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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`.
3.3 KiB
3.3 KiB
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_, 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.initializedVariable 0
b <- 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 <- 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.Session 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
Installation Instructions
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