girving/tensorflow-ocaml

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

These bindings are in an early stage of their development. Some operators are not supported and the API is likely to change in the future. That being said they already contain the necessary to train a convolution network using various optimizers.

Installation

The easiest way is probably to use opam.

opam install tensorflow

The opam package requires the TensorFlow library to be installed on your system.

The version 0.0.6 of the opam package uses TensorFlow release 0.10: libtensorflow-0.10.so.
The version 0.0.7 of the opam package uses release 0.11: libtensorflow-0.11.so.
The current tip uses release 0.12: libtensorflow.so.0.12.

There are two ways to obtain these libraries:

You can build them from source by following these steps:
1. Install the Bazel build system
2. Clone the TensorFlow repo git clone --recurse-submodules -b r0.12 https://github.com/tensorflow/tensorflow
3. In tensorflow run ./configure then bazel build -c opt tensorflow:libtensorflow_c.so.
  - In order to build with GPU support, CUDA needs to be installed and specified during ./configure.
You can download prebuilt x86-64 linux binaries (CPU only), libtensorflow-0.10.so, libtensorflow-0.11.so or libtensorflow.so.0.12.

Adjust your LD_LIBRARY_PATH to include the directory in which you've put libtensorflow-0.1x.so and finally download a very simple example and compile it with the following command:

ocamlbuild forty_two.native -package ctypes -package ctypes.foreign -package core_kernel -package tensorflow -cflags -w,-40

Enjoy!

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.
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;

There are also some MNIST based examples.

Character level RNN

A simplified version of char-rnn can also be found in the examples directory which contains additional details.

Dependencies

ocaml-ctypes is used for the C bindings.
Core_kernel is only necessary when generating the 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.

girving / tensorflow-ocaml