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INDArrayLayers.scala
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INDArrayLayers.scala
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package com.thoughtworks.deeplearning.plugins
import java.io.{PrintStream, PrintWriter}
import com.thoughtworks.deeplearning.DeepLearning
import com.thoughtworks.deeplearning.DeepLearning.Tape
import com.thoughtworks.feature.Factory.inject
import com.thoughtworks.feature.{Factory, ImplicitApply, PartialApply}
import com.thoughtworks.raii.asynchronous._
import org.nd4j.linalg.api.ndarray.INDArray
import scalaz.syntax.all._
import scalaz.Tags.Parallel
import scalaz.Semigroup
import org.nd4s.Implicits._
import org.nd4j.linalg.ops.transforms.Transforms
import DeepLearning.ops._
import org.nd4j.linalg.factory.Nd4j
import scala.concurrent.ExecutionContext
import com.thoughtworks.continuation._
object INDArrayLayers {
final case class MultipleException(throwableSet: Set[Throwable])
extends RuntimeException("Multiple exceptions found") {
override def toString: String = throwableSet.mkString("\n")
override def printStackTrace(): Unit = {
for (throwable <- throwableSet) {
throwable.printStackTrace()
}
}
override def printStackTrace(s: PrintStream): Unit = {
for (throwable <- throwableSet) {
throwable.printStackTrace(s)
}
}
override def printStackTrace(s: PrintWriter): Unit = {
for (throwable <- throwableSet) {
throwable.printStackTrace(s)
}
}
override def getStackTrace: Array[StackTraceElement] = synchronized {
super.getStackTrace match {
case null =>
setStackTrace(throwableSet.flatMap(_.getStackTrace)(collection.breakOut))
super.getStackTrace
case stackTrace =>
stackTrace
}
}
override def fillInStackTrace(): this.type = {
this
}
}
// Workaround for https://github.com/deeplearning4j/nd4j/issues/1869
private[plugins] implicit final class Nd4jIssues1869Workaround(indArray: INDArray) {
def broadcastFix(outputShape: Int*): INDArray = {
indArray.shape match {
case oldShape if (oldShape: Seq[Int]) == outputShape => indArray
case oldShape =>
val currentShape = oldShape.padTo(outputShape.length, 1)
currentShape.indices.foldLeft(indArray) { (indArray, i) =>
val o = outputShape(i)
if (o != 1 && o != currentShape(i)) {
currentShape(i) = o
indArray.broadcast(currentShape: _*)
} else {
indArray
}
}
}
}
}
}
/** A plugin that provides differentiable operators
* on neural networks whose [[DeepLearning.Data Data]] and [[DeepLearning.Delta Delta]] is [[org.nd4j.linalg.api.ndarray.INDArray]].
*
* @note By default, the computation in a [[INDArrayLayer]] will re-evaluate again and again
* if the `INDArrayLayer` is used by multiple other operations.
*
* This behavior is very inefficient if there is are diamond dependencies in a neural network.
* It's wise to use [[CumulativeINDArrayLayers]] instead of this `INDArrayLayers` in such neural network.
*
* @author 杨博 (Yang Bo)
*/
trait INDArrayLayers extends DoubleLayers with DoubleLiterals with ImplicitsSingleton {
import INDArrayLayers._
// TODO: Add a test for this method and auto-broadcasting on n-dimension arrays for n > 2
private def sumAs(outputDeltaValue: INDArray, shape: Array[Int]) = {
val singleElementDimension = (shape: Seq[Int]).view.zip(outputDeltaValue.shape).zipWithIndex.collect {
case ((1, originSize), dimension) if originSize > 1 => dimension
}
if (singleElementDimension.isEmpty) {
outputDeltaValue
} else {
outputDeltaValue.sum(singleElementDimension.force: _*).reshape(shape: _*)
}
}
private def autoBroadcastShape(shape1: Array[Int], shape2: Array[Int]): Array[Int] = {
require(
shape1.length == shape2.length,
raw"""Cannot broadcast between shape ${shape1.mkString("[", ",", "]")} and ${shape2.mkString("[", ",", "]")}.""")
