORRELATION +} + +func (m *ConvolutionDescriptorProto) GetName() string { + if m != nil { + return m.Name + } + return "" +} + +func init() { + proto.RegisterEnum("stream_executor.dnn.DataType", DataType_name, DataType_value) + proto.RegisterEnum("stream_executor.dnn.DataLayout", DataLayout_name, DataLayout_value) + proto.RegisterEnum("stream_executor.dnn.FilterLayout", FilterLayout_name, FilterLayout_value) + proto.RegisterEnum("stream_executor.dnn.ActivationMode", ActivationMode_name, ActivationMode_value) + proto.RegisterEnum("stream_executor.dnn.ConvolutionMode", ConvolutionMode_name, ConvolutionMode_value) + proto.RegisterEnum("stream_executor.dnn.ConvolutionKind", ConvolutionKind_name, ConvolutionKind_value) + proto.RegisterEnum("stream_executor.dnn.AlgorithmProto_MathType", AlgorithmProto_MathType_name, AlgorithmProto_MathType_value) + proto.RegisterType((*TensorDescriptorProto)(nil), "stream_executor.dnn.TensorDescriptorProto") + proto.RegisterType((*AlgorithmProto)(nil), "stream_executor.dnn.AlgorithmProto") + proto.RegisterType((*ConvolutionDescriptorProto)(nil), "stream_executor.dnn.ConvolutionDescriptorProto") +} + +func init() { + proto.RegisterFile("tensorflow/stream_executor/dnn.proto", fileDescriptor_768c61f2a579ee6a) +} + +var fileDescriptor_768c61f2a579ee6a = []byte{ + // 799 bytes of a gzipped FileDescriptorProto + 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0x84, 0x94, 0xdd, 0x8e, 0xda, 0x46, + 0x14, 0xc7, 0x31, 0x5f, 0x0b, 0x07, 0x96, 0x9d, 0x4c, 0x14, 0x05, 0xa5, 0x1f, 0xa1, 0xab, 0x5c, + 0x20, 0x54, 0xb1, 0x55, 0x12, 0x55, 0x6d, 0x2f, 0xaa, 0x18, 0xbc, 0x68, 0xad, 0xb0, 0x78, 0x65, + 0x9c, 0x2d, 0xdb, 0x1b, 0x6b, 0xd6, 0x33, 0x80, 0x85, 0x3d, 0x63, 0xd9, 0xe3, 0x34, 0xfb, 0x18, + 0x7d, 0x89, 0x3e, 0x43, 0x1f, 0xaf, 0xf2, 0xd8, 0xb0, 0x2c, 0xa2, 0xca, 0xdd, 0x39, 0xbf, 0x39, + 0x1f, 0xf6, 0xff, 0x3f, 0x1a, 0x78, 0x23, 0x19, 0x4f, 0x44, 0xbc, 0x0c, 0xc4, 0x5f, 0x17, 0x89, + 0x8c, 0x19, 0x09, 0x5d, 0xf6, 0x85, 0x79, 0xa9, 0x14, 0xf1, 0x05, 0xe5, 0x7c, 0x18, 0xc5, 0x42, + 0x0a, 0xfc, 0xfc, 0xe0, 0x68, 0x48, 0x39, 0x3f, 0xff, 0xbb, 0x0c, 0x2f, 0x1c, 0xd5, 0x6d, 0xb0, + 0xc4, 0x8b, 0xfd, 0x48, 0x8a, 0xf8, 0x46, 0x95, 0x7f, 0x0f, 0x40, 0xfd, 0x90, 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0xb7, 0xe0, 0x64, 0x63, 0x88, 0xf4, 0x3e, 0x60, 0x48, 0xc3, 0x4d, 0xa8, 0x6d, + 0xae, 0x48, 0xb0, 0x44, 0x65, 0x15, 0x9a, 0x5c, 0xfe, 0x82, 0x2a, 0xaa, 0xdc, 0xe4, 0xf2, 0xdd, + 0x5b, 0x54, 0xc5, 0xcf, 0xe0, 0x74, 0x33, 0x16, 0x61, 0x14, 0xb0, 0x2f, 0xf9, 0x84, 0x5a, 0x66, + 0xd4, 0x16, 0x15, 0x83, 0xea, 0x83, 0x00, 0xe0, 0xf1, 0x1e, 0xab, 0xa6, 0xbb, 0x85, 0xc1, 0x22, + 0xb9, 0x1e, 0x11, 0xe9, 0xad, 0x51, 0xa9, 0x40, 0x2a, 0x53, 0x1c, 0x69, 0x0a, 0x29, 0x50, 0x94, + 0xa2, 0xf2, 0x23, 0x52, 0xe0, 0x6e, 0x81, 0x2a, 0x6a, 0xdb, 0x3e, 0x7a, 0x8f, 0xaa, 0x83, 0x04, + 0xda, 0xfb, 0xf7, 0x5d, 0xd5, 0x58, 0xa9, 0x8c, 0x52, 0x69, 0xf2, 0x28, 0x95, 0x77, 0x8b, 0xfc, + 0x3a, 0x15, 0xec, 0x6e, 0xa1, 0x28, 0xd2, 0xf0, 0x73, 0x38, 0x7b, 0x5a, 0xf7, 0x1e, 0x95, 0x55, + 0x61, 0x91, 0xe6, 0x67, 0xc5, 0xd2, 0xa2, 0xad, 0x60, 0xd5, 0xc1, 0x12, 0x3a, 0xba, 0x27, 0xfd, + 0xcf, 0x64, 0x27, 0x7b, 0x26, 0xd9, 0x4c, 0x70, 0x86, 0x4a, 0xb8, 0x0d, 0x8d, 0xcd, 0xdc, 0x5f, + 0x85, 0xc2, 0xa7, 0x85, 0xac, 0x36, 0x0b, 0x52, 0x54, 0x56, 0x5a, 0x66, 0xe1, 0xcf, 0x85, 0xae, + 0x59, 0xbc, 0x40, 0x55, 0x55, 0xe2, 0x10, 0xbe, 0x46, 0x35, 0x7c, 0x0a, 0xcd, 0xcd, 0x88, 0x70, + 0x7a, 0x43, 0x92, 0x04, 0xd5, 0x07, 0xbf, 0xc2, 0xd9, 0x81, 0xe1, 0xf8, 0x05, 0x3c, 0x1b, 0xdb, + 0xd6, 0x7c, 0xee, 0x8e, 0x2d, 0xdb, 0xbe, 0x9c, 0xea, 0x8e, 0x69, 0xcd, 0x50, 0x09, 0x9f, 0x41, + 0x6b, 0x6c, 0xcd, 0x6e, 0xad, 0xe9, 0x27, 0x05, 0xb4, 0x41, 0xf4, 0xa4, 0xf5, 0xa3, 0xcf, 0x69, + 0x66, 0xb7, 0x39, 0xbb, 0xd5, 0xa7, 0xa6, 0x91, 0x7b, 0x3f, 0xb1, 0xec, 0x3f, 0x74, 0xdb, 0xc8, + 0xc5, 0x18, 0xe9, 0xe3, 0x8f, 0x59, 0xe6, 0x4e, 0xcc, 0xa9, 0x73, 0x69, 0xe7, 0x06, 0xec, 0xa0, + 0xa1, 0x3b, 0x3a, 0xaa, 0xe0, 0x6f, 0xe0, 0x65, 0xd1, 0xe4, 0x8e, 0x4c, 0x7d, 0xee, 0xea, 0x63, + 0xc7, 0xbc, 0xcd, 0x3f, 0xa1, 0x3a, 0xfa, 0xf0, 0xe7, 0xef, 0x2b, 0x5f, 0xae, 0xd3, 0xfb, 0xa1, + 0x27, 0xc2, 0x8b, 0xbd, 0x57, 0xfa, 0x78, 0xb8, 0x12, 0x87, 0xcf, 0xf7, 0x7d, 0x5d, 0xbd, 0xdd, + 0xef, 0xfe, 0x0b, 0x00, 0x00, 0xff, 0xff, 0x7b, 0xf2, 0xf2, 0x5c, 0xe3, 0x05, 0x00, 0x00, +} diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go new file mode 100644 index 0000000..9bc643a --- /dev/null +++ b/tensorflow/go/tensor.go @@ -0,0 +1,510 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include +// #include "tensorflow/c/c_api.h" +import "C" + +import ( + "bytes" + "encoding/binary" + "fmt" + "io" + "reflect" + "runtime" + "unsafe" +) + +// DataType holds the type for a scalar value. E.g., one slot in a tensor. +type DataType C.TF_DataType + +// Types of scalar values in the TensorFlow type system. +const ( + Float DataType = C.TF_FLOAT + Double DataType = C.TF_DOUBLE + Int32 DataType = C.TF_INT32 + Uint32 DataType = C.TF_UINT32 + Uint8 DataType = C.TF_UINT8 + Int16 DataType = C.TF_INT16 + Int8 DataType = C.TF_INT8 + String DataType = C.TF_STRING + Complex64 DataType = C.TF_COMPLEX64 + Complex DataType = C.TF_COMPLEX + Int64 DataType = C.TF_INT64 + Uint64 DataType = C.TF_UINT64 + Bool DataType = C.TF_BOOL + Qint8 DataType = C.TF_QINT8 + Quint8 DataType = C.TF_QUINT8 + Qint32 DataType = C.TF_QINT32 + Bfloat16 DataType = C.TF_BFLOAT16 + Qint16 DataType = C.TF_QINT16 + Quint16 DataType = C.TF_QUINT16 + Uint16 DataType = C.TF_UINT16 + Complex128 DataType = C.TF_COMPLEX128 + Half DataType = C.TF_HALF +) + +// Tensor holds a multi-dimensional array of elements of a single data type. +type Tensor struct { + c *C.TF_Tensor + shape []int64 +} + +// NewTensor converts from a Go value to a Tensor. Valid values are scalars, +// slices, and arrays. Every element of a slice must have the same length so +// that the resulting Tensor has a valid shape. +func NewTensor(value interface{}) (*Tensor, error) { + val := reflect.ValueOf(value) + shape, dataType, err := shapeAndDataTypeOf(val) + if err != nil { + return nil, err + } + nflattened := numElements(shape) + nbytes := typeOf(dataType, nil).Size() * uintptr(nflattened) + if dataType == String { + // TF_STRING tensors are encoded as an array of 8-byte offsets + // followed by string data. See c_api.h. + nbytes = uintptr(nflattened*8) + byteSizeOfEncodedStrings(value) + } + var shapePtr *C.int64_t + if len(shape) > 0 { + shapePtr = (*C.int64_t)(unsafe.Pointer(&shape[0])) + } + t := &Tensor{ + c: C.TF_AllocateTensor(C.TF_DataType(dataType), shapePtr, C.int(len(shape)), C.size_t(nbytes)), + shape: shape, + } + runtime.SetFinalizer(t, (*Tensor).finalize) + raw := tensorData(t.c) + buf := bytes.NewBuffer(raw[:0:len(raw)]) + if dataType != String { + if err := encodeTensor(buf, val, shape); err != nil { + return nil, err + } + if uintptr(buf.Len()) != nbytes { + return nil, bug("NewTensor incorrectly calculated the size of a tensor with type %v and shape %v as %v bytes instead of %v", dataType, shape, nbytes, buf.Len()) + } + } else { + e := stringEncoder{offsets: buf, data: raw[nflattened*8:], status: newStatus()} + if err := e.encode(reflect.ValueOf(value), shape); err != nil { + return nil, err + } + if int64(buf.Len()) != nflattened*8 { + return nil, bug("invalid offset encoding for TF_STRING tensor with shape %v (got %v, want %v)", shape, buf.Len(), nflattened*8) + } + } + return t, nil +} + +// ReadTensor constructs a Tensor with the provided type and shape from the +// serialized tensor contents in r. +// +// See also WriteContentsTo. +func ReadTensor(dataType DataType, shape []int64, r io.Reader) (*Tensor, error) { + if err := isTensorSerializable(dataType); err != nil { + return nil, err + } + nbytes := typeOf(dataType, nil).Size() * uintptr(numElements(shape)) + var shapePtr *C.int64_t + if len(shape) > 0 { + shapePtr = (*C.int64_t)(unsafe.Pointer(&shape[0])) + } + t := &Tensor{ + c: C.TF_AllocateTensor(C.TF_DataType(dataType), shapePtr, C.int(len(shape)), C.size_t(nbytes)), + shape: shape, + } + runtime.SetFinalizer(t, (*Tensor).finalize) + raw := tensorData(t.c) + if _, err := io.ReadFull(r, raw); err != nil { + return nil, err + } + return t, nil +} + +// newTensorFromC takes ownership of c and returns the owning Tensor. +func newTensorFromC(c *C.TF_Tensor) *Tensor { + var shape []int64 + if ndims := int(C.TF_NumDims(c)); ndims > 0 { + shape = make([]int64, ndims) + } + for i := range shape { + shape[i] = int64(C.TF_Dim(c, C.int(i))) + } + t := &Tensor{c: c, shape: shape} + runtime.SetFinalizer(t, (*Tensor).finalize) + return t +} + +func (t *Tensor) finalize() { C.TF_DeleteTensor(t.c) } + +// DataType returns the scalar datatype of the Tensor. +func (t *Tensor) DataType() DataType { return DataType(C.TF_TensorType(t.c)) } + +// Shape returns the shape of the Tensor. +func (t *Tensor) Shape() []int64 { return t.shape } + +// Value converts the Tensor to a Go value. For now, not all Tensor types are +// supported, and this function may panic if it encounters an unsupported +// DataType. +// +// The type of the output depends on the Tensor type and dimensions. +// For example: +// Tensor(int64, 0): int64 +// Tensor(float64, 3): [][][]float64 +func (t *Tensor) Value() interface{} { + typ := typeOf(t.DataType(), t.Shape()) + val := reflect.New(typ) + raw := tensorData(t.c) + if t.DataType() != String { + if err := decodeTensor(bytes.NewReader(raw), t.Shape(), typ, val); err != nil { + panic(bug("unable to decode Tensor of type %v and shape %v - %v", t.DataType(), t.Shape(), err)) + } + } else { + nflattened := numElements(t.Shape()) + d := stringDecoder{offsets: bytes.NewReader(raw[0 : 8*nflattened]), data: raw[8*nflattened:], status: newStatus()} + if err := d.decode(val, t.Shape()); err != nil { + panic(bug("unable to decode String tensor with shape %v - %v", t.Shape(), err)) + } + } + return reflect.Indirect(val).Interface() +} + +// WriteContentsTo writes the serialized contents of t to