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baa501b262
Required by #187. The version we were using is old enough that it doesn't work with the latest stackage LTS. haskellstack.org says There is also a Ubuntu package for Ubuntu 16.10 and up, but the distribution's Stack version lags behind, ... So, instead of asking them to update it, it's probably better to download the tar of the version we want. Somehow updating stack surfaced a new pedantic warning in GradientTest, so I've fixed that as well.
440 lines
17 KiB
Haskell
440 lines
17 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 OverloadedStrings #-}
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{-# LANGUAGE NoMonomorphismRestriction #-}
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{-# LANGUAGE ScopedTypeVariables #-}
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{-# LANGUAGE FlexibleContexts #-}
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import Data.Int (Int32, Int64)
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import Data.List (sort)
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import qualified Data.List as List
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import Data.ProtoLens.TextFormat (showMessage)
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import Test.Framework (defaultMain, Test)
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import Lens.Family2 ((^..), (.~))
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import Test.Framework.Providers.HUnit (testCase)
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import Test.HUnit ((@=?), assertEqual)
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import qualified Data.Vector as V
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import System.Random (randomIO, randomRIO)
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import Control.Monad(forM_, replicateM, zipWithM)
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import Control.Monad.IO.Class (liftIO)
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.GenOps.Core as TF (conv2DBackpropInput', max, maximum, tile)
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import qualified TensorFlow.Gradient as TF
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import qualified TensorFlow.Ops as TF hiding (zeroInitializedVariable)
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import qualified TensorFlow.Output as TF
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import qualified TensorFlow.Types as TF
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import qualified TensorFlow.Variable as TF
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import Proto.Tensorflow.Core.Framework.Graph (node)
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import Proto.Tensorflow.Core.Framework.NodeDef (op)
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import qualified Data.ByteString.Char8 as BS
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testGradientSimple :: Test
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testGradientSimple = testCase "testGradientSimple" $ do
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let grads = do
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x <- TF.render $ TF.scalar (3 :: Float)
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b <- TF.render $ TF.scalar (4 :: Float)
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let y = x `TF.mul` x `TF.add` b
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TF.gradients y [x, b]
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-- Assert that the gradients are right.
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[dx, db] <- TF.runSession $ grads >>= TF.run
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6 @=? TF.unScalar dx
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1 @=? TF.unScalar db
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-- Assert that the graph has the expected ops.
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let graphDef = TF.asGraphDef grads
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putStrLn $ showMessage graphDef
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let ops = graphDef ^.. node . traverse . op
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expected = [ "Const"
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, "Mul"
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, "Const"
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, "Add"
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-- Default output gradient of y.
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, "Shape"
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, "Const"
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, "Fill"
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-- Add gradient.
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, "Shape"
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, "Shape"
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, "BroadcastGradientArgs"
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, "Sum"
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, "Sum"
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, "Reshape"
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, "Reshape"
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-- Mul gradient.
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, "Shape"
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-- This Op gets dedup'd because the inputs are the same.
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-- TODO(fmayle): The same would happen to the Mul and Sum ops
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-- below if the gradient function didn't multiply one as
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-- 'dz * y' and the other as 'x * dz'. We could change the
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-- order, but I'm going to keep it the same as the python
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-- version for now.
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--
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-- , "Shape"
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, "BroadcastGradientArgs"
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, "Mul"
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, "Mul"
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, "Sum"
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, "Sum"
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, "Reshape"
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, "Reshape"
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-- AddN to combine x's output gradients.
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, "AddN"
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]
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sort expected @=? sort ops
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testGradientDisconnected :: Test
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testGradientDisconnected = testCase "testGradientDisconnected" $ do
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let grads = do
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x <- TF.render $ TF.scalar (3 :: Float)
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b <- TF.render $ TF.scalar (4 :: Float)
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TF.gradients x [x, b]
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-- Assert that the gradients are right.
