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This change adds a class that both `Build` and `Session` are instances of:
class MonadBuild m where
build :: Build a -> m a
All stateful ops (generated and manually written) now have a signature that returns
an instance of `MonadBuild` (rather than just `Build`). For example:
assign_ :: (MonadBuild m, TensorType t)
=> Tensor Ref t -> Tensor v t -> m (Tensor Ref t)
This lets us remove a bunch of spurious calls to `build` in user code. It also
lets us replace the pattern `buildAnd run foo` with the simpler pattern `foo >>= run`
(or `run =<< foo`, which is sometimes nicer when foo is a complicated expression).
I went ahead and deleted `buildAnd` altogether since it seems to lead to
confusion; in particular a few tests had `buildAnd run . pure` which is
actually equivalent to just `run`.
99 lines
3.3 KiB
Markdown
99 lines
3.3 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-0.1.0.0/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_, zipWithM)
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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.Gradient as TF
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import qualified TensorFlow.Ops 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` w) `TF.add` 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 <- 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 (w, b)
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return (w', b')
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gradientDescent :: Float
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-> TF.Tensor TF.Value Float
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-> [TF.Tensor TF.Ref Float]
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-> TF.Session TF.ControlNode
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gradientDescent alpha loss params = do
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let applyGrad param grad =
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TF.assign param (param `TF.sub` (TF.scalar alpha `TF.mul` grad))
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TF.group =<< zipWithM applyGrad params =<< TF.gradients loss params
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```
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# Installation Instructions
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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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## Build on Mac OS X
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Run the [install_osx_dependencies.sh](./tools/install_osx_dependencies.sh)
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script in the `tools/` directory. The script installs dependencies
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via [Homebrew](http://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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