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_)
import System.Random (randomIO)
import Test.HUnit (assertBool)

import qualified TensorFlow.Core as TF
import qualified TensorFlow.GenOps.Core as TF
import qualified TensorFlow.Minimize as TF
import qualified TensorFlow.Ops as TF hiding (initializedVariable)
import qualified TensorFlow.Variable 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.initializedVariable 0
    b <- TF.initializedVariable 0
    -- Define the loss function.
    let yHat = (x `TF.mul` TF.readValue w) `TF.add` TF.readValue b
        loss = TF.square (yHat `TF.sub` y)
    -- Optimize with gradient descent.
    trainStep <- TF.minimizeWith (TF.gradientDescent 0.001) loss [w, b]
    replicateM_ 1000 (TF.run trainStep)
    -- Return the learned parameters.
    (TF.Scalar w', TF.Scalar b') <- TF.run (TF.readValue w, TF.readValue b)
    return (w', b')

Installation Instructions

Note: building this repository with stack requires version 1.4.0 or newer.

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

Run the install_osx_dependencies.sh script in the tools/ directory. The script installs dependencies via Homebrew and then downloads and installs the TensorFlow library on your machine under /usr/local.

After running the script to install system dependencies, build the project with stack:

stack test

Build on NixOS

tools/userchroot.nix expression contains definitions to open chroot-environment containing necessary dependencies. Type

$ nix-shell tools/userchroot.nix
$ stack build --system-ghc

to enter the environment and build the project. Note, that it is an emulation of common Linux environment rather than full-featured Nix package expression. No exportable Nix package will appear, but local development is possible.

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