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[dx, db] <- TF.runSession $ grads >>= TF.run
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1 @=? TF.unScalar dx
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0 @=? TF.unScalar db
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-- Assert that the graph has the expected ops.
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let graphDef = TF.asGraphDef grads
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putStrLn $ showMessage graphDef
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let ops = graphDef ^.. node . traverse . op
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expected = [ "Const"
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, "Const"
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-- Default output gradient of x.
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, "Shape"
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, "Const"
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, "Fill"
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-- Default output gradient of b.
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, "ZerosLike"
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]
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sort expected @=? sort ops
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-- Test that identical "stateful" ops work with createGraph.
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testCreateGraphStateful :: Test
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testCreateGraphStateful = testCase "testCreateGraphStateful" $ do
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[dx, dy] <- TF.runSession $ do
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let shape = TF.constant (TF.Shape [1]) [1]
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x :: TF.Tensor TF.Value Float <- TF.truncatedNormal shape
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y :: TF.Tensor TF.Value Float <- TF.truncatedNormal shape
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TF.gradients (TF.expr x + TF.expr y * 3) [x, y] >>= TF.run
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-- If this test fails, it will likely be caused by an exception within
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-- `TF.gradients`. These asserts are extra.
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1 @=? TF.unScalar dx
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3 @=? TF.unScalar dy
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-- Test that name scopes work with createGraph.
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testCreateGraphNameScopes :: Test
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testCreateGraphNameScopes = testCase "testCreateGraphNameScopes" $ do
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[dx] <- TF.runSession $ do
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let shape = TF.constant (TF.Shape [1]) [1]
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x :: TF.Tensor TF.Value Float <-
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TF.withNameScope "foo" (TF.truncatedNormal shape)
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TF.gradients x [x] >>= TF.run
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-- If this test fails, it will likely be caused by an exception within
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-- `TF.gradients`. This assert is extra.
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1 @=? TF.unScalar dx
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-- Test that createGraph can handle graphs with diamond shapes.
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testDiamond :: Test
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testDiamond = testCase "testDiamond" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [1]
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let y = x `TF.mul` x
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z = y*y
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TF.gradients z [x] >>= TF.run
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(4 :: Float) @=? TF.unScalar dx
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testAddNGradient :: Test
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testAddNGradient = testCase "testAddNGradient" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [1, 2, 0 :: Float]
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let y = TF.addN [x, x]
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TF.gradients y [x] >>= TF.run
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V.fromList [2, 2, 2 :: Float] @=? dx
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testMaxGradient :: Test
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testMaxGradient = testCase "testMaxGradient" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [1, 2, 3, 0, 1 :: Float]
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let y = TF.max x (0 :: TF.Tensor TF.Build Int32)
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TF.gradients y [x] >>= TF.run
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V.fromList [0, 0, 1, 0, 0 :: Float] @=? dx
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testConcatGradient :: Test
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testConcatGradient = testCase "testConcatGradient" $ do
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[dv,dv'] <- TF.runSession $ do
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v <- TF.render $ TF.vector [1 :: Float]
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v' <- TF.render $ TF.vector [2 :: Float]
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let y = TF.concat (TF.scalar 0) [ v, v' ]
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TF.gradients y [v,v'] >>= TF.run
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V.fromList [1 :: Float] @=? dv
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V.fromList [1 :: Float] @=? dv'
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[dw,dw'] <- TF.runSession $ do
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w <- TF.render $ TF.vector [1,2,3,4 :: Float]
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w' <- TF.render $ TF.vector [5,6,7,8 :: Float]
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let y = TF.concat (TF.scalar 0) [ w, w', w ]
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TF.gradients y [w,w'] >>= TF.run
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V.fromList [2,2,2,2 :: Float] @=? dw
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V.fromList [1,1,1,1 :: Float] @=? dw'
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verifyConcatGradients :: [[Int64]] -> Int32 -> IO ()
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verifyConcatGradients shapes concatDim = do
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let floatsFromShape :: [Int64] -> IO [Float]
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floatsFromShape shape = replicateM (fromIntegral $ List.product shape) randomIO
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constantZip = zipWithM $ \x shape -> TF.render $ TF.constant (TF.Shape shape) x
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inputGrads <- mapM floatsFromShape shapes
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inputs <- mapM floatsFromShape shapes
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dinputs <- TF.runSession $ do
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inputTensors <- inputs `constantZip` shapes
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inputGradTensors <- inputGrads `constantZip` shapes
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inputTensor <- TF.render $ TF.concat (TF.scalar concatDim) inputTensors
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inputGradTensor <- TF.render $ TF.concat (TF.scalar concatDim) inputGradTensors
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output <- TF.render $ inputTensor `TF.mul` inputGradTensor
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TF.gradients output inputTensors >>= TF.run
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(V.fromList <$> inputGrads) @=? dinputs
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-- This test checks that the gradient of a concat op
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-- is correct along the first, second, and third dimension.
