The tensorflow-ocaml project provides some OCaml bindings for TensorFlow.

Experimental ocaml bindings for PyTorch can be found in the ocaml-torch repo.

Installation

Use opam to install the tensorflow-ocaml package. Starting from version 0.0.11 this will automatically install the TensorFlow library.

opam install tensorflow

Build a simple example or run utop

To build your first TensorFlow program, create a new directory and cd into it. Then create a forty_two.ml file with the following content:

open Tensorflow

let () =
  let forty_two = Ops.(f 40. + f 2.) in
  let v = Session.run (Session.Output.scalar_float forty_two) in
  Printf.printf "%f\n%!" v

Then create a dune file with the following content:

(executables
  (names forty_two)
  (libraries tensorflow))

Run dune build forty_two.exe to compile the program and _build/default/forty_two.exe to run it!

You can also use Tensorflow via utop.

Optional step for GPU support

The TensorFlow library installed via opam does not support GPU acceleration. In order to use your GPU you will have to install TensorFlow 1.14, either by building it from source or by using prebuilt binaries. Then the library should be installed system-wide or you could set the LIBTENSORFLOW environment variable.

    export LIBTENSORFLOW={path_to_folder_with_libtensorflow.so}

Possible ways to get the TensorFlow library:

• Use prebuilt binaries from Google. The releases are available for download in URLs of the form: https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-TYPE-OS-ARCH-VERSION.tar.gz. For example:

  • CPU-only, Linux, x86_64. [1.0.0] [1.14.0]
  • GPU-enabled, Linux, x86_64. [1.0.0] [1.14.0]
  • CPU-only, OS X, x86_64. [1.0.0] [1.14.0]
  • GPU-enabled, OS X, x86_64. [1.0.0] [1.14.0]

• Build the library from source. Perform the following steps:

  1. Install the Bazel build system.

  2. Clone the TensorFlow repo:

     git clone --recurse-submodules -b r1.14 https://github.com/tensorflow/tensorflow

  3. Configure the build (you will be asked if you want to enable CUDA support):

     cd tensorflow/
     ./configure
     

  4. Compile the library:

     bazel build -c opt tensorflow:libtensorflow.so

     The binary should appear under bazel-bin/tensorflow/libtensorflow.so.

Examples

Tensorflow-ocaml includes two different APIs to write graphs.

Using the Graph API

The graph API is very close to the original TensorFlow API.

• Some MNIST based tutorials are available in the examples directory. A simple Convolutional Neural Network can be defined as follows:

  let ys_ =
    O.Placeholder.to_node xs
    |> Layer.reshape ~shape:[ -1; 28; 28; 1 ]
    |> Layer.conv2d ~ksize:(5, 5) ~strides:(1, 1) ~output_dim:32
    |> Layer.max_pool ~ksize:(2, 2) ~strides:(2, 2)
    |> Layer.conv2d ~ksize:(5, 5) ~strides:(1, 1) ~output_dim:64
    |> Layer.max_pool ~ksize:(2, 2) ~strides:(2, 2)
    |> Layer.flatten
    |> Layer.linear ~output_dim:1024 ~activation:Relu
    |> O.dropout ~keep_prob:(O.Placeholder.to_node keep_prob)
    |> Layer.linear ~output_dim:10 ~activation:Softmax
  in

• examples/load/load.ml contains a simple example where the TensorFlow graph is loaded from a file (this graph has been generated by examples/load.py),

• examples/basics contains some curve fitting examples. You will need gnuplot to be installed via opam to run the gnuplot versions.

Using the FNN API

The FNN API is a layer based API to easily build neural-networks. A linear classifier could be defined and trained in a couple lines:

  let input, input_id = Fnn.input ~shape:(D1 image_dim) in
  let model =
    Fnn.dense label_count input
    |> Fnn.softmax
    |> Fnn.Model.create Float
  in
  Fnn.Model.fit model
    ~loss:(Fnn.Loss.cross_entropy `mean)
    ~optimizer:(Fnn.Optimizer.gradient_descent ~learning_rate:8.)
    ~epochs
    ~input_id
    ~xs:train_images
    ~ys:train_labels;

A complete VGG-19 model can be defined as follows:

let vgg19 () =
  let block iter ~block_idx ~out_channels x =
    List.init iter ~f:Fn.id
    |> List.fold ~init:x ~f:(fun acc idx ->
      Fnn.conv2d () acc
        ~name:(sprintf "conv%d_%d" block_idx (idx+1))
        ~w_init:(`normal 0.1) ~filter:(3, 3) ~strides:(1, 1) ~padding:`same ~out_channels
      |> Fnn.relu)
    |> Fnn.max_pool ~filter:(2, 2) ~strides:(2, 2) ~padding:`same
  in
  let input, input_id = Fnn.input ~shape:(D3 (img_size, img_size, 3)) in
  let model =
    Fnn.reshape input ~shape:(D3 (img_size, img_size, 3))
    |> block 2 ~block_idx:1 ~out_channels:64
    |> block 2 ~block_idx:2 ~out_channels:128
    |> block 4 ~block_idx:3 ~out_channels:256
    |> block 4 ~block_idx:4 ~out_channels:512
    |> block 4 ~block_idx:5 ~out_channels:512
    |> Fnn.flatten
    |> Fnn.dense ~name:"fc6" ~w_init:(`normal 0.1) 4096
    |> Fnn.relu
    |> Fnn.dense ~name:"fc7" ~w_init:(`normal 0.1) 4096
    |> Fnn.relu
    |> Fnn.dense ~name:"fc8" ~w_init:(`normal 0.1) 1000
    |> Fnn.softmax
    |> Fnn.Model.create Float
  in
  input_id, model

This model is used in the following example to classify an input image. In order to use it you will have to download the pre-trained weights.

There are also some MNIST based examples.

Other Examples

The examples directory contains various models among which:

• A simplified version of char-rnn illustrating character level language modeling using Recurrent Neural Networks.
• Neural Style Transfer applies the style of an image to the content of another image. This uses some deep Convolutional Neural Network.
• Some variants of Generative Adversarial Networks. These are used to generate MNIST like images.

Dependencies

• dune is used as a build system.
• ocaml-ctypes is used for the C bindings.
• Base is only necessary when generating the TensorFlow graph from OCaml, the wrapper itself does not need it.
• The code in the piqi directory comes from the Piqi project. There is no need to install piqi though.
• Cmdliner is used for command line interfaces.
• Gnuplot-ocaml is an optional dependency used by a couple examples.
• npy-ocaml is used to read/write from npy/npz files.