shape1.zip(shape2).map {
case (1, bSize) => bSize
case (aSize, 1) => aSize
case (aSize, bSize) if aSize == bSize => aSize
case _ =>
throw new IllegalArgumentException(
raw"Failed to automatically broadcast between shape [${shape1.mkString(",")}] and [${shape2.mkString(",")}]}"
)
}
}
@transient
implicit private lazy val unitFutureSemigroup: Semigroup[UnitContinuation[Unit]] = {
Parallel.unsubst(
Semigroup.liftSemigroup[ParallelContinuation, Unit](
continuationParallelApplicative,
scalaz.std.anyVal.unitInstance
)
)
}
@transient
private lazy val doParallelApplicative =
com.thoughtworks.raii.asynchronous.asynchronousDoParallelApplicative(new Semigroup[Throwable] {
override def append(f1: Throwable, f2: => Throwable): Throwable =
f1 match {
case MultipleException(exceptionSet1) =>
f2 match {
case MultipleException(exceptionSet2) => MultipleException(exceptionSet1 ++ exceptionSet2)
case e: Throwable => MultipleException(exceptionSet1 + e)
}
case _: Throwable =>
f2 match {
case MultipleException(exceptionSet2) => MultipleException(exceptionSet2 + f1)
case `f1` => f1
case e: Throwable => MultipleException(Set(f1, e))
}
}
})
private def parallelApply2[A, B, C](doA: Do[A], doB: Do[B])(f: (A, B) => C): Do[C] = {
Parallel.unwrap(doParallelApplicative.apply2(Parallel(doA), Parallel(doB))(f))
}
/** The [[scala.concurrent.ExecutionContext]] used internally in plugins. */
@inject
implicit protected def deepLearningExecutionContext: ExecutionContext
def dot[Operand0, Operand1, Out <: INDArrayLayer](operand0: Operand0, operand1: Operand1)(
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
INDArrayLayer.binary(operand0, operand1) { (data0: INDArray, data1: INDArray) =>
val outputData = data0 dot data1
val delta0 = { outputDelta: INDArray =>
outputDelta dot data1.T
}
val delta1 = { outputDelta: INDArray =>
data0.T dot outputDelta
}
(outputData, delta0, delta1)
}
}
trait ImplicitsApi extends super[DoubleLiterals].ImplicitsApi with super[DoubleLayers].ImplicitsApi {
/** An implicit wrapper that adds extension methods for differentiable n-dimensional array types
* that support the [[DeepLearning]] type class.
*/
implicit final class INDArrayLayerOps[Operand0](operand0: Operand0)(
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray]) {
/** @usecase def unary_- : INDArrayLayer = ???
*/
def unary_-[Out <: INDArrayLayer](
implicit layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
INDArrayLayer.unary(operand0) { data0: INDArray =>
val outputData = -data0
val delta0 = { outputDelta: INDArray =>
-outputDelta
}
(outputData, delta0)
}
}
/** @usecase def mean: DoubleLayer = ???
*/
def mean[Out <: DoubleLayer](
implicit layerImplicits: ImplicitApply.Aux[doublePartialApplyRawForward.Rest, Out]
): Out = {
DoubleLayer(operand0.forward.flatMap { tape =>
Operators./(operand0.sum, tape.data.length.toDouble).forward
})
}
/** @usecase def mean(dimensions: Int*): INDArrayLayer = ???
*/
def mean[Out <: INDArrayLayer](dimensions: Int*)(
implicit layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]
): Out = {
INDArrayLayer(operand0.forward.flatMap { tape =>
val shape = tape.data.shape
Operators
./(operand0.sum(dimensions: _*), dimensions.map(shape(_).toDouble).product)(`INDArray/Double`)
.forward
})
}
@deprecated(message = "Use `mean` instead.", since = "2.0.0")
def meanT[Out <: DoubleLayer](
implicit layerImplicits: ImplicitApply.Aux[doublePartialApplyRawForward.Rest, Out]
): Out = {
mean
}
/** @usecase def sum(dimensions: Int*): INDArrayLayer = ???
