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 DifferentiableAny

    A namespace of common operators for any layers.

    A namespace of common operators for any layers.

    After importing DifferentiableAny._, the following methods will be available on any layers.

    Author:

    杨博 (Yang Bo) <[email protected]>

    Definition Classes
    deeplearning
  • AnyLayerOps
  • Layers
  • Trainable

final class AnyLayerOps[Input <: Tape, OutputData, OutputDelta] extends AnyRef

A helper that contains common operations for any layers.

Example: 

  1. import com.thoughtworks.deeplearning.DifferentiableAny._
(input:INDArray @Symbolic).compose(anotherLayer)
Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. AnyLayerOps
  2. AnyRef
  3. Any
Implicitly
  1. by any2stringadd
  2. by StringFormat
  3. by Ensuring
  4. by ArrowAssoc
  1. Hide All
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new AnyLayerOps(anyLayer: Aux[Input, Aux[OutputData, OutputDelta]])

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. def +(other: String): String
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to any2stringadd[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (AnyLayerOps[Input, OutputData, OutputDelta], B)
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to ArrowAssoc[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc
    Annotations
    @inline()
  5. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def compose[NewInput <: Tape](g: Aux[NewInput, Input]): Aux[NewInput, Aux[OutputData, OutputDelta]]

    [use case]

    [use case]
    Full Signature

    def compose[G, NewInput <: Tape, InputData, InputDelta](g: G)(implicit toLayer: Aux[G, NewInput, InputData, InputDelta], toInput: <:<[Aux[NewInput, Aux[InputData, InputDelta]], Aux[NewInput, Input]]): Aux[NewInput, Aux[OutputData, OutputDelta]]

    Example: 

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
def composeNetwork(implicit thisLayer: INDArray @Symbolic)(anotherLayer: INDArray @Symbolic) = {
  thisLayer.compose(anotherLayer)
  9. def ensuring(cond: (AnyLayerOps[Input, OutputData, OutputDelta]) ⇒ Boolean, msg: ⇒ Any): AnyLayerOps[Input, OutputData, OutputDelta]
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  10. def ensuring(cond: (AnyLayerOps[Input, OutputData, OutputDelta]) ⇒ Boolean): AnyLayerOps[Input, OutputData, OutputDelta]
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  11. def ensuring(cond: Boolean, msg: ⇒ Any): AnyLayerOps[Input, OutputData, OutputDelta]
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  12. def ensuring(cond: Boolean): AnyLayerOps[Input, OutputData, OutputDelta]
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def formatted(fmtstr: String): String
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to StringFormat[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  17. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  23. def predict[InputData, InputDelta](inputData: InputData)(implicit ev: <:<[Aux[Input, Aux[OutputData, OutputDelta]], Aux[Aux[InputData, InputDelta], Aux[OutputData, OutputDelta]]]): OutputData

    Returns the result of inputData's forward.

    Returns the result of inputData's forward.

    Example: 

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
def composeNetwork(implicit inputData: INDArray @Symbolic) = ???
val predictor = composeNetwork
predictor.predict(testData)
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. def train[InputData, InputDelta](inputData: InputData)(implicit ev: <:<[Aux[Input, Aux[OutputData, OutputDelta]], Aux[Aux[InputData, InputDelta], Aux[OutputData, OutputDelta]]], outputDataIsOutputDelta: Trainable[OutputData, OutputDelta]): OutputData

    Updates those weights embedded in anyLayer according to the result of inputData's backward.

    Updates those weights embedded in anyLayer according to the result of inputData's backward.

    Example: 

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
def composeNetwork(implicit input: INDArray @Symbolic) = ???
val yourNetwork=composeNetwork
yourNetwork.train(testData)
  27. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. def withOutputDataHook(hook: (OutputData) ⇒ Unit): Aux[Input, Aux[OutputData, OutputDelta]]

    Returns a new Layer which is a wrapper of result of anyLayer's forward and will invoke hook(anyLayer.forward(input).value).

    Returns a new Layer which is a wrapper of result of anyLayer's forward and will invoke hook(anyLayer.forward(input).value). In DeepLearning.Scala, operation is not immediately run, but first filled the network with placeholders, until the entire network is running, the real data will replace placeholders. So if you want to know some layers's intermediate state, you need to use withOutputDataHook.

    Example: 

    1. import com.thoughtworks.deeplearning.DifferentiableAny._
(var:INDArray @Symbolic).withOutputDataHook{ data => println(data) }
  31. def →[B](y: B): (AnyLayerOps[Input, OutputData, OutputDelta], B)
    Implicit
    This member is added by an implicit conversion from AnyLayerOps[Input, OutputData, OutputDelta] to ArrowAssoc[AnyLayerOps[Input, OutputData, OutputDelta]] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from AnyLayerOps[Input, OutputData, OutputDelta] to any2stringadd[AnyLayerOps[Input, OutputData, OutputDelta]]

Inherited by implicit conversion StringFormat from AnyLayerOps[Input, OutputData, OutputDelta] to StringFormat[AnyLayerOps[Input, OutputData, OutputDelta]]

Inherited by implicit conversion Ensuring from AnyLayerOps[Input, OutputData, OutputDelta] to Ensuring[AnyLayerOps[Input, OutputData, OutputDelta]]

Inherited by implicit conversion ArrowAssoc from AnyLayerOps[Input, OutputData, OutputDelta] to ArrowAssoc[AnyLayerOps[Input, OutputData, OutputDelta]]

Ungrouped