w. +// +// Returns the number of bytes written. See ReadTensor for +// reconstructing a Tensor from the serialized form. +// +// WARNING: WriteContentsTo is not comprehensive and will fail +// if t.DataType() is non-numeric (e.g., String). See +// https://github.com/tensorflow/tensorflow/issues/6003. +func (t *Tensor) WriteContentsTo(w io.Writer) (int64, error) { + if err := isTensorSerializable(t.DataType()); err != nil { + return 0, err + } + return io.Copy(w, bytes.NewReader(tensorData(t.c))) +} + +func tensorData(c *C.TF_Tensor) []byte { + // See: https://github.com/golang/go/wiki/cgo#turning-c-arrays-into-go-slices + cbytes := C.TF_TensorData(c) + if cbytes == nil { + return nil + } + length := int(C.TF_TensorByteSize(c)) + var slice []byte + if unsafe.Sizeof(unsafe.Pointer(nil)) == 8 { + slice = (*[1<<50 - 1]byte)(unsafe.Pointer(cbytes))[:length:length] + } else { + slice = (*[1 << 30]byte)(unsafe.Pointer(cbytes))[:length:length] + } + return slice +} + +var types = []struct { + typ reflect.Type + dataType C.TF_DataType +}{ + {reflect.TypeOf(float32(0)), C.TF_FLOAT}, + {reflect.TypeOf(float64(0)), C.TF_DOUBLE}, + {reflect.TypeOf(int32(0)), C.TF_INT32}, + {reflect.TypeOf(uint32(0)), C.TF_UINT32}, + {reflect.TypeOf(uint8(0)), C.TF_UINT8}, + {reflect.TypeOf(int16(0)), C.TF_INT16}, + {reflect.TypeOf(int8(0)), C.TF_INT8}, + {reflect.TypeOf(""), C.TF_STRING}, + {reflect.TypeOf(complex(float32(0), float32(0))), C.TF_COMPLEX64}, + {reflect.TypeOf(int64(0)), C.TF_INT64}, + {reflect.TypeOf(uint64(0)), C.TF_UINT64}, + {reflect.TypeOf(false), C.TF_BOOL}, + {reflect.TypeOf(uint16(0)), C.TF_UINT16}, + {reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128}, + // TODO(apassos): support DT_RESOURCE representation in go. + // TODO(keveman): support DT_VARIANT representation in go. +} + +// shapeAndDataTypeOf returns the data type and shape of the Tensor +// corresponding to a Go type. +func shapeAndDataTypeOf(val reflect.Value) (shape []int64, dt DataType, err error) { + typ := val.Type() + for typ.Kind() == reflect.Array || typ.Kind() == reflect.Slice { + shape = append(shape, int64(val.Len())) + if val.Len() > 0 { + // In order to check tensor structure properly in general case we need to iterate over all slices of the tensor to check sizes match + // Since we already going to iterate over all elements in encodeTensor() let's + // 1) do the actual check in encodeTensor() to save some cpu cycles here + // 2) assume the shape is represented by lengths of elements with zero index in each dimension + val = val.Index(0) + } + typ = typ.Elem() + } + for _, t := range types { + if typ.Kind() == t.typ.Kind() { + return shape, DataType(t.dataType), nil + } + } + return shape, dt, fmt.Errorf("unsupported type %v", typ) +} + +// typeOf converts from a DataType and Shape to the equivalent Go type. +func typeOf(dt DataType, shape []int64) reflect.Type { + var ret reflect.Type + for _, t := range types { + if dt == DataType(t.dataType) { + ret = t.typ + break + } + } + if ret == nil { + panic(bug("DataType %v is not supported (see https://www.tensorflow.org/code/tensorflow/core/framework/types.proto)", dt)) + } + for range shape { + ret = reflect.SliceOf(ret) + } + return ret +} + +func numElements(shape []int64) int64 { + n := int64(1) + for _, d := range shape { + n *= d + } + return n +} + +// byteSizeOfEncodedStrings returns the size of the encoded strings in val. +// val MUST be a string, or a container (array/slice etc.) of strings. +func byteSizeOfEncodedStrings(val interface{}) uintptr { + if s, ok := val.(string); ok { + return uintptr(C.TF_StringEncodedSize(C.size_t(len(s)))) + } + // Otherwise must be an array or slice. + var size uintptr + v := reflect.ValueOf(val) + for i := 0; i < v.Len(); i++ { + size += byteSizeOfEncodedStrings(v.Index(i).Interface()) + } + return size +} + +// encodeTensor writes v to the specified buffer using the format specified in +// c_api.h. Use stringEncoder for String tensors. +func encodeTensor(w *bytes.Buffer, v reflect.Value, shape []int64) error { + switch v.Kind() { + case reflect.Bool: + b := byte(0) + if v.Bool() { + b = 1 + } + if err := w.WriteByte(b); err != nil { + return err + } + case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128: + if err := binary.Write(w, nativeEndian, v.Interface()); err != nil { + return err + } + + case reflect.Array, reflect.Slice: + // If current dimension is a slice, verify that it has the expected size + // Go's type system makes that guarantee for arrays. + if v.Kind() == reflect.Slice { + expected := int(shape[0]) + if v.Len() != expected { + return fmt.Errorf("mismatched slice lengths: %d and %d", v.Len(), expected) + } + } + + // Optimisation: if only one dimension is left we can use binary.Write() directly for this slice + if len(shape) == 1 && v.Len() > 0 { + switch v.Index(0).Kind() { + case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128: + return binary.Write(w, nativeEndian, v.Interface()) + } + } + + subShape := shape[1:] + for i := 0; i < v.Len(); i++ { + err := encodeTensor(w, v.Index(i), subShape) + if err != nil { + return err + } + } + + default: + return fmt.Errorf("unsupported type %v", v.Type()) + } + return nil +} + +// decodeTensor decodes the Tensor from the buffer to ptr using the format +// specified in c_api.h. Use stringDecoder for String tensors. +func decodeTensor(r *bytes.Reader, shape []int64, typ reflect.Type, ptr reflect.Value) error { + switch typ.Kind() { + case reflect.Bool: + b, err := r.ReadByte() + if err != nil { + return err + } + ptr.Elem().SetBool(b == 1) + case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128: + if err := binary.Read(r, nativeEndian, ptr.Interface()); err != nil { + return err + } + + case reflect.Slice: + val := reflect.Indirect(ptr) + val.Set(reflect.MakeSlice(typ, int(shape[0]), int(shape[0]))) + + // Optimization: if only one dimension is left we can use binary.Read() directly for this slice + if len(shape) == 1 && val.Len() > 0 { + switch val.Index(0).Kind() { + case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128: + return binary.Read(r, nativeEndian, val.Interface()) + } + } + + for i := 0; i < val.Len(); i++ { + if err := decodeTensor(r, shape[1:], typ.Elem(), val.Index(i).Addr()); err != nil { + return err + } + } + + default: + return fmt.Errorf("unsupported type %v", typ) + } + return nil +} + +type stringEncoder struct { + offsets io.Writer + data []byte + offset uint64 + status *status +} + +func (e *stringEncoder) encode(v reflect.Value, shape []int64) error { + if v.Kind() == reflect.String { + if err := binary.Write(e.offsets, nativeEndian, e.offset); err != nil { + return err + } + var ( + s = v.Interface().(string) + src = C.CString(s) + srcLen = C.size_t(len(s)) + dst = (*C.char)(unsafe.Pointer(&e.data[e.offset])) + dstLen = C.size_t(uint64(len(e.data)) - e.offset) + ) + e.offset += uint64(C.TF_StringEncode(src, srcLen, dst, dstLen, e.status.c)) + C.free(unsafe.Pointer(src)) + return e.status.Err() + } + + if v.Kind() == reflect.Slice { + expected := int(shape[0]) + if v.Len() != expected { + return fmt.Errorf("mismatched slice lengths: %d and %d", v.Len(), expected) + } + } + + subShape := shape[1:] + for i := 0; i < v.Len(); i++ { + if err := e.encode(v.Index(i), subShape); err != nil { + return err + } + } + return nil +} + +type stringDecoder struct { + offsets io.Reader + data []byte + status *status +} + +func (d *stringDecoder) decode(ptr reflect.Value, shape []int64) error { + if len(shape) == 0 { + var offset uint64 + if err := binary.Read(d.offsets, nativeEndian, &offset); err != nil { + return err + } + var ( + src = (*C.char)(unsafe.Pointer(&d.data[offset])) + srcLen = C.size_t(len(d.data)) - C.size_t(offset) + dst *C.char + dstLen C.size_t + ) + if offset > uint64(len(d.data)) { + return fmt.Errorf("invalid offsets in String Tensor") + } + C.TF_StringDecode(src, srcLen, &dst, &dstLen, d.status.c) + if err := d.status.Err(); err != nil { + return err + } + s := ptr.Interface().(*string) + *s = C.GoStringN(dst, C.int(dstLen)) + return nil + } + val := reflect.Indirect(ptr) + val.Set(reflect.MakeSlice(typeOf(String, shape), int(shape[0]), int(shape[0]))) + for i := 0; i < val.Len(); i++ { + if err := d.decode(val.Index(i).Addr(), shape[1:]); err != nil { + return err + } + } + return nil +} + +func bug(format string, args ...interface{}) error { + return fmt.Errorf("BUG: Please report at https://github.com/tensorflow/tensorflow/issues with the note: Go TensorFlow %v: %v", Version(), fmt.Sprintf(format, args...)) +} + +func isTensorSerializable(dataType DataType) error { + // For numeric types, the serialized Tensor matches the in-memory + // representation. See the implementation of Tensor::AsProtoContent in + // https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc + // + // The more appropriate way to be in sync with Tensor::AsProtoContent + // would be to have the TensorFlow C library export functions for + // serialization and deserialization of Tensors. Till then capitalize + // on knowledge of the implementation for numeric types. + switch dataType { + case Float, Double, Int32, Uint8, Int16, Int8, Complex, Int64, Bool, Quint8, Qint32, Bfloat16, Qint16, Quint16, Uint16, Complex128, Half: + return nil + default: + return fmt.Errorf("serialization of tensors with the DataType %d is not yet supported, see https://github.com/tensorflow/tensorflow/issues/6003", dataType) + } +} + +// nativeEndian is the byte order for the local platform. Used to send back and +// forth Tensors with the C API. We test for endianness at runtime because +// some architectures can be booted into different endian modes. +var nativeEndian binary.ByteOrder + +func init() { + buf := [2]byte{} + *(*uint16)(unsafe.Pointer(&buf[0])) = uint16(0xABCD) + + switch buf { + case [2]byte{0xCD, 0xAB}: + nativeEndian = binary.LittleEndian + case [2]byte{0xAB, 0xCD}: + nativeEndian = binary.BigEndian + default: + panic("Could