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36 lines
1.3 KiB
Haskell
36 lines
1.3 KiB
Haskell
{-# 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_)
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import qualified Data.Vector as V
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.GenOps.Core as TF (square)
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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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import Test.Framework (defaultMain, Test)
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import Test.Framework.Providers.HUnit (testCase)
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import TensorFlow.Test (assertAllClose)
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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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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` (TF.readValue u `TF.matMul` TF.readValue v)
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loss = TF.reduceMean $ TF.square diff
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trainStep <- TF.minimizeWith (TF.gradientDescent 0.01) loss [u, v]
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replicateM_ 1000 (TF.run trainStep)
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(u',v') <- TF.run (TF.readValue u, TF.readValue v)
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-- ones = u * v
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liftIO $ assertAllClose (V.fromList ones) ((*) <$> u' <*> v')
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main :: IO ()
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main = defaultMain [ fitMatrix ]
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