*/
def sum[Out <: INDArrayLayer](dimensions: Int*)(
implicit layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
INDArrayLayer.unary(operand0) { data0: INDArray =>
val shape0 = data0.shape
val outputData = data0.sum(dimensions: _*)
val delta0 = { outputDelta: INDArray =>
outputDelta.broadcast(shape0: _*)
}
(outputData, delta0)
}
}
/** @usecase def sum: DoubleLayer = ???
*/
def sum[Out <: DoubleLayer](
implicit layerImplicits: ImplicitApply.Aux[doublePartialApplyRawForward.Rest, Out]): Out = {
DoubleLayer.unary(operand0) { data0: INDArray =>
val shape0 = data0.shape
val outputData = data0.sumT
val delta0 = { outputDelta: Double =>
Nd4j.valueArrayOf(shape0, outputDelta)
}
(outputData, delta0)
}
}
@deprecated(message = "Use `sum` instead.", since = "2.0.0")
def sumT[Out <: DoubleLayer](
implicit layerImplicits: ImplicitApply.Aux[doublePartialApplyRawForward.Rest, Out]): Out =
sum
/** @usecase def permute(dimensions: Int*): INDArrayLayer = ???
*/
def permute[Out <: INDArrayLayer](dimensions: Int*)(
implicit layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
INDArrayLayer.unary(operand0) { data0: INDArray =>
val shape0 = data0.shape
val outputData = data0.permute(dimensions: _*)
val delta0 = { outputDelta: INDArray =>
outputDelta.permute(shape0.indices.map(dimensions.indexOf): _*)
}
(outputData, delta0)
}
}
/** @usecase def reshape(dimensions: Int*): INDArrayLayer = ???
*/
def reshape[Out <: INDArrayLayer](dimensions: Int*)(
implicit layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
INDArrayLayer.unary(operand0) { data0: INDArray =>
val shape0 = data0.shape
val outputData = data0.reshape(dimensions: _*)
val delta0 = { outputDelta: INDArray =>
outputDelta.reshape(shape0: _*)
}
(outputData, delta0)
}
}
/** @usecase def dot(operand1: INDArray): INDArrayLayer = ???
* @usecase def dot(operand1: INDArrayLayer): INDArrayLayer = ???
* @usecase def dot(operand1: INDArrayWeights#INDArrayWeight): INDArrayLayer = ???
*/
def dot[Operand1, Out <: INDArrayLayer](operand1: Operand1)(
implicit deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
INDArrayLayers.this
.dot[Operand0, Operand1, Out](operand0, operand1)(deepLearning0, deepLearning1, layerImplicits)
}
}
implicit def `INDArray+INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.+.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: INDArray) =>
val shape0 = data0.shape
val shape1 = data1.shape
val outputShape = autoBroadcastShape(shape0, shape1)
val broadcastData0 = data0.broadcastFix(outputShape: _*)
val broadcastData1 = data1.broadcastFix(outputShape: _*)
val outputData = broadcastData0 + broadcastData1
val delta0 = { (outputDelta: INDArray) =>
sumAs(outputDelta, shape0)
}
val delta1 = { (outputDelta: INDArray) =>
sumAs(outputDelta, shape1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `INDArray+Double`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.+.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = data0 + data1
val delta0 = { (outputDelta: INDArray) =>
outputDelta
}
val delta1 = { (outputDelta: INDArray) =>
outputDelta.sumT
}
(outputData, delta0, delta1)
}
}
}
implicit def `Double+INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.+.at[Operand0, Operand1] { (operand0, operand1) =>
`INDArray+Double`[Operand1, Operand0, Out].apply(operand1, operand0)
}
}
implicit def `INDArray-INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.-.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: INDArray) =>
val shape0 = data0.shape
val shape1 = data1.shape
val outputShape = autoBroadcastShape(shape0, shape1)
val broadcastData0 = data0.broadcastFix(outputShape: _*)
val broadcastData1 = data1.broadcastFix(outputShape: _*)
val outputData = broadcastData0 - broadcastData1
val delta0 = { (outputDelta: INDArray) =>
sumAs(outputDelta, shape0)
}
val delta1 = { (outputDelta: INDArray) =>
-sumAs(outputDelta, shape1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `INDArray-Double`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.-.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = data0 - data1
