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Keras 2.2.0 (development)

Install the development version with: install_github("rstudio/keras")

  • Fix issue with serializing models that have constraint arguments

  • Fix issue with k_tile that needs an integer vector instead of a list as the n argument.

  • Fix issue with user-supplied output_shape in layer_lambda() not being supplied to tensorflow backends

  • Filter out metrics that were created for callbacks (e.g. lr)

  • Added application_mobilenet_v2() pre-trained model

  • Added sample_weight parameter to flow_images_from_data()

  • Use native Keras implementation (rather than SciPy) for image_array_save()

  • Default layer_flatten() data_format argument to NULL (which defaults to global Keras config).

  • Add baseline argument to callback_early_stopping() (stop training if a given baseline isn't reached).

  • Add data_format argument to layer_conv_1d().

  • Add layer_activation_relu(), making the ReLU activation easier to configure while retaining easy serialization capabilities.

  • Add axis = -1 argument in backend crossentropy functions specifying the class prediction axis in the input tensor.

  • Handle symbolic tensors and TF datasets in calls to fit(), evaluate(), and predict()

  • Add embeddings_data argument to callback_tensorboard()

  • Support for defining custom Keras models (i.e. custom call() logic for forward pass)

  • Handle named list of model output names in metrics argument of compile()

  • New custom_metric() function for defining custom metrics in R

  • Provide typed wrapper for categorical custom metrics

  • Provide access to Python layer within R custom layers

  • Don't convert custom layer output shape to tuple when shape is a list or tuple of other shapes

  • Re-export shape() function from tensorflow package

  • Re-export tuple() function from reticulate package

  • Indexes for get_layer() are now 1-based (for consistency w/ freeze_weights())

  • Accept named list for sample_weight argument to fit()

Keras 2.1.6 (CRAN)

  • Fix issue with single-element vectors passed to text preprocessing functions

  • Compatibility with TensorFlow v1.7 Keras implementation

  • Support workers parameter for native Keras generators (e.g. flow_images_from_directory())

  • Accept tensor as argument to k_pow()

  • In callback_reduce_lr_on_plateau(), rename epsilon argument to min_delta (backwards-compatible).

  • Add axis parameter to k_softmax()

  • Add send_as_json parameter to callback_remote_monitor()

  • Add data_format method to layer_flatten()

  • In multi_gpu_model(), add arguments cpu_merge and cpu_relocation (controlling whether to force the template model's weights to be on CPU, and whether to operate merge operations on CPU or GPU).

  • Record correct loss name for tfruns when custom functions are provided for loss

Keras 2.1.5

  • Support for custom constraints from R

  • Added timeseries_generator() utility function

  • New layer layer_depthwise_conv_2d()

  • Added brightness_range and validation_split arguments to [image_data_generator()].

Keras 2.1.4

  • Added support for remove_learning_phase in export_savedmodel() to avoid removing learning phase.

  • Normalize validation data to Keras array in fit() and fit_generator()

  • Ensure that custom layers return a tuple from compute_output_shape()

  • Added Nasnet and Densenet pre-trained models

  • New layers layer_activation_softmax() and layer_separable_conv_1d()

  • Added amsgrad parameter to optimizer_adam()

  • Fix incompatibility with Progbar.update() method in Keras 2.1.4

Keras 2.1.3

  • Models saved via export_savedmodel() that make use of learning phases can now be exported without having to manually reload the original model.

  • Ensure that models saved via export_savedmodel() can be served from CloudML

  • Run image data generators with R preprocessing functions on the main thread

  • Return R list from texts_to_sequences()

  • Various fixes for use_implementation() function

Keras 2.1.2

  • Added theme_bw option to plot method for training history

  • Support TF Dataset objects as generators for fit_generator(), etc.

  • Added use_implementation() and use_backend() functions as alternative to setting KERAS_IMPLEMENATION and KERAS_BACKEND environment variables.

  • Added R wrappers for Keras backend functions (e.g. k_variable(), k_dot(), etc.)

  • Use 1-based axis for normalize function.

  • Fix issue with printing training history after early stopping.

  • Experimental support for using the PlaidML backend.

