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  • package com
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  • package thoughtworks
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  • package deeplearning

    This is the documentation for the DeepLearning.Scala

    This is the documentation for the DeepLearning.Scala

    Overview

    BufferedLayer, DifferentiableAny, DifferentiableNothing, Layer, Poly and Symbolic are base packages which contains necessary operations , all other packages dependent on those base packages.

    If you want to implement a layer, you need to know how to use base packages.

    Imports guidelines

    If you want use some operations of Type T, you should import:

    import com.thoughtworks.deeplearning.DifferentiableT._

    it means: If you want use some operations of INDArray, you should import:

    import com.thoughtworks.deeplearning.DifferentiableINDArray._

    If you write something like this:

    def softmax(implicit scores: INDArray @Symbolic): INDArray @Symbolic = {
  val expScores = exp(scores)
  expScores / expScores.sum(1)
}

    If compiler shows error :

    Could not infer implicit value for com.thoughtworks.deeplearning.Symbolic[org.nd4j.linalg.api.ndarray.INDArray]

    you need add import this time :

    import com.thoughtworks.deeplearning.DifferentiableINDArray._

    If you write something like this:

    def crossEntropyLossFunction(
  implicit pair: (INDArray :: INDArray :: HNil) @Symbolic)
: Double @Symbolic = {
 val score = pair.head
 val label = pair.tail.head
 -(label * log(score * 0.9 + 0.1) + (1.0 - label) * log(1.0 - score * 0.9)).mean
}

    If the compiler shows error:

    value * is not a member of com.thoughtworks.deeplearning.Layer.Aux[com.thoughtworks.deeplearning.Layer.Tape.Aux[org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray],com.thoughtworks.deeplearning.DifferentiableINDArray.INDArrayPlaceholder.Tape]val bias = Nd4j.ones(numberOfOutputKernels).toWeight * 0.1...

    you need add import :

    import com.thoughtworks.deeplearning.Poly.MathMethods.*
import com.thoughtworks.deeplearning.DifferentiableINDArray._

    If the compiler shows error:

    not found: value log -(label * log(score * 0.9 + 0.1) + (1.0 - label) * log(1.0 - score * 0.9)).mean...

    you need add import:

    import com.thoughtworks.deeplearning.Poly.MathFunctions.*
import com.thoughtworks.deeplearning.DifferentiableINDArray._

    Those + - * / and log exp abs max min are defined at MathMethods and MathFunctions，those method are been implemented at DifferentiableType，so you need to import the implicit of DifferentiableType.

    Composability

    Neural networks created by DeepLearning.scala are composable. You can create large networks by combining smaller networks. If two larger networks share some sub-networks, the weights in shared sub-networks trained with one network affect the other network.

    Definition Classes
    thoughtworks
    See also

    Compose

  • CumulativeLayer
  • DifferentiableAny
  • DifferentiableBoolean
  • DifferentiableCoproduct
  • DifferentiableDouble
  • DifferentiableFloat
  • DifferentiableHList
  • DifferentiableInt
  • DifferentiableNothing
  • DifferentiableSeq
  • Layer
  • Poly
  • Symbolic
o

com.thoughtworks.deeplearning

DifferentiableHList

object DifferentiableHList

A namespace of common operators for HList layers.

After importing DifferentiableHList._, the following methods will be available on HList layers.

Author:

杨博 (Yang Bo) <[email protected]>

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Type Members

  1. final class HConsLayerOps [Input <: Tape, HeadData, HeadDelta, TailData <: HList, TailDelta <: Coproduct] extends AnyRef
  2. final class HListLayerOps [Input <: Tape, TailData <: HList, TailDelta <: Coproduct] extends AnyRef

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  11. implicit def hconsToLiteral[Head, HeadData, HeadDelta, Tail <: HList, TailData <: HList, TailDelta <: Coproduct](implicit headToLiteral: Lazy[Aux[Head, HeadData, HeadDelta]], tailToLiteral: Lazy[Aux[Tail, TailData, TailDelta]]): Aux[::[Head, Tail], ::[HeadData, TailData], :+:[HeadDelta, TailDelta]]
  12. implicit def hnilToLiteral[From <: HNil]: Aux[From, HNil, CNil]
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. final def notify(): Unit
    Definition Classes
    AnyRef
  16. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  17. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  18. implicit def toHConsLayerOps[From, Input <: Tape, OutputData, OutputDelta, HeadData, HeadDelta, TailData <: HList, TailDelta <: Coproduct](from: From)(implicit toLayer: Aux[From, Input, OutputData, OutputDelta], toHListLayer: <:<[Aux[Input, Aux[OutputData, OutputDelta]], Aux[Input, Aux[::[HeadData, TailData], :+:[HeadDelta, TailDelta]]]]): HConsLayerOps[Input, HeadData, HeadDelta, TailData, TailDelta]
  19. implicit def toHListLayerOps[From, Input <: Tape, TailData <: HList, TailDelta <: Coproduct](from: From)(implicit toLayer: Aux[From, Input, TailData, TailDelta]): HListLayerOps[Input, TailData, TailDelta]

    Implicitly converts any layer to HListLayerOps, which enables common methods for HList layers.

    Implicitly converts any layer to HListLayerOps, which enables common methods for HList layers.

    Example: 

    1. import com.thoughtworks.deeplearning.DifferentiableHList._
  20. def toString(): String
    Definition Classes
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  21. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  23. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  24. object Layers

Inherited from AnyRef

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