131 lines
4.9 KiB
Haskell
131 lines
4.9 KiB
Haskell
-- Copyright 2016 TensorFlow authors.
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--
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-- Licensed under the Apache License, Version 2.0 (the "License");
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-- you may not use this file except in compliance with the License.
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-- You may obtain a copy of the License at
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--
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-- http://www.apache.org/licenses/LICENSE-2.0
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--
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-- Unless required by applicable law or agreed to in writing, software
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-- distributed under the License is distributed on an "AS IS" BASIS,
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-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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{-# LANGUAGE OverloadedLists #-}
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{-# LANGUAGE OverloadedStrings #-}
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module Main where
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import Control.Monad.IO.Class (liftIO)
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import Data.Int (Int32, Int64)
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import Test.Framework (defaultMain, Test)
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import Lens.Family2 ((.~))
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import System.IO.Temp (withSystemTempDirectory)
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import Test.Framework.Providers.HUnit (testCase)
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import Test.HUnit ((@=?))
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import qualified Data.ByteString.Char8 as B8
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import qualified Data.Vector as V
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import qualified TensorFlow.Build as TF
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import qualified TensorFlow.Nodes as TF
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import qualified TensorFlow.Ops as TF
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import qualified TensorFlow.Session as TF
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import qualified TensorFlow.Tensor as TF
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import qualified TensorFlow.Types as TF
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-- | Test that one can easily determine number of elements in the tensor.
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testSize :: Test
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testSize = testCase "testSize" $ do
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x <- eval $ TF.size (TF.constant (TF.Shape [2, 3]) [0..5 :: Float])
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TF.Scalar (2 * 3 :: Int32) @=? x
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eval :: TF.Fetchable t a => t -> IO a
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eval = TF.runSession . TF.run
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-- | Confirms that the original example from Python code works.
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testReducedShape :: Test
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testReducedShape = testCase "testReducedShape" $ do
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x <- eval $ TF.reducedShape (TF.vector [2, 3, 5, 7 :: Int64])
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(TF.vector [1, 2 :: Int32])
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V.fromList [2, 1, 1, 7 :: Int32] @=? x
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testSaveRestore :: Test
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testSaveRestore = testCase "testSaveRestore" $
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withSystemTempDirectory "" $ \dirPath -> do
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let path = B8.pack $ dirPath ++ "/checkpoint"
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var :: TF.MonadBuild m => m (TF.Tensor TF.Ref Float)
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var = TF.zeroInitializedVariable' (TF.opName .~ "a")
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(TF.Shape [])
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TF.runSession $ do
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v <- var
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TF.assign v 134 >>= TF.run_
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TF.save path [v] >>= TF.run_
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result <- TF.runSession $ do
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v <- var
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TF.restore path v >>= TF.run_
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TF.run v
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liftIO $ TF.Scalar 134 @=? result
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-- | Test that 'placeholder' is not CSE'd.
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testPlaceholderCse :: Test
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testPlaceholderCse = testCase "testPlaceholderCse" $ TF.runSession $ do
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p1 <- TF.placeholder []
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p2 <- TF.placeholder []
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let enc :: Float -> TF.TensorData Float
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enc n = TF.encodeTensorData [] (V.fromList [n])
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result <- TF.runWithFeeds [TF.feed p1 (enc 2), TF.feed p2 (enc 3)]
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$ p1 `TF.add` p2
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liftIO $ result @=? TF.Scalar 5
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-- | Test that regular tensors can also be used for feeds, as long as they each
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-- have a different name.
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testScalarFeedCse :: Test
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testScalarFeedCse = testCase "testScalarFeedCse" $ TF.runSession $ do
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p1 <- TF.render $ TF.scalar' (TF.opName .~ "A") 0
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-- The second op is identical to the first other than its name; make sure
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-- we don't aggressively CSE them together and prevent feeding them
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-- separately.
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p2 <- TF.render $ TF.scalar' (TF.opName .~ "B") 0
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let enc :: Float -> TF.TensorData Float
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enc n = TF.encodeTensorData [] (V.fromList [n])
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result <- TF.runWithFeeds [TF.feed p1 (enc 2), TF.feed p2 (enc 3)]
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$ p1 `TF.add` p2
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liftIO $ result @=? TF.Scalar 5
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-- | See https://github.com/tensorflow/haskell/issues/92.
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-- Even though we're not explicitly evaluating `f0` until the end,
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-- it should hold the earlier value of the variable.
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testRereadRef :: Test
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testRereadRef = testCase "testReRunAssign" $ TF.runSession $ do
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w <- TF.initializedVariable 0
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f0 <- TF.run w
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TF.run_ =<< TF.assign w (TF.scalar (0.1 :: Float))
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f1 <- TF.run w
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liftIO $ (0.0, 0.1) @=? (TF.unScalar f0, TF.unScalar f1)
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-- | Test Einstein summation.
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testEinsum :: Test
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testEinsum = testCase "testEinsum" $ TF.runSession $ do
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-- Matrix multiply
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let matA = TF.constant (TF.Shape [3,3]) [1..9 :: Float]
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let matB = TF.constant (TF.Shape [3,1]) [1..3 :: Float]
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matMulOut <- TF.run $ TF.matMul matA matB
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einsumOut <- TF.run $ TF.einsum "ij,jk->ik" [matA,matB]
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liftIO $ (matMulOut :: V.Vector Float) @=? einsumOut
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-- Hadamard multiply
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hadMulOut <- TF.run $ TF.mul matA matA
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einsumHad <- TF.run $ TF.einsum "ij,ij->ij" [matA,matA]
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liftIO $ (hadMulOut :: V.Vector Float) @=? einsumHad
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main :: IO ()
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main = defaultMain
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[ testSaveRestore
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, testSize
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, testReducedShape
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, testPlaceholderCse
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, testScalarFeedCse
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, testRereadRef
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, testEinsum
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]
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