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64971c876a
- Merge tensorflow-nn and tensorflow-queue into tensorflow-ops. They don't add extra dependencies and each contain a single module, so I don't think it's worth separating them at the package level. - Remove google-shim in favor of direct use of test-framework.
103 lines
3.5 KiB
Haskell
103 lines
3.5 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 FlexibleContexts #-}
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{-# LANGUAGE OverloadedLists #-}
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module Main where
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import TensorFlow.Test (assertAllClose)
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import Test.Framework (defaultMain, Test)
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import Test.Framework.Providers.HUnit (testCase)
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import qualified Data.Vector as V
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import qualified TensorFlow.Gradient as TF
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import qualified TensorFlow.NN as TF
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import qualified TensorFlow.Ops as TF
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import qualified TensorFlow.Core as TF
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-- | These tests are ported from:
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--
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-- <tensorflow>/tensorflow/python/ops/nn_xent_tests.py
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--
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-- This is the implementation we use to check the implementation we
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-- wrote in `TensorFlow.NN.sigmoidCrossEntropyWithLogits`.
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--
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sigmoidXentWithLogits :: Floating a => Ord a => [a] -> [a] -> [a]
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sigmoidXentWithLogits logits' targets' =
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let sig = map (\x -> 1 / (1 + exp (-x))) logits'
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eps = 0.0001
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predictions = map (\p -> min (max p eps) (1 - eps)) sig
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xent y z = (-z) * (log y) - (1 - z) * log (1 - y)
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in zipWith xent predictions targets'
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data Inputs = Inputs {
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logits :: [Float]
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, targets :: [Float]
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}
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defInputs :: Inputs
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defInputs = Inputs {
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logits = [-100, -2, -2, 0, 2, 2, 2, 100]
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, targets = [ 0, 0, 1, 0, 0, 1, 0.5, 1]
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}
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testLogisticOutput :: Test
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testLogisticOutput = testCase "testLogisticOutput" $ do
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let inputs = defInputs
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r <- run $ do
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vLogits <- TF.render $ TF.vector $ logits inputs
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vTargets <- TF.render $ TF.vector $ targets inputs
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TF.sigmoidCrossEntropyWithLogits vLogits vTargets
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let ourLoss = V.fromList $ sigmoidXentWithLogits (logits inputs) (targets inputs)
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assertAllClose r ourLoss
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testLogisticOutputMultipleDim :: Test
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testLogisticOutputMultipleDim =
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testCase "testLogisticOutputMultipleDim" $ do
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let inputs = defInputs
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shape = [2, 2, 2]
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r <- run $ do
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vLogits <- TF.render $ TF.constant shape (logits inputs)
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vTargets <- TF.render $ TF.constant shape (targets inputs)
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TF.sigmoidCrossEntropyWithLogits vLogits vTargets
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let ourLoss = V.fromList $ sigmoidXentWithLogits (logits inputs) (targets inputs)
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assertAllClose r ourLoss
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testGradientAtZero :: Test
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testGradientAtZero = testCase "testGradientAtZero" $ do
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r <- run $ do
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let inputs = defInputs { logits = [0, 0], targets = [0, 1] }
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vTargets <- TF.render $ TF.vector $ targets inputs
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vLogits <- TF.render $ TF.vector $ logits inputs
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let tfLoss = TF.sigmoidCrossEntropyWithLogits vLogits vTargets
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l <- tfLoss
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TF.gradients l [vLogits]
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assertAllClose (head r) (V.fromList [0.5, -0.5])
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run :: TF.Fetchable t a => TF.Session t -> IO a
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run = TF.runSession . (>>= TF.run)
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main :: IO ()
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main = defaultMain
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[ testGradientAtZero
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, testLogisticOutput
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, testLogisticOutputMultipleDim
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]
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