not determine native endianness.") + } +} diff --git a/tensorflow/go/tensor_handle.go b/tensorflow/go/tensor_handle.go new file mode 100644 index 0000000..09192ec --- /dev/null +++ b/tensorflow/go/tensor_handle.go @@ -0,0 +1,170 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +// #include "tensorflow/c/eager/c_api.h" +import "C" +import ( + "runtime" + "unsafe" +) + +// TensorHandle is a handle to a tensor on a device. +// +// A Tensor referenced by a TensorHandle may be on any device, whereas a Tensor +// always resides in the host CPU's memory. +// +// A Tensor referenced by a TensorHandle may not have been computed yet. For +// example, a TensorHandle might reference the output of an operation that has +// not finished executing. Because of this, various methods, such as Shape() may +// block until the tensor has been instantiated. +// +// This allows multiple operations to be performed on tensors on a device +// (e.g. a GPU) without sending these values back to the host CPU in between +// every operation. +type TensorHandle struct { + c *C.TFE_TensorHandle +} + +// NewTensorHandle creates a new tensor handle from a tensor. +func NewTensorHandle(t *Tensor) (*TensorHandle, error) { + status := newStatus() + cHandle := C.TFE_NewTensorHandle(t.c, status.c) + if err := status.Err(); err != nil { + return nil, err + } + + th := &TensorHandle{c: cHandle} + runtime.SetFinalizer(th, (*TensorHandle).finalizer) + return th, nil +} + +func (th *TensorHandle) finalizer() { + C.TFE_DeleteTensorHandle(th.c) +} + +// newTensorHandleFromC takes ownership of c and returns the owning TensorHandle. +func newTensorHandleFromC(c *C.TFE_TensorHandle) *TensorHandle { + th := &TensorHandle{c: c} + runtime.SetFinalizer(th, (*TensorHandle).finalizer) + return th +} + +// DataType returns the TensorHandle's datatype. +func (th *TensorHandle) DataType() DataType { + return DataType(C.TFE_TensorHandleDataType(th.c)) +} + +// Shape returns the shape of the Tensor referenced by th. +func (th *TensorHandle) Shape() ([]int64, error) { + n, err := th.numDims() + if err != nil { + return nil, err + } + r := make([]int64, n) + for i := 0; i < n; i++ { + if r[i], err = th.dim(i); err != nil { + return nil, err + } + } + return r, nil +} + +// numDims returns the number of dimensions of the TensorHandle. It blocks +// until the operation that produces the handle has completed. +func (th *TensorHandle) numDims() (int, error) { + status := newStatus() + n := int(C.TFE_TensorHandleNumDims(th.c, status.c)) + return n, status.Err() +} + +// dim returns the size of the index'th dimension of the TensorHandle. It +// blocks until the operation that produces the handle has completed. +func (th *TensorHandle) dim(index int) (int64, error) { + status := newStatus() + n := int64(C.TFE_TensorHandleDim(th.c, C.int(index), status.c)) + if err := status.Err(); err != nil { + return 0, err + } + return n, nil +} + +// DeviceName returns the name of the device of the operation that produced the +// TensorHandle. If the handle was produced by a copy, it returns the +// destination device of the copy. Note that returned device name is not always +// the device holding the tensor handle's memory. If you want the latter, use +// BackingDeviceName. This function will block till the operation that produces +// th has completed. +func (th *TensorHandle) DeviceName() (string, error) { + status := newStatus() + name := C.TFE_TensorHandleDeviceName(th.c, status.c) + if err := status.Err(); err != nil { + return "", err + } + return C.GoString(name), nil +} + +// BackingDeviceName returns the name of the device in whose memory the tensor +// handle resides. This function will block till the operation that produces +// `h` has completed. +// +// WARNING: The implementation currently returns the same as DeviceName(). +// After TensoFlow 1.13's C library is released, this implementation will +// be updated to return what the documentation says! +func (th *TensorHandle) BackingDeviceName() (string, error) { + // TODO(ashankar): Restore after TensorFlow 1.13 is released. + // See https://github.com/tensorflow/tensorflow/issues/23257#issuecomment-433751410 + return th.DeviceName() + /* + status := newStatus() + name := C.TFE_TensorHandleBackingDeviceName(th.c, status.c) + if err := status.Err(); err != nil { + return "", err + } + return C.GoString(name), nil + */ +} + +// ToTensor returns the Tensor referenced by th. It may block if this tensor is +// not yet computed. +func (th *TensorHandle) ToTensor() (*Tensor, error) { + status := newStatus() + cTensor := C.TFE_TensorHandleResolve(th.c, status.c) + if err := status.Err(); err != nil { + return nil, err + } + return newTensorFromC(cTensor), nil +} + +// CopyToDevice creates a new TensorHandle with the same contents as this +// TensorHandle but placed in the memory of the device 'deviceName'. If source +// and destination are the same device, then this creates a new handle that +// shares the