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Haskell bindings for TensorFlow
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Judah Jacobson a64af5076a Work around #92 by always copying TensorData when fetching.
It would be better to avoid the copy when it's not necessary, but
that will require more involved changes to the internal API.  (For example,
Fetchable might need to allow IO or ST actions.)
2017-05-09 00:10:29 -07:00
ci_build Update to 1.0 release and newest proto-lens (#77) 2017-02-22 15:24:45 -08:00
docker Update to 1.0 release and newest proto-lens (#77) 2017-02-22 15:24:45 -08:00
docs/haddock Regenerate the Haddock docs. (#95) 2017-04-08 07:14:47 -07:00
google-shim Initial commit 2016-10-24 19:26:42 +00:00
tensorflow Work around #92 by always copying TensorData when fetching. 2017-05-09 00:10:29 -07:00
tensorflow-core-ops Adapt to lts-8.6 and use proto-lens-0.2.0.1 (#97) 2017-04-11 14:09:01 -07:00
tensorflow-logging Adapt to lts-8.6 and use proto-lens-0.2.0.1 (#97) 2017-04-11 14:09:01 -07:00
tensorflow-mnist Adapt to lts-8.6 and use proto-lens-0.2.0.1 (#97) 2017-04-11 14:09:01 -07:00
tensorflow-mnist-input-data Initial commit 2016-10-24 19:26:42 +00:00
tensorflow-nn Distinguish between "rendered" and "unrendered" Tensors. (#88) 2017-04-06 15:10:33 -07:00
tensorflow-opgen Add resource-based variable ops. (#98) 2017-04-16 09:24:02 -07:00
tensorflow-ops Work around #92 by always copying TensorData when fetching. 2017-05-09 00:10:29 -07:00
tensorflow-proto Adapt to lts-8.6 and use proto-lens-0.2.0.1 (#97) 2017-04-11 14:09:01 -07:00
tensorflow-queue Adapt to lts-8.6 and use proto-lens-0.2.0.1 (#97) 2017-04-11 14:09:01 -07:00
tensorflow-records Improve comments and make naming consistent. 2017-02-11 12:53:42 -08:00
tensorflow-records-conduit Add cabal files and CI setup for TFRecords. 2017-02-11 12:53:42 -08:00
tensorflow-test Make code --pedantic (#35) 2016-11-18 10:42:02 -08:00
third_party Uprev to TF 1.0rc1. (#69) 2017-02-09 14:20:43 -08:00
tools call sudo consistently within OSX build script (#91) 2017-04-03 20:27:22 -07:00
.gitignore Optimize fetching (#27) 2016-11-17 10:41:49 -08:00
.gitmodules Initial commit 2016-10-24 19:26:42 +00:00
CONTRIBUTING.md Initial commit 2016-10-24 19:26:42 +00:00
LICENSE Initial commit 2016-10-24 19:26:42 +00:00
README.md Distinguish between "rendered" and "unrendered" Tensors. (#88) 2017-04-06 15:10:33 -07:00
stack.yaml Adapt to lts-8.6 and use proto-lens-0.2.0.1 (#97) 2017-04-11 14:09:01 -07:00

Build Status

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.initializedVariable 0
    b <- 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 <- 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.Build Float
                -> [TF.Tensor TF.Ref Float]
                -> TF.Session 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

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