https://github.com/laurentmazare/ocaml-tensorflow-eager
OCaml bindings for TensorFlow Eager mode
https://github.com/laurentmazare/ocaml-tensorflow-eager
bindings eager ocaml tensorflow
Last synced: about 1 month ago
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OCaml bindings for TensorFlow Eager mode
- Host: GitHub
- URL: https://github.com/laurentmazare/ocaml-tensorflow-eager
- Owner: LaurentMazare
- License: apache-2.0
- Created: 2017-10-02T20:41:22.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-10-05T23:20:44.000Z (over 6 years ago)
- Last Synced: 2025-04-09T14:40:45.320Z (about 2 months ago)
- Topics: bindings, eager, ocaml, tensorflow
- Language: OCaml
- Homepage:
- Size: 952 KB
- Stars: 11
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
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These bindings are pretty much out of date.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).