Packages

  • package root
    Definition Classes
    root
  • package com
    Definition Classes
    root
  • package thoughtworks
    Definition Classes
    com
  • 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

  • object Symbolic extends LowPrioritySymbolic

    There are two ways to convert a value to Layer.

    There are two ways to convert a value to Layer.

    The first way is invoke toLayer, such as:

    def createMyNeuralNetwork(implicit input: Float @Symbolic): Float @Symbolic = {
  val floatLayer: Float @Symbolic = 1.0f.toLayer
  floatLayer
}

    The second way is autoToLayer, such as:

    def createMyNeuralNetwork(implicit input: Float @Symbolic): Float @Symbolic = {
  val floatLayer: Float @Symbolic = 1.0f
  floatLayer
}

    In order to compile the above code through, you will need:

    import com.thoughtworks.deeplearning.Symbolic._
import com.thoughtworks.deeplearning.Symbolic
import com.thoughtworks.deeplearning.DifferentiableFloat._
    Definition Classes
    deeplearning
  • Layers
  • ToLayer
  • ToLayerOps
  • ToLiteral
  • ToTapeOps

object ToLayer extends ToLayerLowPriorityImplicits

Linear Supertypes
ToLayerLowPriorityImplicits, AnyRef, Any
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Type Members

  1. type Aux[From, Input <: Tape, OutputData0, OutputDelta0] = ToLayer[From, Input] { ... /* 2 definitions in type refinement */ }
  2. type OfPlaceholder[From, Input <: Tape, OutputPlaceholder <: Identity[_, _]] = Aux[From, Input, ToLayer.OfPlaceholder.differentiablePlaceholder.type.Data, ToLayer.OfPlaceholder.differentiablePlaceholder.type.Delta] forSome {val differentiablePlaceholder: OutputPlaceholder}

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. implicit def isLayerToLayer[NativeInput, NativeOutput, InputData0, InputDelta0, OutputData0, OutputDelta0]: Aux[(IsLayer { ... /* 4 definitions in type refinement */ })#@, Layer.Tape.Aux[InputData0, InputDelta0], OutputData0, OutputDelta0]
    Definition Classes
    ToLayerLowPriorityImplicits
  13. implicit def layerToLayer[Input <: Tape, OutputData0, OutputDelta0]: Aux[Layer.Aux[Input, Layer.Tape.Aux[OutputData0, OutputDelta0]], Input, OutputData0, OutputDelta0]
  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. implicit def placeholderToLayer[From, InputData, InputDelta, OutputData0, OutputDelta0](implicit inputPlaceholder: Identity[InputData, InputDelta], toLiteral: ToLiteral.Aux[From, OutputData0, OutputDelta0]): Aux[From, Layer.Tape.Aux[InputData, InputDelta], OutputData0, OutputDelta0]
  18. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  19. implicit def toLayerOfPlaceholder[Input0 <: Tape, OutputPlaceholder <: Identity[_, _]]: OfPlaceholder[Layer.Aux[Input0, ToLayerLowPriorityImplicits.toLayerOfPlaceholder.OutputPlaceholder.Tape], Input0, OutputPlaceholder]
    Definition Classes
    ToLayerLowPriorityImplicits
  20. def toString(): String
    Definition Classes
    AnyRef → Any
  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( ... )

Inherited from ToLayerLowPriorityImplicits

Inherited from AnyRef

Inherited from Any

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