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Merge pull request #212 from Atry/EagerExecution
Add an Eager LDK for eager execution
2 parents 46c063b + c468364 commit 3c322e4

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9 files changed

+225
-3
lines changed

9 files changed

+225
-3
lines changed

‎plugins-CumulativeFloatLayers/build.sbt‎

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@@ -10,3 +10,7 @@ exampleSuperTypes += "_root_.com.thoughtworks.deeplearning.scalatest.Thoughtwork
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libraryDependencies += "com.thoughtworks.each" %% "each" % "3.3.1" % Test
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scalacOptions += "-Ypartial-unification"
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addCompilerPlugin("com.thoughtworks.dsl" %% "compilerplugins-bangnotation" % "1.0.0-RC9")
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addCompilerPlugin("com.thoughtworks.dsl" %% "compilerplugins-reseteverywhere" % "1.0.0-RC9")

‎plugins-CumulativeFloatLayers/src/test/scala/com/thoughtworks/deeplearning/plugins/CumulativeFloatLayersSpec.scala‎

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@@ -378,4 +378,36 @@ final class CumulativeFloatLayersSpec
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}
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}
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"EagerExecution" in {
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val hyperparameters =
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Factory[FloatTraining with Operators with FloatLiterals with CumulativeFloatLayers with ImplicitsSingleton with FixedLearningRate]
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.newInstance(fixedLearningRate = 1.0f)
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import hyperparameters.implicits._
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val weight = hyperparameters.FloatWeight(1.0f)
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def myNetwork(input: Float): hyperparameters.FloatLayer = {
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// FIXME: inlining !-notation does not compile due to https://github.com/ThoughtWorksInc/Dsl.scala/issues/119
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// 6.7f + !(input + weight) + weight + 5.5f
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val f = !(input + weight)
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6.7f + f + weight + 5.5f
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}: @com.thoughtworks.dsl.Dsl.reset
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def train(inputData: Float): Future[Float] = {
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myNetwork(inputData).train
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}
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for {
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_ <- train(1.0f)
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_ <- train(1.0f)
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_ <- train(1.0f)
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_ <- train(1.0f)
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_ <- train(1.0f)
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} yield weight.data should be(-4)
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}
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}

‎plugins-EagerExecution/build.sbt‎

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@@ -0,0 +1,11 @@
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libraryDependencies += "com.thoughtworks.dsl" %% "dsl" % "1.0.0-RC9"
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libraryDependencies += "com.thoughtworks.dsl" %% "domains-scalaz" % "1.0.0-RC9" % Test
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addCompilerPlugin("com.thoughtworks.dsl" %% "compilerplugins-bangnotation" % "1.0.0-RC9")
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addCompilerPlugin("com.thoughtworks.dsl" %% "compilerplugins-reseteverywhere" % "1.0.0-RC9")
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fork in Test := true
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enablePlugins(Example)

‎plugins-EagerExecution/src/main/scala/com/thoughtworks/deeplearning/plugins/EagerExecution.scala‎

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@@ -0,0 +1,49 @@
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package com.thoughtworks.deeplearning.plugins
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import com.thoughtworks.deeplearning.DeepLearning
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import com.thoughtworks.deeplearning.plugins.EagerExecution.Eager
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import com.thoughtworks.dsl.Dsl
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import com.thoughtworks.dsl.Dsl.{Keyword, shift}
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import com.thoughtworks.feature.Factory
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import scala.annotation.compileTimeOnly
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/**
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* @author 杨博 (Yang Bo)
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*/
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trait EagerExecution extends Layers {
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//
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// trait LayerApi extends super.LayerApi {
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//
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//
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// @shift
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// @compileTimeOnly(
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// """This method requires the compiler plugin: `addCompilerPlugin("com.thoughtworks.dsl" %% "compilerplugins-bangnotation" % "latest.release")` and must only be called inside a code block annotated as `@reset`.""")
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// final def data : Data = {
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// throw new IllegalAccessException(
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// """This method requires the compiler plugin: `addCompilerPlugin("com.thoughtworks.dsl" %% "compilerplugins-bangnotation" % "latest.release")` and must only be called inside a code block annotated as `@reset`."""
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// )
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// }
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//
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// @inline
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// final def cpsApply[Domain](handler: Data => Domain): Domain = {
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// ???
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//// dsl.interpret(this, handler)
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// }
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//
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// }
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//
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// type Layer <: LayerApi
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// def Eager[A](a: A)(implicit deepLearning: DeepLearning[A]): Eager[A, deepLearning.Data, deepLearning.Delta] = {
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// new Eager[A, deepLearning.Data, deepLearning.Delta](a, deepLearning)
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// }
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}
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object EagerExecution {
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final case class Eager[Differentiable, Data, Delta](differentiable: Differentiable)(
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implicit
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deepLearning: DeepLearning.Aux[Differentiable, Data, Delta])
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extends Keyword[Eager[Differentiable, Data, Delta], Data]
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}