val delta0 = { (outputDelta: INDArray) =>
outputDelta
}
val delta1 = { (outputDelta: INDArray) =>
-outputDelta.sumT
}
(outputData, delta0, delta1)
}
}
}
implicit def `Double-INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.-.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: Double, data1: INDArray) =>
val outputData = data1 rsub data0
val delta0 = { (outputDelta: INDArray) =>
outputDelta.sumT
}
val delta1 = { (outputDelta: INDArray) =>
-outputDelta
}
(outputData, delta0, delta1)
}
}
}
implicit def `INDArray*INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.*.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: INDArray) =>
val shape0 = data0.shape
val shape1 = data1.shape
val outputShape = autoBroadcastShape(shape0, shape1)
val broadcastData0 = data0.broadcastFix(outputShape: _*)
val broadcastData1 = data1.broadcastFix(outputShape: _*)
val outputData = broadcastData0 * broadcastData1
val delta0 = { outputDelta: INDArray =>
sumAs(outputDelta * broadcastData1, shape0)
}
val delta1 = { outputDelta: INDArray =>
sumAs(outputDelta * broadcastData0, shape1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `INDArray*Double`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.*.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = data0 * data1
val delta0 = { (outputDelta: INDArray) =>
outputDelta * data1
}
val delta1 = { (outputDelta: INDArray) =>
(data0 * outputDelta).sumT
}
(outputData, delta0, delta1)
}
}
}
implicit def `Double*INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.*.at[Operand0, Operand1] { (operand0, operand1) =>
`INDArray*Double`[Operand1, Operand0, Out].apply(operand1, operand0)
}
}
implicit def `INDArray/INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators./.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: INDArray) =>
val shape0 = data0.shape
val shape1 = data1.shape
val outputShape = autoBroadcastShape(shape0, shape1)
val broadcastData0 = data0.broadcastFix(outputShape: _*)
val broadcastData1 = data1.broadcastFix(outputShape: _*)
val outputData = broadcastData0 / broadcastData1
val delta0 = { outputDelta: INDArray =>
sumAs(outputDelta / broadcastData1, shape0)
}
val delta1 = { outputDelta: INDArray =>
sumAs(-outputDelta * broadcastData0 / (data1 * data1).broadcastFix(outputDelta.shape: _*), shape1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `INDArray/Double`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators./.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = data0 / data1
val delta0 = { (outputDelta: INDArray) =>
outputDelta / data1
}
val delta1 = { (outputDelta: INDArray) =>
-(outputDelta * data0).sumT / (data1 * data1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `Double/INDArray`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators./.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: Double, data1: INDArray) =>
val outputData = data1 rdiv data0
val delta0 = { (outputDelta: INDArray) =>
(outputDelta / data1).sumT
}
val delta1 = { (outputDelta: INDArray) =>
(outputDelta * -data0) / (data1 * data1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `min(INDArray,INDArray)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.min.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: INDArray) =>
val shape0 = data0.shape
val shape1 = data1.shape
val outputShape = autoBroadcastShape(shape0, shape1)
val broadcastData0 = data0.broadcastFix(outputShape: _*)
val broadcastData1 = data1.broadcastFix(outputShape: _*)
val outputData = Transforms.min(broadcastData0, broadcastData1)
val delta0 = { outputDelta: INDArray =>
sumAs((broadcastData0 lt broadcastData1) * outputDelta, shape0)
}
val delta1 = { outputDelta: INDArray =>
sumAs((broadcastData0 gt broadcastData1) * outputDelta, shape1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `min(INDArray,Double)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.min.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = Transforms.min(data0, data1)
val delta0 = { (outputDelta: INDArray) =>
(data0 lt data1) * outputDelta
}
val delta1 = { (outputDelta: INDArray) =>
