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added matrix factorization test (#101)

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Chris Mckinlay 2017-04-27 17:05:34 -07:00 committed by fkm3
parent 51c883684b
commit 09c792b84c
2 changed files with 66 additions and 0 deletions

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@ -45,6 +45,24 @@ Test-Suite RegressionTest
, tensorflow-core-ops , tensorflow-core-ops
, tensorflow-ops , tensorflow-ops
Test-Suite MatrixTest
default-language: Haskell2010
type: exitcode-stdio-1.0
main-is: MatrixTest.hs
hs-source-dirs: tests
build-depends: base
, HUnit
, random
, google-shim
, tensorflow
, tensorflow-core-ops
, tensorflow-ops
, tensorflow-test
, test-framework
, test-framework-hunit
, transformers
, vector
Test-Suite BuildTest Test-Suite BuildTest
default-language: Haskell2010 default-language: Haskell2010
type: exitcode-stdio-1.0 type: exitcode-stdio-1.0

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@ -0,0 +1,48 @@
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE OverloadedLists #-}
import Control.Monad.IO.Class (liftIO)
import Control.Monad (replicateM_, zipWithM)
import qualified TensorFlow.GenOps.Core as TF (square, rank)
import qualified TensorFlow.Core as TF
import qualified TensorFlow.Gradient as TF
import qualified TensorFlow.Ops as TF
import qualified Data.Vector as V
import Test.Framework (Test)
import Test.Framework.Providers.HUnit (testCase)
import TensorFlow.Test (assertAllClose)
import Google.Test (googleTest)
randomParam :: TF.Shape -> TF.Session (TF.Tensor TF.Value Float)
randomParam (TF.Shape shape) = TF.truncatedNormal (TF.vector shape)
reduceMean :: TF.Tensor v Float -> TF.Tensor TF.Build Float
reduceMean xs = TF.mean xs (TF.range 0 (TF.rank xs) 1)
fitMatrix :: Test
fitMatrix = testCase "fitMatrix" $ TF.runSession $ do
u <- TF.initializedVariable =<< randomParam [2, 1]
v <- TF.initializedVariable =<< randomParam [1, 2]
let ones = [1, 1, 1, 1] :: [Float]
matx = TF.constant [2, 2] ones
diff = matx `TF.sub` (u `TF.matMul` v)
loss = reduceMean $ TF.square diff
trainStep <- gradientDescent 0.01 loss [u, v]
replicateM_ 300 (TF.run trainStep)
(u',v') <- TF.run (u, v)
-- ones = u * v
liftIO $ assertAllClose (V.fromList ones) ((*) <$> u' <*> v')
gradientDescent :: Float
-> TF.Tensor TF.Build 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
main :: IO ()
main = googleTest [ fitMatrix ]