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testConcatGradientSimple :: Test
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testConcatGradientSimple = testCase "testConcatGradientSimple" $ do
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-- The following check is equivalent to ConcatTest._testGradientsSimple from
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-- tensorflow/tensorflow/compiler/tests/concat_ops_test.py
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verifyConcatGradients [[10,x,2] | x <- [1,2,6]] 1
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-- The following check is equivalent to ConcatTest._testGradientsFirstDim from
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-- tensorflow/tensorflow/compiler/tests/concat_ops_test.py
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verifyConcatGradients [[x,10,2] | x <- [1,2,6]] 0
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-- The following check is equivalent to ConcatTest._testGradientsLastDim from
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-- tensorflow/tensorflow/compiler/tests/concat_ops_test.py
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verifyConcatGradients [[10,2,x] | x <- [1,2,6]] 2
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-- This test checks that the gradient of a concat op
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-- along a random dimension across random shapes is as expected.
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-- This test is inspired by ConcatTest._RunAndVerifyGradientsRandom from
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-- tensorflow/tensorflow/compiler/tests/concat_ops_test.py, but also
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-- verifies the gradient along negative concat dimensions.
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testConcatRunAndVerifyGradientsRandom :: Test
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testConcatRunAndVerifyGradientsRandom = testCase "testConcatRunAndVerifyGradientsRandom" $
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forM_ [1..5 :: Int] $ \_ -> do
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(shapes' :: [Int64]) <- replicateM 5 $ randomRIO (1, 5)
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(numTensors :: Int) <- randomRIO (2, 10)
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(concatDim :: Int) <- randomRIO (-4, 4)
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(concatDimSizes :: [Int64]) <- replicateM numTensors $ randomRIO (1, 5)
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let update i xs x = take i xs ++ x: drop (i+1) xs
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concatDim' = concatDim `mod` length shapes'
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shapes = map (update concatDim' shapes') concatDimSizes
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verifyConcatGradients shapes $ fromIntegral concatDim
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-- run single test like this:
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-- stack --docker --docker-image=$IMAGE_NAME test tensorflow-ops:GradientTest --test-arguments -t"*MaximumGrad*"
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testMaximumGrad :: Test
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testMaximumGrad = testCase "testMaximumGrad" $ do
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[gx, gy] <- TF.runSession $ do
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x <- TF.render $ TF.vector [0 :: Float]
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y <- TF.render $ TF.vector [0 :: Float]
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let z = TF.maximum x y
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TF.gradients z [x, y] >>= TF.run
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V.fromList [1] @=? gx
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V.fromList [1] @=? gy
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testMaximumGradGrad :: Test
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testMaximumGradGrad = testCase "testMaximumGradGrad" $ do
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[ggx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [2 :: Float]
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y <- TF.render $ TF.vector [1 :: Float]
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let z = TF.maximum x y
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[gx, _gy] <- TF.gradients z [x, y]
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TF.gradients gx [x] >>= TF.run
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V.fromList [0] @=? ggx
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testReluGrad :: Test
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testReluGrad = testCase "testReluGrad" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [2 :: Float]
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let y = TF.relu x
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TF.gradients y [x] >>= TF.run
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V.fromList [1] @=? dx
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testReluGradGrad :: Test
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testReluGradGrad = testCase "testReluGradGrad" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [2 :: Float]
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let y = TF.relu x
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[y'] <- TF.gradients y [x]
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TF.gradients y' [x] >>= TF.run
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V.fromList [0] @=? dx
