The tensorflow-haskell package provides Haskell bindings to TensorFlow.
This is not an official Google product.
https://tensorflow.github.io/haskell/haddock/
TensorFlow.Core is a good place to start.
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
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
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.