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Add gradient of 'maximum' and 'gradForBinaryCwise'

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`maximum` gradient uses `gradForBinaryCwise` which may be useful for other
binary componentwise op gradients
This commit is contained in:
Christian Berentsen 2017-06-16 10:26:10 +02:00 committed by fkm3
parent ea30577264
commit bebc4aa7d9
2 changed files with 50 additions and 2 deletions
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26
tensorflow-ops/src/TensorFlow/Gradient.hs
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@ -431,6 +431,22 @@ flatSlice t begin size = CoreOps.slice t (vector [begin]) (vector [size])
nodeDefName :: NodeDef -> NodeName
nodeDefName = NodeName . view name
-- | Gradient helper for binary component wise operations
-- See https://github.com/tensorflow/tensorflow/blob/e9de087fa7f59c39bbe12ac2c83c5547c83f746c/tensorflow/core/ops/math_grad.cc#L329
gradForBinaryCwise :: ( OneOf '[ Int32, Int64, Float, Double, Complex Float, Complex Double ] t
)
=> (Tensor v1 t, Tensor v1 t)
-> (Tensor v1 t, Tensor v1 t)
-> [ Maybe (Tensor Build t) ]
gradForBinaryCwise (x, gx) (y, gy) =
[ Just dx
, Just dy ]
where
dx = reshape (sum gx rx) sx
dy = reshape (sum gy ry) sy
sx = shape x -- (x :: Tensor Build t)
sy = shape y -- (y :: Tensor Build t)
(rx, ry) = broadcastGradientArgs sx sy
-- | The gradient function for an op type.
--
@ -483,6 +499,15 @@ opGrad "Max" _ [toT -> x, toT -> indices] [dz] =
-- Min and Max have identical gradient implementations.
opGrad "Min" u v w = opGrad "Max" u v w
-- Element wise maximum gradient
-- See https://github.com/tensorflow/tensorflow/blob/e9de087fa7f59c39bbe12ac2c83c5547c83f746c/tensorflow/core/ops/math_grad.cc#L473
opGrad "Maximum" _ [toT -> x, toT -> y] [dz] =
gradForBinaryCwise (x, gx) (y, gy)
where
xmask = CoreOps.greaterEqual x y
gx = CoreOps.select xmask dz (CoreOps.zerosLike dz)
gy = CoreOps.select (CoreOps.logicalNot xmask) dz (CoreOps.zerosLike dz)
opGrad "Sum" _ [toT -> x, toT -> indices] [dz] =
[ Just $ CoreOps.tile grad tileScaling, Nothing ]
where
@ -731,6 +756,7 @@ numOutputs o =
"Log" -> 1
"MatMul" -> 1
"Max" -> 1
"Maximum" -> 1
"MaxPool" -> 1
"Mean" -> 1
"Min" -> 1

26
tensorflow-ops/tests/GradientTest.hs
View file

@ -29,7 +29,7 @@ import qualified Data.Vector as V
import Control.Monad.IO.Class (liftIO)
import qualified TensorFlow.Core as TF
import qualified TensorFlow.GenOps.Core as TF (max, tile)
import qualified TensorFlow.GenOps.Core as TF (max, tile, maximum)
import qualified TensorFlow.Gradient as TF
import qualified TensorFlow.Ops as TF hiding (zeroInitializedVariable)
import qualified TensorFlow.Output as TF
@ -173,6 +173,27 @@ testMaxGradient = testCase "testMaxGradient" $ do
TF.gradients y [x] >>= TF.run
V.fromList [0, 0, 1, 0, 0 :: Float] @=? dx
-- run single test like this:
-- stack --docker --docker-image=$IMAGE_NAME test tensorflow-ops:GradientTest --test-arguments -t"*MaximumGrad*"
testMaximumGrad :: Test
testMaximumGrad = testCase "testMaximumGrad" $ do
[gx, gy] <- TF.runSession $ do
x <- TF.render $ TF.vector [0 :: Float]
y <- TF.render $ TF.vector [0 :: Float]
let z = TF.maximum x y
TF.gradients z [x, y] >>= TF.run
V.fromList [1] @=? gx
V.fromList [1] @=? gy
testMaximumGradGrad :: Test
testMaximumGradGrad = testCase "testMaximumGradGrad" $ do
[ggx] <- TF.runSession $ do
x <- TF.render $ TF.vector [2 :: Float]
y <- TF.render $ TF.vector [1 :: Float]
let z = TF.maximum x y
[gx, _gy] <- TF.gradients z [x, y]
TF.gradients gx [x] >>= TF.run
V.fromList [0] @=? ggx
testReluGrad :: Test
testReluGrad = testCase "testReluGrad" $ do
@ -191,7 +212,6 @@ testReluGradGrad = testCase "testReluGradGrad" $ do
TF.gradients y' [x] >>= TF.run
V.fromList [0] @=? dx
testFillGrad :: Test
testFillGrad = testCase "testFillGrad" $ do
[dx] <- TF.runSession $ do
@ -309,6 +329,8 @@ main = defaultMain
, testDiamond
, testAddNGradient
, testMaxGradient
, testMaximumGrad
, testMaximumGradGrad
, testReluGrad
, testReluGradGrad
, testFillGrad