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testFillGrad :: Test
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testFillGrad = testCase "testFillGrad" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.scalar (9 :: Float)
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let shape = TF.vector [2, 3 :: Int32]
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let y = TF.fill shape x
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TF.gradients y [x] >>= TF.run
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V.fromList [6] @=? dx
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testTileGrad :: Test
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testTileGrad = testCase "testTileGrad" $ do
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[dx] <- TF.runSession $ do
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x <- TF.render $ TF.vector [5, 9 :: Float]
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let multiples = TF.vector [2 :: Int32]
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let y = TF.tile x multiples
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TF.gradients y [x] >>= TF.run
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V.fromList [2, 2] @=? dx
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testTile2DGrad :: Test
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testTile2DGrad = testCase "testTileGrad2D" $ do
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(dx, shapeDX, shapeX) <- TF.runSession $ do
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let shape = TF.vector [3, 2 :: Int32]
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x <- TF.render $ TF.fill shape (TF.scalar (1::Float))
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let multiples = TF.vector [2, 3 :: Int32]
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let y = TF.tile x multiples
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[dx] <- TF.gradients y [x]
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TF.run (dx, TF.shape dx, TF.shape x)
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shapeX @=? (shapeDX :: V.Vector Int32)
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V.fromList [6, 6, 6, 6, 6, 6::Float] @=? (dx :: V.Vector Float)
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matMulGradient :: Test
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matMulGradient = testCase "matMulGradients" $ do
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let dfBuild = do
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x <- TF.render $ TF.zeros $ TF.Shape [3, 1 :: Int64]
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w <- TF.zeroInitializedVariable $ TF.Shape [1, 2 :: Int64]
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let f = x `TF.matMul` TF.readValue w :: TF.Tensor TF.Build Float
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dfs <- TF.gradients f [x]
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return (x, dfs)
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(xShape, dxShape) <- TF.runSession $ do
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(x, [dx]) <- TF.build dfBuild
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TF.run (TF.shape x, TF.shape dx)
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assertEqual "Shape of gradient must match shape of input" xShape (dxShape :: V.Vector Int32)
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-- test that gradient of matMul can be taken gradient of
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matMulGradGrad :: Test
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matMulGradGrad = testCase "matMulGradGrad" $ do
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let width = 2 :: Int64
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batch = 4 :: Int64
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let tower = do
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x <- TF.render $ TF.zeros $ TF.Shape [batch, 1]
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w <- TF.zeroInitializedVariable $ TF.Shape [1, width]
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let f = x `TF.matMul` TF.readValue w
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[dfdx] <- TF.gradients f [x]
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let f'x = TF.reduceSum dfdx
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[dfdw] <- TF.gradients f'x [w] -- take gradient again (this time over w)
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return [TF.readValue w, TF.expr dfdw]
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TF.runSession $ do
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[w, dfdw] <- TF.build tower
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(wShape, dfdwShape) <- TF.run (TF.shape w, TF.shape dfdw)
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liftIO $ assertEqual "Shape of gradient must match input" wShape (dfdwShape :: V.Vector Int32)
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let step = w `TF.add` dfdw
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w0 <- TF.run step
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liftIO $ V.fromList [4, 4 :: Float] @=? w0
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-- test that gradient of matMul deals correctly with transpose_a and transpose_b
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matMulTransposeGradient :: (Bool, Bool) -> Test
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matMulTransposeGradient txw = testCase ("matMulTransposeGradients " ++ show txw) $ do
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let (transposeX, transposeW) = txw
