ocaml-tensorflow-eager

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ocaml-tensorflow-eager

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OCaml bindings for TensorFlow Eager mode

ocaml-tensorflow-eager 최신버젼 다운로드

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Experimental [OCaml](http://ocaml.org) bindings for [TensorFlow](http://tensorflow.org) [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager).

These bindings are pretty much out of date. Some bindings for [PyTorch](https://pytorch.org)
can be found in the [ocaml-torch repo](https://github.com/LaurentMazare/ocaml-torch).

When using TensorFlow Eager execution, operations are executed immediately in the
same way as [PyTorch](http://pytorch.org/). The computation graph is dynamic.

## Examples

### Hello World!
A very simple example performing an addition using TensorFlow can be seen below:

```ocaml
module O = Tf_ops.Ops

let () =
  let twenty_one = O.f32 21. in
  let forty_two = O.(twenty_one + twenty_one) in
  O.print forty_two
```

### Linear Model for Mnist

In this example, we show how to build a linear classifier for the MNIST
dataset. This requires you to download the [MNIST data files](http://yann.lecun.com/exdb/mnist/).

Gradients are computed using `Gradients.compute`. This function returns the
gradients with respect to *watched* tensors. In order to watch a variable use
`Var.read_and_watch` as in the example below.

Once the gradients have been computed, `Gradients.apply_sgd_step` is used
to update the variables via a gradient descent step.

```ocaml
open Base
open Tf_ops
module O = Ops

(* This should reach ~92% accuracy. *)
let image_dim = Helper.image_dim
let label_count = Helper.label_count
let training_steps = 500
let batch_size = 512

let () =
  let mnist_dataset = Helper.read_files "data" in
  let test_images = Helper.test_images mnist_dataset in
  let test_labels = Helper.test_labels mnist_dataset in

(* Create the variables for the linear model. *)
  let w = Var.f32 [ image_dim; label_count ] 0. in
  let b = Var.f32 [ label_count ] 0. in

(* Build the model. [read_and_watch] returns the current value of a variable
     and ensures that gradients wrt this variable will be computed. *)
  let model xs =
    let w_read = Var.read_and_watch w in
    let b_read = Var.read_and_watch b in
    O.(xs *^ w_read + b_read) |> O.softmax
  in

for step_index = 1 to training_steps do
    (*Every so often, print the accuracy on the test dataset. *)
    if step_index % 50 = 0 then begin
      let accuracy =
        O.(equal (arg_max (model test_images)) (arg_max test_labels))
        |> O.cast ~type_dstT:Float
        |> O.reduce_mean
        |> O.to_float
      in
      Stdio.printf "step index %d, accuracy %.2f%%\n%!" step_index (100. *. accuracy)
    end;

(* Get a training batch, apply the model and compute the loss. *)
    let train_images, train_labels =
      Helper.train_batch mnist_dataset ~batch_size ~batch_idx:step_index
    in
    let ys = model train_images in
    let cross_entropy = O.cross_entropy ~ys:train_labels ~y_hats:ys `mean in

(* Compute the loss gradients and apply gradient descent to minimize it. *)
    let gradients = Tf_ops.Gradients.compute cross_entropy in
    Tf_ops.Gradients.apply_sgd_step gradients ~learning_rate:8.
  done
```

## Installation

In order to build this on linux, download the [TensorFlow 1.7.0 binaries](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.7.0.tar.gz). If this is unpacked at `TFPATH` compiling can be done via:
```bash
LD_LIBRARY_PATH=$TFPATH/lib:$LD_LIBRARY_PATH LIBRARY_PATH=$TFPATH/lib:$LIBRARY_PATH make all
```

For the [VGG-19 example](https://github.com/LaurentMazare/ocaml-tf/tree/master/examples/vgg19.ml), the weights are available [here](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz).

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