underlying buffer. Otherwise, it currently requires at least one +// of the source or destination devices to be CPU (i.e., for the source or +// destination tensor to be placed in host memory). +func (th *TensorHandle) CopyToDevice(c *Context, deviceName string) (*TensorHandle, error) { + status := newStatus() + n := C.CString(deviceName) + newTh := C.TFE_TensorHandleCopyToDevice(th.c, c.c, n, status.c) + C.free(unsafe.Pointer(n)) + if err := status.Err(); err != nil { + return nil, err + } + return newTensorHandleFromC(newTh), nil +} diff --git a/tensorflow/go/tensor_handle_test.go b/tensorflow/go/tensor_handle_test.go new file mode 100644 index 0000000..15dea64 --- /dev/null +++ b/tensorflow/go/tensor_handle_test.go @@ -0,0 +1,127 @@ +/* +Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "reflect" + "strings" + "testing" +) + +func TestNewTensorHandle(t *testing.T) { + vals := [][]float32{{1.0, 2.0}, {3.0, 4.0}} + tensor, err := NewTensor(vals) + if err != nil { + t.Fatal(err) + } + if _, err = NewTensorHandle(tensor); err != nil { + t.Fatal(err) + } +} + +func TestTensorHandleDataType(t *testing.T) { + vals := [][]float32{{1.0, 2.0}, {3.0, 4.0}} + tensor, err := NewTensor(vals) + if err != nil { + t.Fatal(err) + } + th, err := NewTensorHandle(tensor) + if err != nil { + t.Fatal(err) + } + + if got, want := th.DataType(), Float; got != want { + t.Errorf("Got %v, want %v", got, want) + } +} + +func TestTensorHandleShape(t *testing.T) { + vals := [][]float32{{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}} + tensor, err := NewTensor(vals) + if err != nil { + t.Fatal(err) + } + th, err := NewTensorHandle(tensor) + if err != nil { + t.Fatal(err) + } + + got, err := th.Shape() + if err != nil { + t.Fatal(err) + } + if want := []int64{2, 3}; !reflect.DeepEqual(got, want) { + t.Errorf("Got %#v, want %#v", got, want) + } +} + +func TestTensorHandleDeviceName(t *testing.T) { + vals := [][]float32{{1.0, 2.0}, {3.0, 4.0}} + tensor, err := NewTensor(vals) + if err != nil { + t.Fatal(err) + } + th, err := NewTensorHandle(tensor) + if err != nil { + t.Fatal(err) + } + + d, err := th.DeviceName() + if err != nil { + t.Fatal(err) + } + if !strings.Contains(d, "CPU") { + t.Errorf("DeviceName() did not return a CPU device; got: %s", d) + } +} + +func TestTensorHandleBackingDeviceName(t *testing.T) { + vals := [][]float32{{1.0, 2.0}, {3.0, 4.0}} + tensor, err := NewTensor(vals) + if err != nil { + t.Fatal(err) + } + th, err := NewTensorHandle(tensor) + if err != nil { + t.Fatal(err) + } + + d, err := th.BackingDeviceName() + if err != nil { + t.Fatal(err) + } + if !strings.Contains(d, "CPU") { + t.Errorf("BackingDeviceName() did not return a CPU device; got: %s", d) + } +} + +func TestTensorHandleToTensor(t *testing.T) { + initialVals := [][]float32{{1.0, 2.0}, {3.0, 4.0}} + initialTensor, err := NewTensor(initialVals) + if err != nil { + t.Fatal(err) + } + th, err := NewTensorHandle(initialTensor) + if err != nil { + t.Fatal(err) + } + + tensor, err := th.ToTensor() + if v := tensor.Value().([][]float32); !reflect.DeepEqual(v, initialVals) { + t.Errorf("Got %#v, want %#v", v, initialVals) + } +} diff --git a/tensorflow/go/tensor_test.go b/tensorflow/go/tensor_test.go new file mode 100644 index 0000000..dc533cd --- /dev/null +++ b/tensorflow/go/tensor_test.go @@ -0,0 +1,314 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +import ( + "bytes" + "io" + "reflect" + "testing" +) + +func TestNewTensor(t *testing.T) { + var tests = []struct { + shape []int64 + value interface{} + }{ + {nil, bool(true)}, + {nil, int8(5)}, + {nil, int16(5)}, + {nil, int32(5)}, + {nil, int64(5)}, + {nil, uint8(5)}, + {nil, uint16(5)}, + {nil, uint32(5)}, + {nil, uint64(5)}, + {nil, float32(5)}, + {nil, float64(5)}, + {nil, complex(float32(5), float32(6))}, + {nil, complex(float64(5), float64(6))}, + {nil, "a string"}, + {[]int64{1}, []uint32{1}}, + {[]int64{1}, []uint64{1}}, + {[]int64{2}, []bool{true, false}}, + {[]int64{1}, []float64{1}}, + {[]int64{1}, [1]float64{1}}, + {[]int64{1, 1}, [1][1]float64{{1}}}, + {[]int64{1, 1, 1}, [1][1][]float64{{{1}}}}, + {[]int64{1, 1, 2}, [1][][2]float64{{{1, 2}}}}, + {[]int64{1, 1, 1, 1}, [1][][1][]float64{{{{1}}}}}, + {[]int64{2}, []string{"string", "slice"}}, + {[]int64{2}, [2]string{"string", "array"}}, + {[]int64{3, 2}, [][]float64{{1, 2}, {3, 4}, {5, 6}}}, + {[]int64{2, 3}, [2][3]float64{{1, 2, 3}, {3, 4, 6}}}, + {[]int64{4, 3, 2}, [][][]float64{ + {{1, 2}, {3, 4}, {5, 6}}, + {{7, 8}, {9, 10}, {11, 12}}, + {{0, -1}, {-2, -3}, {-4, -5}}, + {{-6, -7}, {-8, -9}, {-10, -11}}, + }}, + {[]int64{2, 0}, [][]int64{{}, {}}}, + {[]int64{2, 2}, [][]string{{"row0col0", "row0,col1"}, {"row1col0", "row1,col1"}}}, + {[]int64{2, 3}, [2][3]string{ + {"row0col0", "row0,col1", "row0,col2"}, + {"row1col0", "row1,col1", "row1,col2"}, + }}, + } + + var errorTests = []interface{}{ + struct{ a int }{5}, + new(int32), + new([]int32), + // native ints not supported + int(5), + []int{5}, + // Mismatched