((data0 gt data1) * outputDelta).sumT
}
(outputData, delta0, delta1)
}
}
}
implicit def `min(Double,INDArray)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.min.at[Operand0, Operand1] { (operand0, operand1) =>
`min(INDArray,Double)`[Operand1, Operand0, Out].apply(operand1, operand0)
}
}
implicit def `max(INDArray,INDArray)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.max.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: INDArray) =>
val shape0 = data0.shape
val shape1 = data1.shape
val outputShape = autoBroadcastShape(shape0, shape1)
val broadcastData0 = data0.broadcastFix(outputShape: _*)
val broadcastData1 = data1.broadcastFix(outputShape: _*)
val outputData = Transforms.max(broadcastData0, broadcastData1)
val delta0 = { outputDelta: INDArray =>
sumAs((broadcastData0 gt broadcastData1) * outputDelta, shape0)
}
val delta1 = { outputDelta: INDArray =>
sumAs((broadcastData0 lt broadcastData1) * outputDelta, shape1)
}
(outputData, delta0, delta1)
}
}
}
implicit def `max(INDArray,Double)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.max.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = Transforms.max(data0, data1)
val delta0 = { (outputDelta: INDArray) =>
(data0 gt data1) * outputDelta
}
val delta1 = { (outputDelta: INDArray) =>
((data0 lt data1) * outputDelta).sumT
}
(outputData, delta0, delta1)
}
}
}
implicit def `max(Double,INDArray)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double],
deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.max.at[Operand0, Operand1] { (operand0, operand1) =>
`max(INDArray,Double)`[Operand1, Operand0, Out].apply(operand1, operand0)
}
}
implicit def `pow(INDArray,Double)`[Operand0, Operand1, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
deepLearning1: DeepLearning.Aux[Operand1, Double, Double],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.pow.at[Operand0, Operand1] {
INDArrayLayer.binary(_, _) { (data0: INDArray, data1: Double) =>
val outputData = Transforms.pow(data0, data1)
val delta0 = { (outputDelta: INDArray) =>
outputDelta * data1 * Transforms.pow(data0, data1 - 1)
}
val delta1 = { (outputDelta: INDArray) =>
(outputDelta * Transforms.log(data0) * outputData).sumT
}
(outputData, delta0, delta1)
}
}
}
implicit def `log(INDArray)`[Operand0, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.log.at[Operand0] {
INDArrayLayer.unary(_) { data0: INDArray =>
val outputData = Transforms.log(data0)
val delta0 = { outputDelta: INDArray =>
outputDelta / data0
}
(outputData, delta0)
}
}
}
implicit def `exp(INDArray)`[Operand0, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.exp.at[Operand0] {
INDArrayLayer.unary(_) { data0: INDArray =>
val outputData = Transforms.exp(data0)
val delta0 = { outputDelta: INDArray =>
outputData * outputDelta
}
(outputData, delta0)
}
}
}
implicit def `abs(INDArray)`[Operand0, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.abs.at[Operand0] {
INDArrayLayer.unary(_) { data0: INDArray =>
val outputData = Transforms.abs(data0)
val delta0 = { outputDelta: INDArray =>
outputDelta * Transforms.sign(data0)
}
(outputData, delta0)
}
}
}
implicit def `sqrt(INDArray)`[Operand0, Out <: INDArrayLayer](
implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]) = {
Operators.sqrt.at[Operand0] {
INDArrayLayer.unary(_) { data0: INDArray =>
val outputData = Transforms.sqrt(data0)
val delta0 = { outputDelta: INDArray =>
outputData * 0.5 / outputData
}
(outputData, delta0)
}
}
}
}
override type Implicits <: ImplicitsApi
/** @template */
type INDArrayLayer <: INDArrayLayerApi with Layer
@inject
protected val indArrayLayerFactory: Factory[INDArrayLayer]
@inject
protected def indArrayRawForwardParameter: Do[Tape[INDArray, INDArray]] <:< indArrayPartialApplyRawForward.Parameter
@inject
protected val indArrayPartialApplyRawForward: PartialApply[indArrayLayerFactory.Constructor,
shapeless.Witness.`"rawForward"`.T]
trait INDArrayLayerApi extends super.LayerApi {
override type Data = INDArray
override type Delta = INDArray
/** The original forward operation passed in [[INDArrayLayer$ FloatLayer.apply]].
