tensorflow-haskell/tensorflow-ops/tests/GradientTest.hs

-- 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.

{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE ScopedTypeVariables #-}

import Data.List (sort)
import Data.ProtoLens.TextFormat (showMessage)
import Google.Test (googleTest)
import Lens.Family2 ((^..))
import Test.Framework (Test)
import Test.Framework.Providers.HUnit (testCase)
import Test.HUnit ((@=?))

import qualified TensorFlow.Build as TF
import qualified TensorFlow.Gradient as TF
import qualified TensorFlow.Nodes as TF
import qualified TensorFlow.Ops as TF
import qualified TensorFlow.Session as TF
import qualified TensorFlow.Tensor as TF
import qualified TensorFlow.Types as TF

import Proto.Tensorflow.Core.Framework.Graph (node)
import Proto.Tensorflow.Core.Framework.NodeDef (op)

testGradientSimple :: Test
testGradientSimple = testCase "testGradientSimple" $ do
    let x = TF.scalar (3 :: Float)
        b = TF.scalar (4 :: Float)
        y = x*x + b
        grads = TF.gradients y [x, b]
    -- Assert that the gradients are right.
    [dx, db] <- TF.runSession $ TF.buildAnd TF.run grads
    6 @=? TF.unScalar dx
    1 @=? TF.unScalar db
    -- Assert that the graph has the expected ops.
    let graphDef = TF.asGraphDef grads
    putStrLn $ showMessage graphDef
    let ops = graphDef ^.. node . traverse . op
        expected = [ "Const"
                   , "Mul"
                   , "Const"
                   , "Add"
                     -- Default output gradient of y.
                   , "Shape"
                   , "Const"
                   , "Fill"
                     -- Add gradient.
                   , "Shape"
                   , "Shape"
                   , "BroadcastGradientArgs"
                   , "Sum"
                   , "Sum"
                   , "Reshape"
                   , "Reshape"
                     -- Mul gradient.
                   , "Shape"
                   -- This Op gets dedup'd because the inputs are the same.
                   -- TODO(fmayle): The same would happen to the Mul and Sum ops
                   -- below if the gradient function didn't multiply one as
                   -- 'dz * y' and the other as 'x * dz'. We could change the
                   -- order, but I'm going to keep it the same as the python
                   -- version for now.
                   --
                   -- , "Shape"
                   , "BroadcastGradientArgs"
                   , "Mul"
                   , "Mul"
                   , "Sum"
                   , "Sum"
                   , "Reshape"
                   , "Reshape"
                     -- AddN to combine x's output gradients.
                   , "AddN"
                   ]
    sort expected @=? sort ops

testGradientDisconnected :: Test
testGradientDisconnected = testCase "testGradientDisconnected" $ do
    let x = TF.scalar (3 :: Float)
        b = TF.scalar (4 :: Float)
        grads = TF.gradients x [x, b]
    -- Assert that the gradients are right.
    [dx, db] <- TF.runSession $ TF.buildAnd TF.run grads
    1 @=? TF.unScalar dx
    0 @=? TF.unScalar db
    -- Assert that the graph has the expected ops.
    let graphDef = TF.asGraphDef grads
    putStrLn $ showMessage graphDef
    let ops = graphDef ^.. node . traverse . op
        expected = [ "Const"
                   , "Const"
                     -- Default output gradient of x.
                   , "Shape"
                   , "Const"
                   , "Fill"
                     -- Default output gradient of b.
                   , "ZerosLike"
                   ]
    sort expected @=? sort ops


-- Test that identical "stateful" ops work with createGraph.
testCreateGraphStateful :: Test
testCreateGraphStateful = testCase "testCreateGraphStateful" $ do
    [dx, dy] <- TF.runSession $ TF.buildAnd TF.run $ do
        let shape = TF.constant (TF.Shape [1]) [1]
        x :: TF.Tensor TF.Value Float <- TF.truncatedNormal shape
        y :: TF.Tensor TF.Value Float <- TF.truncatedNormal shape
        TF.gradients (x + y*3) [x, y]
    -- If this test fails, it will likely be caused by an exception within
    -- `TF.gradients`. These asserts are extra.
    1 @=? TF.unScalar dx
    3 @=? TF.unScalar dy


-- Test that name scopes work with createGraph.
testCreateGraphNameScopes :: Test
testCreateGraphNameScopes = testCase "testCreateGraphNameScopes" $ do
    [dx] <- TF.runSession $ TF.buildAnd TF.run $ do
        let shape = TF.constant (TF.Shape [1]) [1]
        x :: TF.Tensor TF.Value Float <-
            TF.withNameScope "foo" (TF.truncatedNormal shape)
        TF.gradients x [x]
    -- If this test fails, it will likely be caused by an exception within
    -- `TF.gradients`. This assert is extra.
    1 @=? TF.unScalar dx


-- Test that createGraph can handle graphs with diamond shapes.
testDiamond :: Test
testDiamond = testCase "testDiamond" $ do
    [dx] <- TF.runSession $ TF.buildAnd TF.run $ do
        let x = TF.vector [1]
            y = x*x
            z = y*y
        TF.gradients z [x]
    (4 :: Float) @=? TF.unScalar dx


main :: IO ()
main = googleTest [ testGradientSimple
                  , testGradientDisconnected
                  , testCreateGraphStateful
                  , testCreateGraphNameScopes
                  , testDiamond
                  ]