mirror of
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156 lines
5.2 KiB
Markdown
156 lines
5.2 KiB
Markdown
[](https://ci.tensorflow.org/job/tensorflow-haskell-master)
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The tensorflow-haskell package provides Haskell bindings to
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[TensorFlow](https://www.tensorflow.org/).
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This is not an official Google product.
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# Documentation
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https://tensorflow.github.io/haskell/haddock/
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[TensorFlow.Core](https://tensorflow.github.io/haskell/haddock/tensorflow-0.1.0.2/TensorFlow-Core.html)
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is a good place to start.
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# Examples
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Neural network model for the MNIST dataset: [code](tensorflow-mnist/app/Main.hs)
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Toy example of a linear regression model
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([full code](tensorflow-ops/tests/RegressionTest.hs)):
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```haskell
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import Control.Monad (replicateM, replicateM_)
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import System.Random (randomIO)
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import Test.HUnit (assertBool)
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.GenOps.Core as TF
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import qualified TensorFlow.Minimize as TF
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import qualified TensorFlow.Ops as TF hiding (initializedVariable)
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import qualified TensorFlow.Variable as TF
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main :: IO ()
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main = do
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-- Generate data where `y = x*3 + 8`.
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xData <- replicateM 100 randomIO
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let yData = [x*3 + 8 | x <- xData]
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-- Fit linear regression model.
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(w, b) <- fit xData yData
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assertBool "w == 3" (abs (3 - w) < 0.001)
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assertBool "b == 8" (abs (8 - b) < 0.001)
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fit :: [Float] -> [Float] -> IO (Float, Float)
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fit xData yData = TF.runSession $ do
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-- Create tensorflow constants for x and y.
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let x = TF.vector xData
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y = TF.vector yData
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-- Create scalar variables for slope and intercept.
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w <- TF.initializedVariable 0
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b <- TF.initializedVariable 0
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-- Define the loss function.
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let yHat = (x `TF.mul` TF.readValue w) `TF.add` TF.readValue b
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loss = TF.square (yHat `TF.sub` y)
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-- Optimize with gradient descent.
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trainStep <- TF.minimizeWith (TF.gradientDescent 0.001) loss [w, b]
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replicateM_ 1000 (TF.run trainStep)
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-- Return the learned parameters.
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(TF.Scalar w', TF.Scalar b') <- TF.run (TF.readValue w, TF.readValue b)
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return (w', b')
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```
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# Installation Instructions
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Note: building this repository with `stack` requires version `1.4.0` or newer.
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Check your stack version with `stack --version` in a terminal.
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## Build with Docker on Linux
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As an expedient we use [docker](https://www.docker.com/) for building. Once you have docker
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working, the following commands will compile and run the tests.
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git clone --recursive https://github.com/tensorflow/haskell.git tensorflow-haskell
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cd tensorflow-haskell
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IMAGE_NAME=tensorflow/haskell:v0
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docker build -t $IMAGE_NAME docker
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# TODO: move the setup step to the docker script.
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stack --docker --docker-image=$IMAGE_NAME setup
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stack --docker --docker-image=$IMAGE_NAME test
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There is also a demo application:
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cd tensorflow-mnist
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stack --docker --docker-image=$IMAGE_NAME build --exec Main
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### Docker GPU support
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If you want to use GPU you can do:
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IMAGE_NAME=tensorflow/haskell:1.3.0-gpu
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docker build -t $IMAGE_NAME docker/gpu
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We need stack to use nvidia-docker by using a 'docker' wrapper script. This will shadow the normal docker command.
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ln -s `pwd`/tools/nvidia-docker-wrapper.sh <somewhere in your path>/docker
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stack --docker --docker-image=$IMAGE_NAME setup
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stack --docker --docker-image=$IMAGE_NAME test
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## Build on macOS
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Run the [install_macos_dependencies.sh](./tools/install_macos_dependencies.sh)
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script in the `tools/` directory. The script installs dependencies
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via [Homebrew](https://brew.sh/) and then downloads and installs the TensorFlow
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library on your machine under `/usr/local`.
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After running the script to install system dependencies, build the project with stack:
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stack test
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## Build on NixOS
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`tools/userchroot.nix` expression contains definitions to open
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chroot-environment containing necessary dependencies. Type
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$ nix-shell tools/userchroot.nix
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$ stack build --system-ghc
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to enter the environment and build the project. Note, that it is an emulation
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of common Linux environment rather than full-featured Nix package expression.
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No exportable Nix package will appear, but local development is possible.
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# Related Projects
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## Statically validated tensor shapes
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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.
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Example:
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```haskell
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{-# LANGUAGE DataKinds, ScopedTypeVariables #-}
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import Data.Maybe (fromJust)
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import Data.Vector.Sized (Vector, fromList)
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import TensorFlow.DepTyped
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test :: IO (Vector 8 Float)
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test = runSession $ do
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(x :: Placeholder "x" '[4,3] Float) <- placeholder
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let elems1 = fromJust $ fromList [1,2,3,4,1,2]
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elems2 = fromJust $ fromList [5,6,7,8]
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(w :: Tensor '[3,2] '[] Build Float) = constant elems1
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(b :: Tensor '[4,1] '[] Build Float) = constant elems2
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y = (x `matMul` w) `add` b -- y shape: [4,2] (b shape is [4.1] but `add` broadcasts it to [4,2])
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let (inputX :: TensorData "x" [4,3] Float) =
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encodeTensorData . fromJust $ fromList [1,2,3,4,1,0,7,9,5,3,5,4]
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runWithFeeds (feed x inputX :~~ NilFeedList) y
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main :: IO ()
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main = test >>= print
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```
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# License
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This project is licensed under the terms of the [Apache 2.0 license](LICENSE).
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