  • Correct handling for R functions specified in custom_objects

  • Added with_custom_object_scope() function.

  • Automatically provide name to loss function during compile (enables save/load of models with custom loss function)

  • Provide global keras.fit_verbose option (defaults to 1)

keras 2.0.9

  • Added multi_gpu_model() function.

  • Automatically call keras_array() on the results of generator functions.

  • Ensure that steps_per_epoch is passed as an integer

  • Import evaluate() generic from tensorflow package

  • Handle NULL when converting R arrays to Keras friendly arrays

  • Added dataset_imbd_word_index() function

  • Ensure that sample_weight is passed to fit() as an array.

  • Accept single function as metrics argument to compile()

  • Automatically cast input_shape argument to applications to integer

  • Allow Keras models to be composable within model pipelines

  • Added freeze_weights() and unfreeze_weights() functions.

  • Implement export_savedmodel() generic from TensorFlow package

  • Convert R arrays to row-major before image preprocessing

  • Use tensorflow.keras for tensorflow implementation (TF v1.4)

  • Added application_inception_resnet_v2() pre-trained model

  • Added dataset_fashion_mnist() dataset

  • Added layer_cudnn_gru() and layer_cudnn_lstm() (faster recurrent layers backed by CuDNN)

  • Added layer_minimum() function

  • Added interpolation parameter to image_load() function

  • Add save_text_tokenizer() and load_text_tokenizer() functions.

  • Fix for progress bar output in Keras >= 2.0.9

  • Remove deprecated implementation argument from recurrent layers

  • Support for passing generators for validation data in fit_generator()

  • Accept single integer arguments for kernel sizes

  • Add standard layer arguments to layer_flatten() and layer_separable_conv_2d()

  • Added image_array_resize() and image_array_save() for 3D image arrays.

  • Allow custom layers and lambda layers to accept list parameters.

  • Expose add_loss() function for custom layers

keras 2.0.8

  • Add use_session_with_seed() function that establishes a random seed for the Keras session. Note that this should not be used when training time is paramount, as it disables GPU computation and CPU parallelism by default for more deterministic computations.

  • Fix for plotting training history with early stopping callback (thanks to @JamesAllingham).

  • Return R training history object from fit_generator()

  • Rename to_numpy_array() function to keras_array() reflecting automatic use of Keras default backend float type and "C" ordering.

  • Add standard layer arguments (e.g. name, trainable, etc.) to merge layers

  • Better support for training models from data tensors in TensorFlow (e.g. Datasets, TFRecords). Add a related example script.

  • Add clone_model() function, enabling to construct a new model, given an existing model to use as a template. Works even in a TensorFlow graph different from that of the original model.

  • Add target_tensors argument in compile(), enabling to use custom tensors or placeholders as model targets.

  • Add steps_per_epoch argument in fit(), enabling to train a model from data tensors in a way that is consistent with training from arrays. Similarly, add steps argument in predict() and evaluate().

  • Add layer_subtract() layer function.

  • Add weighted_metrics argument in compile to specify metric functions meant to take into account sample_weight or class_weight.

  • Enable stateful RNNs with CNTK.

keras 2.0.6

  • install_keras() function which installs both TensorFlow and Keras

  • Use keras package as default implementation rather than tf.contrib.keras

  • Training metrics plotted in realtime within the RStudio Viewer during fit

  • serialize_model() and unserialize_model() functions for saving Keras models as 'raw' R objects.

  • Automatically convert 64-bit R floats to backend default float type

  • Ensure that arrays passed to generator functions are normalized to C-order

  • to_numpy_array() utility function for custom generators (enables custom generators to yield C-ordered arrays of the correct float type)

  • Added batch_size and write_grads arguments to callback_tensorboard()

  • Added return_state argument to recurrent layers.

  • Don't re-export install_tensorflow() and tf_config() from tensorflow package.

  • is_keras_available() function to probe whether the Keras python package is available in the current environment.

  • as.data.frame() S3 method for Keras training history

  • Remove names from keras_model() inputs

  • Return result of evaluate() as named list

  • Write run metrics and evaluation data to tfruns

  • Provide hint to use r-tensorflow environment when importing keras

keras 2.0.5

  • Initial CRAN release