1
0
Fork 0
mirror of https://github.com/tensorflow/haskell.git synced 2024-11-27 05:19:45 +01:00
tensorflow-haskell/tensorflow-ops/tests/DataFlowOpsTest.hs

66 lines
2.6 KiB
Haskell
Raw Normal View History

2016-10-24 21:26:42 +02:00
-- Copyright 2016 TensorFlow authors.
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
-- http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.
Support fetching storable vectors + use them in benchmark (#50) In addition, you can now fetch TensorData directly. This might be useful in scenarios where you feed the result of a computation back in, like RNN. Before: benchmarking feedFetch/4 byte time 83.31 μs (81.88 μs .. 84.75 μs) 0.997 R² (0.994 R² .. 0.998 R²) mean 87.32 μs (86.06 μs .. 88.83 μs) std dev 4.580 μs (3.698 μs .. 5.567 μs) variance introduced by outliers: 55% (severely inflated) benchmarking feedFetch/4 KiB time 114.9 μs (111.5 μs .. 118.2 μs) 0.996 R² (0.994 R² .. 0.998 R²) mean 117.3 μs (116.2 μs .. 118.6 μs) std dev 3.877 μs (3.058 μs .. 5.565 μs) variance introduced by outliers: 31% (moderately inflated) benchmarking feedFetch/4 MiB time 109.0 ms (107.9 ms .. 110.7 ms) 1.000 R² (0.999 R² .. 1.000 R²) mean 108.6 ms (108.2 ms .. 109.2 ms) std dev 740.2 μs (353.2 μs .. 1.186 ms) After: benchmarking feedFetch/4 byte time 82.92 μs (80.55 μs .. 85.24 μs) 0.996 R² (0.993 R² .. 0.998 R²) mean 83.58 μs (82.34 μs .. 84.89 μs) std dev 4.327 μs (3.664 μs .. 5.375 μs) variance introduced by outliers: 54% (severely inflated) benchmarking feedFetch/4 KiB time 85.69 μs (83.81 μs .. 87.30 μs) 0.997 R² (0.996 R² .. 0.999 R²) mean 86.99 μs (86.11 μs .. 88.15 μs) std dev 3.608 μs (2.854 μs .. 5.273 μs) variance introduced by outliers: 43% (moderately inflated) benchmarking feedFetch/4 MiB time 1.582 ms (1.509 ms .. 1.677 ms) 0.970 R² (0.936 R² .. 0.993 R²) mean 1.645 ms (1.554 ms .. 1.981 ms) std dev 490.6 μs (138.9 μs .. 1.067 ms) variance introduced by outliers: 97% (severely inflated)
2016-12-15 03:53:06 +01:00
{-# LANGUAGE FlexibleContexts #-}
2016-10-24 21:26:42 +02:00
{-# LANGUAGE ScopedTypeVariables #-}
import Data.Int (Int32, Int64)
import Data.List (genericLength)
import Test.Framework (defaultMain)
2016-10-24 21:26:42 +02:00
import Test.Framework.Providers.QuickCheck2 (testProperty)
import Test.HUnit ((@=?))
import Test.QuickCheck (Arbitrary(..), Property, choose, vectorOf)
import Test.QuickCheck.Monadic (monadicIO, run)
import qualified Data.Vector as V
import qualified TensorFlow.GenOps.Core as CoreOps
import qualified TensorFlow.Ops as TF
import qualified TensorFlow.Core as TF
2016-10-24 21:26:42 +02:00
-- DynamicSplit is undone with DynamicStitch to get the original input
-- back.
testDynamicPartitionStitchInverse :: forall a.
Support fetching storable vectors + use them in benchmark (#50) In addition, you can now fetch TensorData directly. This might be useful in scenarios where you feed the result of a computation back in, like RNN. Before: benchmarking feedFetch/4 byte time 83.31 μs (81.88 μs .. 84.75 μs) 0.997 R² (0.994 R² .. 0.998 R²) mean 87.32 μs (86.06 μs .. 88.83 μs) std dev 4.580 μs (3.698 μs .. 5.567 μs) variance introduced by outliers: 55% (severely inflated) benchmarking feedFetch/4 KiB time 114.9 μs (111.5 μs .. 118.2 μs) 0.996 R² (0.994 R² .. 0.998 R²) mean 117.3 μs (116.2 μs .. 118.6 μs) std dev 3.877 μs (3.058 μs .. 5.565 μs) variance introduced by outliers: 31% (moderately inflated) benchmarking feedFetch/4 MiB time 109.0 ms (107.9 ms .. 110.7 ms) 1.000 R² (0.999 R² .. 1.000 R²) mean 108.6 ms (108.2 ms .. 109.2 ms) std dev 740.2 μs (353.2 μs .. 1.186 ms) After: benchmarking feedFetch/4 byte time 82.92 μs (80.55 μs .. 85.24 μs) 0.996 R² (0.993 R² .. 0.998 R²) mean 83.58 μs (82.34 μs .. 84.89 μs) std dev 4.327 μs (3.664 μs .. 5.375 μs) variance introduced by outliers: 54% (severely inflated) benchmarking feedFetch/4 KiB time 85.69 μs (83.81 μs .. 87.30 μs) 0.997 R² (0.996 R² .. 0.999 R²) mean 86.99 μs (86.11 μs .. 88.15 μs) std dev 3.608 μs (2.854 μs .. 5.273 μs) variance introduced by outliers: 43% (moderately inflated) benchmarking feedFetch/4 MiB time 1.582 ms (1.509 ms .. 1.677 ms) 0.970 R² (0.936 R² .. 0.993 R²) mean 1.645 ms (1.554 ms .. 1.981 ms) std dev 490.6 μs (138.9 μs .. 1.067 ms) variance introduced by outliers: 97% (severely inflated)
2016-12-15 03:53:06 +01:00
(TF.TensorDataType V.Vector a, Show a, Eq a) => StitchExample a -> Property
2016-10-24 21:26:42 +02:00
testDynamicPartitionStitchInverse (StitchExample numParts values partitions) =
let splitParts :: [TF.Tensor TF.Build a] =
2016-10-24 21:26:42 +02:00
CoreOps.dynamicPartition numParts (TF.vector values) partTensor
partTensor = TF.vector partitions
restitchIndices = CoreOps.dynamicPartition numParts
(TF.vector [0..genericLength values-1])
partTensor
-- drop (numParts - 2) from both args to expose b/27343984
restitch = CoreOps.dynamicStitch restitchIndices splitParts
in monadicIO $ run $ do
fromIntegral numParts @=? length splitParts
valuesOut <- TF.runSession $ TF.run restitch
2016-10-24 21:26:42 +02:00
V.fromList values @=? valuesOut
data StitchExample a = StitchExample Int64 [a] [Int32]
deriving Show
instance Arbitrary a => Arbitrary (StitchExample a) where
arbitrary = do
-- Limits the size of the vector.
size <- choose (1, 100)
values <- vectorOf size arbitrary
numParts <- choose (2, 15)
partitions <- vectorOf size (choose (0, fromIntegral numParts - 1))
return $ StitchExample numParts values partitions
main :: IO ()
main = defaultMain
2016-10-24 21:26:42 +02:00
[ testProperty "DynamicPartitionStitchInverse"
(testDynamicPartitionStitchInverse :: StitchExample Int64 -> Property)
]