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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

DifferentiableSeq

object DifferentiableSeq

A namespace of common operators for Seq layers.

After importing DifferentiableSeq._, the following methods will be available on Seq layers.

Author:

杨博 (Yang Bo) <[email protected]>

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Type Members

  1. final class SeqLayerOps [Input <: Tape, ElementData, ElementDelta] extends AnyRef

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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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
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
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  10. def hashCode(): Int
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  11. final def isInstanceOf[T0]: Boolean
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  12. final def ne(arg0: AnyRef): Boolean
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  13. final def notify(): Unit
    Definition Classes
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  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  15. implicit def seqToLayer[From, Input0 <: Tape, ElementData, ElementDelta](implicit elementToLayer: Aux[From, Input0, ElementData, ElementDelta]): Aux[Seq[From], Input0, Seq[ElementData], (Int, ElementDelta)]
  16. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  17. implicit def toSeqLayerOps[From, Input <: Tape, SeqData, SeqDelta, ElementData, ElementDelta](from: From)(implicit toLayer: Aux[From, Input, SeqData, SeqDelta], toSeqLayer: <:<[Aux[Input, Aux[SeqData, SeqDelta]], Aux[Input, Aux[Seq[ElementData], (Int, ElementDelta)]]]): SeqLayerOps[Input, ElementData, ElementDelta]

    Implicitly converts any layer to SeqLayerOps, which enables common methods for Seq layers.

    Implicitly converts any layer to SeqLayerOps, which enables common methods for Seq layers.

    Example: 

    1. import com.thoughtworks.deeplearning.DifferentiableSeq._
  18. def toString(): String
    Definition Classes
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  19. final def wait(): Unit
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    @throws( ... )
  20. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  21. final def wait(arg0: Long): Unit
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    @throws( ... )
  22. object Layers

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