‎plugins-EagerExecution/src/test/scala/com/thoughtworks/deeplearning/plugins/EagerExecutionSpec.scala‎

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@@ -0,0 +1,73 @@
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package com.thoughtworks.deeplearning
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package plugins
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import com.thoughtworks.deeplearning.plugins.EagerExecution.Eager
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import com.thoughtworks.feature.Factory
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import org.scalactic.ErrorMessage
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import org.scalatest._
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import com.thoughtworks.future._
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import com.thoughtworks.deeplearning.scalatest.ThoughtworksFutureToScalaFuture
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import scalaz.std.iterable._
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object EagerExecutionSpec {
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trait FixedLearningRate extends LearningRate {
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def fixedLearningRate: scala.Float
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trait FloatOptimizerApi extends super.FloatOptimizerApi { this: FloatOptimizer =>
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final def learningRate: scala.Float = fixedLearningRate
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}
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override type FloatOptimizer <: FloatOptimizerApi with Optimizer
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}
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trait LearningRate extends FloatWeights {
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trait FloatOptimizerApi extends super.FloatOptimizerApi { this: FloatOptimizer =>
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def learningRate: scala.Float
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override def delta: scala.Float = super.delta * learningRate
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}
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override type FloatOptimizer <: FloatOptimizerApi with Optimizer
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}
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}
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/**
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* @author 杨博 (Yang Bo)
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*/
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final class EagerExecutionSpec extends AsyncFreeSpec with Matchers with Inside with ThoughtworksFutureToScalaFuture {
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import EagerExecutionSpec._
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"EagerExecution" in {
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val hyperparameters =
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Factory[FloatTraining with EagerExecution with Operators with FloatLiterals with CumulativeFloatLayers with ImplicitsSingleton with FixedLearningRate]
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.newInstance(fixedLearningRate = 1.0f)
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import hyperparameters.FloatLayer
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import hyperparameters.implicits._
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val weight = hyperparameters.FloatWeight(1.0f)
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def myNetwork(input: Float): FloatLayer = {
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// FIXME: inlining !-notation does not compile due to https://github.com/ThoughtWorksInc/Dsl.scala/issues/119
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// 6.7f + !Eager(input + weight) + weight + 5.5f
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val f = !Eager(input + weight)
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6.7f + f + weight + 5.5f
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}: @com.thoughtworks.dsl.Dsl.reset
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def train(inputData: Float): Future[Float] = {
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myNetwork(inputData).train
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}
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for {
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_ <- train(1.0f)
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_ <- train(1.0f)
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_ <- train(1.0f)
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_ <- train(1.0f)
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_ <- train(1.0f)
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} yield weight.data should be(-4)
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}
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}

‎plugins-FloatLayers/src/main/scala/com/thoughtworks/deeplearning/plugins/FloatLayers.scala‎

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@@ -4,13 +4,15 @@ import com.thoughtworks.deeplearning.DeepLearning.Tape
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import com.thoughtworks.feature.{Factory, ImplicitApply, PartialApply}
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import com.thoughtworks.feature.Factory.inject
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import com.thoughtworks.raii.asynchronous._
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import scalaz.syntax.all._
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import scala.annotation.meta.getter
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import scalaz.Apply
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import com.thoughtworks.continuation._
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import com.thoughtworks.future._
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import DeepLearning.ops._
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import com.thoughtworks.deeplearning.plugins.Layers.Eager
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import com.thoughtworks.dsl.Dsl
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/** A plugin that provides differentiable operators
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* on neural networks whose [[DeepLearning.Data Data]] and [[DeepLearning.Delta Delta]] is [[scala.Float]].
@@ -26,6 +28,18 @@ import DeepLearning.ops._
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trait FloatLayers extends Layers {
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trait ImplicitsApi extends super[Layers].ImplicitsApi {
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implicit def eagerFloatDsl[Differentiable, Data, Delta, Constructor, Out <: FloatLayer](
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implicit implicitApply: ImplicitApply.Aux[floatPartialApplyRawForward.Rest, Out]
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): Dsl[Eager[Differentiable, Data, Delta], FloatLayer, Data] = {
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new Dsl[Eager[Differentiable, Data, Delta], FloatLayer, Data] {
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def interpret(keyword: Eager[Differentiable, Data, Delta], handler: Data => FloatLayer): Out =
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FloatLayer(
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keyword.deepLearning.forward(keyword.operand0).flatMap { tape =>
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handler(tape.data).internalForward
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}
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)
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}
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}
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/** An implicit wrapper that adds extension methods for differentiable float types
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* that support the [[DeepLearning]] type class.
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*/
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protected val rawForward: Do[Tape[Float, Float]]
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/** A bridge for calling [[rawForward]] in [[FloatLayers]] */
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private[FloatLayers] final def internalForward: Do[Tape[Float, Float]] = rawForward
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override def forward: Do[Tape[Float, Float]] = rawForward
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}
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object FloatLayer {
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/** @usecase def apply(forward: Do[Tape[Float, Float]]): FloatLayer = ???