*
* @note This [[rawForward]] may be different from [[forward]],
* in the case of [[forward]] was overriden by other plugins, e.g. [[CumulativeINDArrayLayers]].
*/
protected val rawForward: Do[Tape[INDArray, INDArray]]
override def forward: Do[Tape[INDArray, INDArray]] = rawForward
}
object INDArrayLayer {
/** @usecase def apply(forward: Do[Tape[INDArray, INDArray]]): INDArrayLayer = ???
*
* Returns a [[INDArrayLayer]] according to the given `forward` operation.
*/
def apply[Out <: INDArrayLayer](forward: Do[Tape[INDArray, INDArray]])(
implicit layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]): Out = {
layerImplicits(
indArrayPartialApplyRawForward(indArrayLayerFactory.newInstance, indArrayRawForwardParameter(forward)))
}
/** Internal helper to create unary [[INDArrayLayer]]. */
def unary[Operand0, Input0Data, Input0Delta, Out <: INDArrayLayer](
operand0: Operand0
)(f: Input0Data => (INDArray, INDArray => Input0Delta))(
implicit deepLearning0: DeepLearning.Aux[Operand0, Input0Data, Input0Delta],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]
): Out = {
INDArrayLayer(Do.execute(()).intransitiveFlatMap { _ =>
deepLearning0.forward(operand0).map {
case Tape(data0, backward0) =>
val (outputData, delta0) = f(data0)
val outputShape = outputData.shape
def backward(doOutputDelta: Do[INDArray]) = {
backward0(Do.execute(()).intransitiveFlatMap { _ =>
doOutputDelta.map { outputDelta =>
delta0(outputDelta.broadcastFix(outputShape: _*))
}
})
}
Tape(outputData, backward)
}
})
}
/** Internal helper to create binary [[INDArrayLayer]]. */
def binary[Operand0, Operand1, Input0Data, Input0Delta, Input1Data, Input1Delta, Out <: INDArrayLayer](
operand0: Operand0,
operand1: Operand1
)(f: (Input0Data, Input1Data) => (INDArray, INDArray => Input0Delta, INDArray => Input1Delta))(
implicit deepLearning0: DeepLearning.Aux[Operand0, Input0Data, Input0Delta],
deepLearning1: DeepLearning.Aux[Operand1, Input1Data, Input1Delta],
layerImplicits: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]
): Out = {
INDArrayLayer(Do.execute(()).intransitiveFlatMap { _ =>
parallelApply2(deepLearning0.forward(operand0), deepLearning1.forward(operand1)) {
case (Tape(data0, backward0), Tape(data1, backward1)) =>
val (outputData, delta0, delta1) = f(data0, data1)
val outputShape = outputData.shape
def backward(doOutputDelta: Do[INDArray]) = {
backward0(Do.execute(()).intransitiveFlatMap { _ =>
doOutputDelta.map { outputDelta =>
delta0(outputDelta.broadcastFix(outputShape: _*))
}
}) |+| backward1(Do.execute(()).intransitiveFlatMap { _ =>
doOutputDelta.map { outputDelta =>
delta1(outputDelta.broadcastFix(outputShape: _*))
}
})
}
Tape(outputData, backward)
}
})
}
}
}