dimensions + [][]float32{{1, 2, 3}, {4}}, + // Mismatched dimensions. Should return "mismatched slice lengths" error instead of "BUG" + [][][]float32{{{1, 2}, {3, 4}}, {{1}, {3}}}, + // Mismatched dimensions. Should return error instead of valid tensor + [][][]float32{{{1, 2}, {3, 4}}, {{1}, {3}}, {{1, 2, 3}, {2, 3, 4}}}, + // Mismatched dimensions for strings + [][]string{{"abc"}, {"abcd", "abcd"}}, + } + + for _, test := range tests { + tensor, err := NewTensor(test.value) + if err != nil { + t.Errorf("NewTensor(%v): %v", test.value, err) + continue + } + if !reflect.DeepEqual(test.shape, tensor.Shape()) { + t.Errorf("Tensor.Shape(): got %v, want %v", tensor.Shape(), test.shape) + } + + // Test that encode and decode gives the same value. We skip arrays because + // they're returned as slices. + if reflect.TypeOf(test.value).Kind() != reflect.Array { + got := tensor.Value() + if !reflect.DeepEqual(test.value, got) { + t.Errorf("encode/decode: got %v, want %v", got, test.value) + } + } + } + + for _, test := range errorTests { + tensor, err := NewTensor(test) + if err == nil { + t.Errorf("NewTensor(%v): %v", test, err) + } + if tensor != nil { + t.Errorf("NewTensor(%v) = %v, want nil", test, tensor) + } + } +} + +func TestTensorSerialization(t *testing.T) { + var tests = []interface{}{ + bool(true), + int8(5), + int16(5), + int32(5), + int64(5), + uint8(5), + uint16(5), + float32(5), + float64(5), + complex(float32(5), float32(6)), + complex(float64(5), float64(6)), + []float64{1}, + [][]float32{{1, 2}, {3, 4}, {5, 6}}, + [][][]int8{ + {{1, 2}, {3, 4}, {5, 6}}, + {{7, 8}, {9, 10}, {11, 12}}, + {{0, -1}, {-2, -3}, {-4, -5}}, + {{-6, -7}, {-8, -9}, {-10, -11}}, + }, + []bool{true, false, true}, + } + for _, v := range tests { + t1, err := NewTensor(v) + if err != nil { + t.Errorf("(%v): %v", v, err) + continue + } + buf := new(bytes.Buffer) + n, err := t1.WriteContentsTo(buf) + if err != nil { + t.Errorf("(%v): %v", v, err) + continue + } + if n != int64(buf.Len()) { + t.Errorf("(%v): WriteContentsTo said it wrote %v bytes, but wrote %v", v, n, buf.Len()) + } + t2, err := ReadTensor(t1.DataType(), t1.Shape(), buf) + if err != nil { + t.Errorf("(%v): %v", v, err) + continue + } + if buf.Len() != 0 { + t.Errorf("(%v): %v bytes written by WriteContentsTo not read by ReadTensor", v, buf.Len()) + } + if got, want := t2.DataType(), t1.DataType(); got != want { + t.Errorf("(%v): Got %v, want %v", v, got, want) + } + if got, want := t2.Shape(), t1.Shape(); !reflect.DeepEqual(got, want) { + t.Errorf("(%v): Got %v, want %v", v, got, want) + } + if got, want := t2.Value(), v; !reflect.DeepEqual(got, want) { + t.Errorf("(%v): Got %v, want %v", v, got, want) + } + } +} + +func TestReadTensorDoesNotReadBeyondContent(t *testing.T) { + t1, _ := NewTensor(int8(7)) + t2, _ := NewTensor(float32(2.718)) + buf := new(bytes.Buffer) + if _, err := t1.WriteContentsTo(buf); err != nil { + t.Fatal(err) + } + if _, err := t2.WriteContentsTo(buf); err != nil { + t.Fatal(err) + } + + t3, err := ReadTensor(t1.DataType(), t1.Shape(), buf) + if err != nil { + t.Fatal(err) + } + t4, err := ReadTensor(t2.DataType(), t2.Shape(), buf) + if err != nil { + t.Fatal(err) + } + + if v, ok := t3.Value().(int8); !ok || v != 7 { + t.Errorf("Got (%v (%T), %v), want (7 (int8), true)", v, v, ok) + } + if v, ok := t4.Value().(float32); !ok || v != 2.718 { + t.Errorf("Got (%v (%T), %v), want (2.718 (float32), true)", v, v, ok) + } +} + +func TestTensorSerializationErrors(t *testing.T) { + // String tensors cannot be serialized + t1, err := NewTensor("abcd") + if err != nil { + t.Fatal(err) + } + buf := new(bytes.Buffer) + if n, err := t1.WriteContentsTo(buf); n != 0 || err == nil || buf.Len() != 0 { + t.Errorf("Got (%v, %v, %v) want (0, , 0)", n, err, buf.Len()) + } + // Should fail to read a truncated value. + if t1, err = NewTensor(int8(8)); err != nil { + t.Fatal(err) + } + n, err := t1.WriteContentsTo(buf) + if err != nil { + t.Fatal(err) + } + r := bytes.NewReader(buf.Bytes()[:n-1]) + if _, err = ReadTensor(t1.DataType(), t1.Shape(), r); err == nil { + t.Error("ReadTensor should have failed if the tensor content was truncated") + } +} + +func TestReadTensorReadAll(t *testing.T) { + // Get the bytes of a tensor. + a := []float32{1.1, 1.2, 1.3} + ats, err := NewTensor(a) + if err != nil { + t.Fatal(err) + } + abuf := new(bytes.Buffer) + if _, err := ats.WriteContentsTo(abuf); err != nil { + t.Fatal(err) + } + + // Get the bytes of another tensor. + b := []float32{1.1, 1.2, 1.3} + bts, err := NewTensor(b) + if err != nil { + t.Fatal(err) + } + bbuf := new(bytes.Buffer) + if _, err := bts.WriteContentsTo(bbuf); err != nil { + t.Fatal(err) + } + + // Check that ReadTensor reads all bytes of both tensors, when the situation + // requires one than reads. + abbuf := io.MultiReader(abuf, bbuf) + abts, err := ReadTensor(Float, []int64{2, 3}, abbuf) + if err != nil { + t.Fatal(err) + } + abtsf32 := abts.Value().