‎plugins-INDArrayLayers/src/main/scala-2.11/com/thoughtworks/deeplearning/plugins/INDArrayLayers.scala‎

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@@ -8,7 +8,6 @@ import com.thoughtworks.feature.Factory.inject
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import com.thoughtworks.feature.{Factory, ImplicitApply, PartialApply}
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import com.thoughtworks.raii.asynchronous._
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import org.nd4j.linalg.api.ndarray.INDArray
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import scalaz.syntax.all._
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import scalaz.Tags.Parallel
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import scalaz.Semigroup
@@ -19,6 +18,8 @@ import org.nd4j.linalg.factory.Nd4j
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import scala.concurrent.ExecutionContext
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import com.thoughtworks.continuation._
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import com.thoughtworks.deeplearning.plugins.Layers.Eager
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import com.thoughtworks.dsl.Dsl
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object INDArrayLayers {
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@@ -176,6 +177,19 @@ trait INDArrayLayers extends DoubleLayers with DoubleLiterals with ImplicitsSing
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}
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trait ImplicitsApi extends super[DoubleLiterals].ImplicitsApi with super[DoubleLayers].ImplicitsApi {
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implicit def eagerINDArrayDsl[Differentiable, Data, Delta, Constructor, Out <: INDArrayLayer](
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implicit implicitApply: ImplicitApply.Aux[indArrayPartialApplyRawForward.Rest, Out]
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): Dsl[Eager[Differentiable, Data, Delta], INDArrayLayer, Data] = {
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new Dsl[Eager[Differentiable, Data, Delta], INDArrayLayer, Data] {
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def interpret(keyword: Eager[Differentiable, Data, Delta], handler: Data => INDArrayLayer): Out =
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INDArrayLayer(
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keyword.deepLearning.forward(keyword.operand0).flatMap { tape =>
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handler(tape.data).internalForward
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}
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)
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}
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}
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/** An implicit wrapper that adds extension methods for differentiable n-dimensional array types
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* that support the [[DeepLearning]] type class.
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*/
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*/
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protected val rawForward: Do[Tape[INDArray, INDArray]]
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/** A bridge for calling [[rawForward]] in [[INDArrayLayers]] */
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private[INDArrayLayers] final def internalForward: Do[Tape[INDArray, INDArray]] = rawForward
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override def forward: Do[Tape[INDArray, INDArray]] = rawForward
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}
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object INDArrayLayer {

‎plugins-Layers/build.sbt‎

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@@ -6,3 +6,4 @@ libraryDependencies += "com.thoughtworks.feature" %% "implicitapply" % "2.3.0-M8
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libraryDependencies += "com.thoughtworks.feature" %% "factory" % "2.3.0-M8"
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libraryDependencies += "com.thoughtworks.dsl" %% "dsl" % "1.0.0-RC9"

‎plugins-Layers/src/main/scala/com/thoughtworks/deeplearning/plugins/Layers.scala‎

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@@ -3,20 +3,24 @@ package plugins
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import java.util.logging.Logger
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import com.thoughtworks.deeplearning.DeepLearning.Tape
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import com.thoughtworks.dsl.Dsl.Keyword
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import com.thoughtworks.feature.{Factory, ImplicitApply, PartialApply, The}
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import com.thoughtworks.feature.Factory.inject
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import com.thoughtworks.raii.asynchronous.Do
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import com.thoughtworks.raii.asynchronous.Do._
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import com.thoughtworks.raii.shared._
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import shapeless.{Poly1, Poly2}
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import shapeless.poly.Case1
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import scalaz.syntax.all._
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import scala.language.implicitConversions
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import scala.annotation.meta.getter
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import com.thoughtworks.future.Future
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/** A plugin that enables [[Layer]] in neural networks. */
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trait Layers {
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import com.thoughtworks.deeplearning.plugins.Layers._
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trait LayerApi {
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type Data
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type Delta
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type Layer <: LayerApi
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trait ImplicitsApi {
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@inline
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implicit def implicitEager[Operand0](a: Operand0)(
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implicit deepLearning: DeepLearning[Operand0]): Eager[Operand0, deepLearning.Data, deepLearning.Delta] = {
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new Eager[Operand0, deepLearning.Data, deepLearning.Delta](a)(deepLearning)
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}
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implicit def layerDeepLearning[From, Data0, Delta0](implicit asLayer: From <:< LayerApi {
3748
type Data = Data0
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type Delta = Delta0
@@ -51,3 +62,9 @@ trait Layers {
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type Implicits <: ImplicitsApi
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}
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object Layers {
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final case class Eager[Operand0, Data, Delta](operand0: Operand0)(
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implicit val deepLearning: DeepLearning.Aux[Operand0, Data, Delta])
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extends Keyword[Eager[Operand0, Data, Delta], Data]
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}

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