925c2e9515
* Fix compilation with tensorflow 2.12 and higher * Update tensorflow submodule to 2.12.0 * Update Dockerfiles to 2.12.0 |
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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 2.3.1
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
docker build -t tensorflow/haskell:2.12.0 docker
# TODO: move the setup step to the docker script.
stack --docker setup
stack --docker test
There is also a demo application:
cd tensorflow-mnist
stack --docker build --exec Main
Stack + Docker + GPU
If you want to use GPU you can do:
IMAGE_NAME=tensorflow/haskell:2.12.0-gpu
docker build -t $IMAGE_NAME docker/gpu
# TODO: move the setup step to the docker script.
stack --docker --docker-image=$IMAGE_NAME setup
stack --docker --docker-image=$IMAGE_NAME test
Using nvidia-docker version 2
See Nvidia docker 2 install instructions
stack --docker --docker-image=$IMAGE_NAME setup
stack --docker --docker-run-args "--runtime=nvidia" --docker-image=$IMAGE_NAME test
Using nvidia-docker classic
Stack needs to use nvidia-docker
instead of the normal docker
for GPU support. We must wrap 'docker' with a script. This script will shadow the normal docker
command.
ln -s `pwd`/tools/nvidia-docker-wrapper.sh <somewhere in your path>/docker
stack --docker --docker-image=$IMAGE_NAME setup
stack --docker --docker-image=$IMAGE_NAME test
Build on macOS
Run the install_macos_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
The stack.yaml
file describes a NixOS environment containing the necessary
dependencies. To build, run:
$ stack --nix build
Installation on CentOS
Xiaokui Shu (@subbyte) maintains separate instructions for installation on CentOS.
Related Projects
Statically validated tensor shapes
https://github.com/helq/tensorflow-haskell-deptyped is experimenting with using dependent types to statically validate tensor shapes. May be merged with this repository in the future.
Example:
{-# LANGUAGE DataKinds, ScopedTypeVariables #-}
import Data.Maybe (fromJust)
import Data.Vector.Sized (Vector, fromList)
import TensorFlow.DepTyped
test :: IO (Vector 8 Float)
test = runSession $ do
(x :: Placeholder "x" '[4,3] Float) <- placeholder
let elems1 = fromJust $ fromList [1,2,3,4,1,2]
elems2 = fromJust $ fromList [5,6,7,8]
(w :: Tensor '[3,2] '[] Build Float) = constant elems1
(b :: Tensor '[4,1] '[] Build Float) = constant elems2
y = (x `matMul` w) `add` b -- y shape: [4,2] (b shape is [4.1] but `add` broadcasts it to [4,2])
let (inputX :: TensorData "x" [4,3] Float) =
encodeTensorData . fromJust $ fromList [1,2,3,4,1,0,7,9,5,3,5,4]
runWithFeeds (feed x inputX :~~ NilFeedList) y
main :: IO ()
main = test >>= print
License
This project is licensed under the terms of the Apache 2.0 license.