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added matrix factorization test (#101)
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@ -45,6 +45,24 @@ Test-Suite RegressionTest
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, tensorflow-core-ops
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, tensorflow-ops
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Test-Suite MatrixTest
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default-language: Haskell2010
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type: exitcode-stdio-1.0
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main-is: MatrixTest.hs
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hs-source-dirs: tests
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build-depends: base
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, HUnit
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, random
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, google-shim
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, tensorflow
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, tensorflow-core-ops
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, tensorflow-ops
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, tensorflow-test
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, test-framework
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, test-framework-hunit
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, transformers
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, vector
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Test-Suite BuildTest
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default-language: Haskell2010
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type: exitcode-stdio-1.0
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48
tensorflow-ops/tests/MatrixTest.hs
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48
tensorflow-ops/tests/MatrixTest.hs
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@ -0,0 +1,48 @@
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE OverloadedLists #-}
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import Control.Monad.IO.Class (liftIO)
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import Control.Monad (replicateM_, zipWithM)
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import qualified TensorFlow.GenOps.Core as TF (square, rank)
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import qualified TensorFlow.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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import qualified Data.Vector as V
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import Test.Framework (Test)
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import Test.Framework.Providers.HUnit (testCase)
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import TensorFlow.Test (assertAllClose)
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import Google.Test (googleTest)
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randomParam :: TF.Shape -> TF.Session (TF.Tensor TF.Value Float)
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randomParam (TF.Shape shape) = TF.truncatedNormal (TF.vector shape)
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reduceMean :: TF.Tensor v Float -> TF.Tensor TF.Build Float
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reduceMean xs = TF.mean xs (TF.range 0 (TF.rank xs) 1)
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fitMatrix :: Test
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fitMatrix = testCase "fitMatrix" $ TF.runSession $ do
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u <- TF.initializedVariable =<< randomParam [2, 1]
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v <- TF.initializedVariable =<< randomParam [1, 2]
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let ones = [1, 1, 1, 1] :: [Float]
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matx = TF.constant [2, 2] ones
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diff = matx `TF.sub` (u `TF.matMul` v)
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loss = reduceMean $ TF.square diff
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trainStep <- gradientDescent 0.01 loss [u, v]
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replicateM_ 300 (TF.run trainStep)
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(u',v') <- TF.run (u, v)
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-- ones = u * v
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liftIO $ assertAllClose (V.fromList ones) ((*) <$> u' <*> v')
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gradientDescent :: Float
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-> TF.Tensor TF.Build 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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main :: IO ()
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main = googleTest [ fitMatrix ]
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