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let dfBuild = do
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let xShape = TF.Shape [3, 1 :: Int64]
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let xZeros = TF.zeros xShape
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x <- TF.render $ if transposeX then TF.matTranspose xZeros else xZeros
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variable <- TF.zeroInitializedVariable $ TF.Shape [1, 2 :: Int64]
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let wv = if transposeW then TF.matTranspose (TF.readValue variable) else TF.readValue variable
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let f = TF.matMul' (transAttrs transposeX transposeW) x wv :: TF.Tensor TF.Build Float
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w <- TF.render wv
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ds <- TF.gradients f [x, w]
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return (x, w, ds)
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TF.runSession $ do
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(x, w, [dx, dw]) <- TF.build dfBuild
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xShape <- TF.run $ TF.shape x
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dxShape <- TF.run $ TF.shape dx
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liftIO $ assertEqual "xShape must match dxShape" xShape (dxShape :: V.Vector Int32)
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wShape <- TF.run $ TF.shape w
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dwShape <- TF.run $ TF.shape dw
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liftIO $ assertEqual "wShape must match dwShape" wShape (dwShape :: V.Vector Int32)
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transAttrs :: (TF.Attribute a,
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TF.Attribute b) =>
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a -> b -> TF.OpDef -> TF.OpDef
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transAttrs a b =
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(TF.opAttr "transpose_a" .~ a) . (TF.opAttr "transpose_b" .~ b)
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testConv2DBackpropInputGrad :: Test
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testConv2DBackpropInputGrad = testCase "testConv2DBackpropInputGrad" $ do
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(dx, shapeDX, shapeX) <- TF.runSession $ do
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let conv_input_shape = TF.vector [1, 2, 2, 1 :: Int32] -- [batch, h, w, in_channels]
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let conv_out_shape = TF.vector [1, 1, 1, 1 :: Int32] -- [batch, h, w, out_channels]
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x <- TF.render $ TF.fill conv_out_shape (TF.scalar (1::Float))
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let filterShape = TF.vector [2, 2, 1, 1 :: Int32] -- [fh, fw, inc, out]
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filter' <- TF.render $ TF.fill filterShape (TF.scalar (1::Float))
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let y = TF.conv2DBackpropInput'
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( (TF.opAttr "strides" .~ [1::Int64, 1, 1, 1])
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. (TF.opAttr "padding" .~ (BS.pack "VALID"))
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. (TF.opAttr "data_format" .~ (BS.pack "NHWC"))
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)
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conv_input_shape filter' x
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[dx] <- TF.gradients y [x]
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TF.run (dx, TF.shape dx, TF.shape x)
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shapeX @=? (shapeDX :: V.Vector Int32)
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V.fromList [4::Float] @=? (dx :: V.Vector Float)
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main :: IO ()
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main = defaultMain
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[ testGradientSimple
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, testGradientDisconnected
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, testCreateGraphStateful
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, testCreateGraphNameScopes
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, testDiamond
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, testAddNGradient
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, testMaxGradient
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, testConcatGradient
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, testConcatGradientSimple
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, testConcatRunAndVerifyGradientsRandom
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, testMaximumGrad
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, testMaximumGradGrad
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, testReluGrad
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, testReluGradGrad
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, testFillGrad
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, testTileGrad
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, testTile2DGrad
|
|
, matMulGradient
|
|
, matMulGradGrad
|
|
, matMulTransposeGradient (False, False)
|
|
, matMulTransposeGradient (False, True)
|
|
, matMulTransposeGradient (True, False)
|
|
, matMulTransposeGradient (True, True)
|
|
, testConv2DBackpropInputGrad
|
|
]
|