The tensorflow-haskell package provides Haskell bindings to TensorFlow.

This is not an official Google product.

Documentation

https://tensorflow.github.io/haskell/haddock/

TensorFlow.Core is a good place to start.

Examples

Neural network model for the MNIST dataset: code

Toy example of a linear regression model (full code):

import Control.Monad (replicateM, replicateM_, zipWithM)
import System.Random (randomIO)
import Test.HUnit (assertBool)

import qualified TensorFlow.Core as TF
import qualified TensorFlow.GenOps.Core as TF
import qualified TensorFlow.Gradient as TF
import qualified TensorFlow.Ops as TF

main :: IO ()
main = do
    -- Generate data where `y = x*3 + 8`.
    xData <- replicateM 100 randomIO
    let yData = [x*3 + 8 | x <- xData]
    -- Fit linear regression model.
    (w, b) <- fit xData yData
    assertBool "w == 3" (abs (3 - w) < 0.001)
    assertBool "b == 8" (abs (8 - b) < 0.001)

fit :: [Float] -> [Float] -> IO (Float, Float)
fit xData yData = TF.runSession $ do
    -- Create tensorflow constants for x and y.
    let x = TF.vector xData
        y = TF.vector yData
    -- Create scalar variables for slope and intercept.
    w <- TF.build (TF.initializedVariable 0)
    b <- TF.build (TF.initializedVariable 0)
    -- Define the loss function.
    let yHat = (x `TF.mul` w) `TF.add` b
        loss = TF.square (yHat `TF.sub` y)
    -- Optimize with gradient descent.
    trainStep <- TF.build (gradientDescent 0.001 loss [w, b])
    replicateM_ 1000 (TF.run trainStep)
    -- Return the learned parameters.
    (TF.Scalar w', TF.Scalar b') <- TF.run (w, b)
    return (w', b')

gradientDescent :: Float
                -> TF.Tensor TF.Value Float
                -> [TF.Tensor TF.Ref Float]
                -> TF.Build TF.ControlNode
gradientDescent alpha loss params = do
    let applyGrad param grad =
            TF.assign param (param `TF.sub` (TF.scalar alpha `TF.mul` grad))
    TF.group =<< zipWithM applyGrad params =<< TF.gradients loss params

Installation Instructions

Build with Docker on Linux

As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests.

git clone --recursive https://github.com/tensorflow/haskell.git tensorflow-haskell
cd tensorflow-haskell
IMAGE_NAME=tensorflow/haskell:v0
docker build -t $IMAGE_NAME docker
# TODO: move the setup step to the docker script.
stack --docker --docker-image=$IMAGE_NAME setup
stack --docker --docker-image=$IMAGE_NAME test

There is also a demo application:

cd tensorflow-mnist
stack --docker --docker-image=$IMAGE_NAME build --exec Main

Build on Mac OS X

The following instructions were verified with Mac OS X El Capitan.

• Install the "protoc" binary somewhere in your PATH. You can get it by downloading the corresponding file for your system from https://github.com/google/protobuf/releases. (The corresponding file will be named something like protoc-*-.zip.)

• Install dependencies via Homebrew:

    brew install swig
    brew install bazel
  

• Build the TensorFlow library and install it on your machine:

    cd third_party/tensorflow
    ./configure  # Choose the defaults when prompted
    bazel build -c opt tensorflow:libtensorflow_c.so
    install bazel-bin/tensorflow/libtensorflow_c.so /usr/local/lib/libtensorflow_c.dylib
    install_name_tool -id libtensorflow_c.dylib /usr/local/lib/libtensorflow_c.dylib
    cd ../..
  

• Run stack:

    stack test
  

Note: you may need to upgrade your version of Clang if you get an error like the following:

tensorflow/core/ops/ctc_ops.cc:60:7: error: return type 'tensorflow::Status' must match previous return type 'const ::tensorflow::Status' when lambda expression has unspecified explicit return type
    return Status::OK();

In that case you can just upgrade XCode and then run gcc --version to get the new version of the compiler.