([][]float32) + expected := [][]float32{a, b} + + if len(abtsf32) != 2 { + t.Fatalf("first dimension %d is not 2", len(abtsf32)) + } + for i := 0; i < 2; i++ { + if len(abtsf32[i]) != 3 { + t.Fatalf("second dimension %d is not 3", len(abtsf32[i])) + } + for j := 0; j < 3; j++ { + if abtsf32[i][j] != expected[i][j] { + t.Errorf("value at %d %d not equal %f %f", i, j, abtsf32[i][j], expected[i][j]) + } + } + } +} + +func benchmarkNewTensor(b *testing.B, v interface{}) { + for i := 0; i < b.N; i++ { + if t, err := NewTensor(v); err != nil || t == nil { + b.Fatalf("(%v, %v)", t, err) + } + } +} + +func BenchmarkNewTensor(b *testing.B) { + var ( + // Some sample sizes from the Inception image labeling model. + // Where input tensors correspond to a 224x224 RGB image + // flattened into a vector. + vector [224 * 224 * 3]int32 + ) + b.Run("[150528]", func(b *testing.B) { benchmarkNewTensor(b, vector) }) +} + +func benchmarkDecodeTensor(b *testing.B, t *Tensor) { + for i := 0; i < b.N; i++ { + _ = t.Value() + } +} + +func BenchmarkDecodeTensor(b *testing.B) { + var ( + // Some sample sizes from the Inception image labeling model. + // Where input tensors correspond to a 224x224 RGB image + // flattened into a vector. + vector [224 * 224 * 3]int32 + ) + t, err := NewTensor(vector) + if err != nil { + b.Fatalf("(%v, %v)", t, err) + } + b.Run("[150528]", func(b *testing.B) { benchmarkDecodeTensor(b, t) }) +} diff --git a/tensorflow/go/test.sh b/tensorflow/go/test.sh new file mode 100755 index 0000000..3ea8a05 --- /dev/null +++ b/tensorflow/go/test.sh @@ -0,0 +1,76 @@ +#!/usr/bin/env bash +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# TensorFlow uses 'bazel' for builds and tests. +# The TensorFlow Go API aims to be usable with the 'go' tool +# (using 'go get' etc.) and thus without bazel. +# +# This script acts as a brige between bazel and go so that: +# bazel test :test +# succeeds iff +# go test github.com/tensorflow/tensorflow/tensorflow/go +# succeeds. + +set -ex + +# Find the 'go' tool +if [[ ! -x "go" && -z $(which go) ]] +then + if [[ -x "/usr/local/go/bin/go" ]] + then + export PATH="${PATH}:/usr/local/go/bin" + else + echo "Could not find the 'go' tool in PATH or /usr/local/go" + exit 1 + fi +fi + +# Setup a GOPATH that includes just the TensorFlow Go API. +export GOPATH="${TEST_TMPDIR}/go" +export GOCACHE="${TEST_TMPDIR}/cache" +mkdir -p "${GOPATH}/src/github.com/tensorflow" +ln -s -f "${PWD}" "${GOPATH}/src/github.com/tensorflow/tensorflow" + +# Ensure that the TensorFlow C library is accessible to the +# linker at build and run time. +export LIBRARY_PATH="${PWD}/tensorflow" +OS=$(uname -s) +if [[ "${OS}" = "Linux" ]] +then + if [[ -z "${LD_LIBRARY_PATH}" ]] + then + export LD_LIBRARY_PATH="${PWD}/tensorflow" + else + export LD_LIBRARY_PATH="${PWD}/tensorflow:${LD_LIBRARY_PATH}" + fi +elif [[ "${OS}" = "Darwin" ]] +then + if [[ -z "${DYLD_LIBRARY_PATH}" ]] + then + export DYLD_LIBRARY_PATH="${PWD}/tensorflow" + else + export DYLD_LIBRARY_PATH="${PWD}/tensorflow:${DYLD_LIBRARY_PATH}" + fi +else + echo "Only support Linux/Darwin, System $OS is not supported" + exit 1 +fi + +# Document the Go version and run tests +echo "Go version: $(go version)" +go test \ + github.com/tensorflow/tensorflow/tensorflow/go \ + github.com/tensorflow/tensorflow/tensorflow/go/op diff --git a/tensorflow/go/util_test.go b/tensorflow/go/util_test.go new file mode 100644 index 0000000..2bec954 --- /dev/null +++ b/tensorflow/go/util_test.go @@ -0,0 +1,65 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +func Placeholder(g *Graph, name string, dt DataType) (Output, error) { + op, err := g.AddOperation(OpSpec{ + Type: "Placeholder", + Name: name, + Attrs: map[string]interface{}{ + "dtype": dt, + }, + }) + return op.Output(0), err +} + +func Const(g *Graph, name string, value interface{}) (Output, error) { + t, ok := value.(*Tensor) + if !ok { + var err error + if t, err = NewTensor(value); err != nil { + return Output{}, err + } + } + op, err := g.AddOperation(OpSpec{ + Type: "Const", + Name: name, + Attrs: map[string]interface{}{ + "dtype": t.DataType(), + "value": t, + }, + }) + return op.Output(0), err +} + +func Neg(g *Graph, name string, port Output) (Output, error) { + op, err := g.AddOperation(OpSpec{ + Type: "Neg", + Name: name, + Input: []Input{port}, + }) + return op.Output(0), err +} + +func Add(g *Graph, name string, x, y Output) (Output, error) { + op, err := g.AddOperation(OpSpec{ + Type: "Add", + Name: name, + Input: []Input{x, y}, + }) + return op.Output(0), err +} diff --git a/tensorflow/go/version.go b/tensorflow/go/version.go new file mode 100644 index 0000000..7de909d --- /dev/null +++ b/tensorflow/go/version.go @@ -0,0 +1,25 @@ +/* +Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +package tensorflow + +// #include +// #include "tensorflow/c/c_api.h" +import "C" + +// Version returns a string describing the version of the underlying TensorFlow +// runtime. +func Version() string { return C.GoString(C.TF_Version()) }