ci_build | ||
docker | ||
docs/haddock | ||
tensorflow | ||
tensorflow-core-ops | ||
tensorflow-logging | ||
tensorflow-mnist | ||
tensorflow-mnist-input-data | ||
tensorflow-opgen | ||
tensorflow-ops | ||
tensorflow-proto | ||
tensorflow-records | ||
tensorflow-records-conduit | ||
tensorflow-test | ||
third_party | ||
tools | ||
.gitignore | ||
.gitmodules | ||
ChangeLog.md | ||
CONTRIBUTING.md | ||
LICENSE | ||
README.md | ||
stack.yaml |
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_)
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.
Check your stack version with stack --version
in a terminal.
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.