1
0
Fork 0
mirror of https://github.com/tensorflow/haskell.git synced 2024-11-05 02:29:41 +01:00
tensorflow-haskell/tensorflow-ops/tests/GradientTest.hs
Judah Jacobson d62c614695 Distinguish between "rendered" and "unrendered" Tensors. (#88)
Distinguish between "rendered" and "unrendered" Tensors.

There are now three types of `Tensor`:

- `Tensor Value a`: rendered value
- `Tensor Ref a`: rendered reference
- `Tensor Build a` : unrendered value

The extra bookkeeping makes it easier to track (and enforce) which tensors are
rendered or not.  For examples where this has been confusing in the past, see

With this change, pure ops look similar to before, returning `Tensor Build`
instead of `Tensor Value`.  "Stateful" (monadic) ops are unchanged.  For
example:

    add :: OneOf [..] t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
    assign :: (MonadBuild m, TensorType t)
           => Tensor Ref t -> Tensor v'2 t -> m (Tensor Ref t)

The `gradients` function now requires that the variables over which it's
differentiating are pre-rendered:

    gradients :: (..., Rendered v2) => Tensor v1 a -> [Tensor v2 a]
              -> m [Tensor Value a]

(`Rendered v2` means that `v2` is either a `Ref` or a `Value`.)

Additionally, the implementation of `gradients` now takes care to render every
intermediate value when performing the reverse accumulation.  I suspect this
fixes an exponential blowup for complicated expressions.
2017-04-06 15:10:33 -07:00

170 lines
6.1 KiB
Haskell

-- 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 NoMonomorphismRestriction #-}
{-# LANGUAGE ScopedTypeVariables #-}
import Data.Int (Int32)
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 Data.Vector as V
import qualified TensorFlow.Core as TF
import qualified TensorFlow.GenOps.Core as TF (max)
import qualified TensorFlow.Gradient as TF
import qualified TensorFlow.Ops as TF
import Proto.Tensorflow.Core.Framework.Graph (node)
import Proto.Tensorflow.Core.Framework.NodeDef (op)
testGradientSimple :: Test
testGradientSimple = testCase "testGradientSimple" $ do
let grads = do
x <- TF.render $ TF.scalar (3 :: Float)
b <- TF.render $ TF.scalar (4 :: Float)
let y = x `TF.mul` x `TF.add` b
TF.gradients y [x, b]
-- Assert that the gradients are right.
[dx, db] <- TF.runSession $ grads >>= TF.run
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 grads = do
x <- TF.render $ TF.scalar (3 :: Float)
b <- TF.render $ TF.scalar (4 :: Float)
TF.gradients x [x, b]
-- Assert that the gradients are right.
[dx, db] <- TF.runSession $ grads >>= TF.run
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 $ 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 (TF.expr x + TF.expr y * 3) [x, y] >>= TF.run
-- 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 $ 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] >>= TF.run
-- 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 $ do
x <- TF.render $ TF.vector [1]
let y = x `TF.mul` x
z = y*y
TF.gradients z [x] >>= TF.run
(4 :: Float) @=? TF.unScalar dx
testMaxGradient :: Test
testMaxGradient = testCase "testMaxGradient" $ do
[dx] <- TF.runSession $ do
x <- TF.render $ TF.vector [1, 2, 3, 0, 1 :: Float]
let y = TF.max x (0 :: TF.Tensor TF.Build Int32)
TF.gradients y [x] >>= TF.run
V.fromList [0, 0, 1, 0, 0 :: Float] @=? dx
main :: IO ()
main = googleTest [ testGradientSimple
, testGradientDisconnected
, testCreateGraphStateful
, testCreateGraphNameScopes
, testDiamond
, testMaxGradient
]