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

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

trait Symbolic[NativeOutput] extends AnyRef

Provides @Symbolic annotation to create symbolic methods, in which you can create Layers from mathematical formulas.

Symbolic is a dependent type class that calculates a specific Layer type according to NativeOutput. Combining with implicit-dependent-type compiler plugin, it can be treated as a type annotation in the form of NativeOutput @Symbolic, converting NativeOutput to a specific Layer type.

Three usages of @Symbolic

Implicit parameter types used for symbol methods

In case that the implicit parameter of an method is marked with @Symbolic, then this method is symbol method. The implicit parameter type marked with @Symbolic is the input type of this symbol method. In this case, NativeOutput @Symbolic will be expanded as:

Identity[NativeOutput, Derivative type of NativeOutput]

For example:

def sumNetwork(implicit scores: INDArray @Symbolic): Double = {
  exp(scores).sum
}

In the above code, because the derivative type of INDArray is also INDArray, the input type INDArray @Symbolic of sumNetwork, once expanded, is Identity[INDArray, INDArray]

Used for symbol method internal variable and return value

A NativeOutput @Symbolic inside a symbol method, or at the return position of a symbol method, will be expanded as:

Layer.Aux[Tape.Aux[value type of input type, derivative type of input type], Tape.Aux[NativeOutput, derivative type of NativeOutput]]

For example:

def sumNetwork(implicit scores: INDArray @Symbolic): Double @Symbolic = {
  val expScores: INDArray @Symbolic = exp(scores)
  val result: Double @Symbolic = expScores.sum
  result
}

In the above code, the type INDArray @Symbolic of expScores is expanded as:

Layer.Aux[Tape.Aux[INDArray, INDArray], Tape.Aux[INDArray, INDArray]]

The type Double @Symbolic of result is expanded as:

Layer.Aux[Tape.Aux[INDArray, INDArray], Tape.Aux[Double, Double]]
Used for cases excluding symbol method

(NativeInput => NativeOutput) @Symbolic outside a symbol method, will be expanded as:

Layer.Aux[Tape.Aux[NativeInput, derivative type of NativeInput], Tape.Aux[NativeOutput, derivative type of NativeOutput]]

For example:

val predictor: (INDArray => Double) @Symbolic = sumNetwork

In the above code, type (INDArray => Double) @Symbolic of predictor is expanded as:

Layer.Aux[Tape.Aux[INDArray, INDArray], Tape.Aux[Double, Double]]

Custom symbol type

The @Symbolic determines the mapping relation between the primitive type and derivative by checking Symbolic.ToLiteral implicit value. Therefore, @Symbolic can be a custom symbol type once you define your own the implicit Symbolic.ToLiteral.

For example, if you want to support Short @Symbolic, using Float as the derivative type of Short, then you can conduct the follows:

implicit object ShortToLiteral extends ToLiteral[Short] {
  override type Data = Short
  override type Delta = Float
  override def apply(data: Short) = Literal(data)
}

def makeShortNetwork(implicit input: Short @Symbolic): Short @Symbolic = {
  input
}

val shortNetwork: (Short => Short) @Symbolic = makeShortNetwork

Thus, type of shortNetwork is expanded as:

Layer.Aux[Tape.Aux[Short, Float], Tape.Aux[Short, Float]]
Annotations
@implicitNotFound( ... )
See also

Layer.Tape#Delta

Symbolic.ToLiteral

Symbolic.Layers.Identity

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Inherited
  1. Symbolic
  2. AnyRef
  3. Any
Implicitly
  1. by autoToLayer
  2. by ToTapeOps
  3. by ToLayerOps
  4. by any2stringadd
  5. by StringFormat
  6. by Ensuring
  7. by ArrowAssoc
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Type Members

  1. abstract type @ <: Layer

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 Symbolic[NativeOutput] to any2stringadd[Symbolic[NativeOutput]] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (Symbolic[NativeOutput], B)
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to ArrowAssoc[Symbolic[NativeOutput]] 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 ensuring(cond: (Symbolic[NativeOutput]) ⇒ Boolean, msg: ⇒ Any): Symbolic[NativeOutput]
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to Ensuring[Symbolic[NativeOutput]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  9. def ensuring(cond: (Symbolic[NativeOutput]) ⇒ Boolean): Symbolic[NativeOutput]
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to Ensuring[Symbolic[NativeOutput]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  10. def ensuring(cond: Boolean, msg: ⇒ Any): Symbolic[NativeOutput]
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to Ensuring[Symbolic[NativeOutput]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  11. def ensuring(cond: Boolean): Symbolic[NativeOutput]
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to Ensuring[Symbolic[NativeOutput]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. def formatted(fmtstr: String): String
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to StringFormat[Symbolic[NativeOutput]] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  16. def forward: ((input: _1.Input)_1.Output) forSome {val _1: Layer { ... /* 2 definitions in type refinement */ }}
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to Aux[Input, Aux[OutputData, OutputDelta]] performed by method autoToLayer in com.thoughtworks.deeplearning.Symbolic. This conversion will take place only if an implicit value of type Aux[Symbolic[NativeOutput], Input, OutputData, OutputDelta] is in scope.
    Definition Classes
    Layer
  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. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  24. def toLayer: Aux[Input, Aux[OutputData, OutputDelta]]
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to ToLayerOps[Symbolic[NativeOutput], Input, OutputData, OutputDelta] performed by method ToLayerOps in com.thoughtworks.deeplearning.Symbolic. This conversion will take place only if an implicit value of type Aux[Symbolic[NativeOutput], Input, OutputData, OutputDelta] is in scope.
    Definition Classes
    ToLayerOps
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. def toTape: Aux[Data, Delta]
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to ToTapeOps[Symbolic[NativeOutput], Data, Delta] performed by method ToTapeOps in com.thoughtworks.deeplearning.Symbolic. This conversion will take place only if an implicit value of type Aux[Symbolic[NativeOutput], Data, Delta] is in scope.
    Definition Classes
    ToTapeOps
    Annotations
    @inline()
  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 →[B](y: B): (Symbolic[NativeOutput], B)
    Implicit
    This member is added by an implicit conversion from Symbolic[NativeOutput] to ArrowAssoc[Symbolic[NativeOutput]] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion autoToLayer from Symbolic[NativeOutput] to Aux[Input, Aux[OutputData, OutputDelta]]

Inherited by implicit conversion ToTapeOps from Symbolic[NativeOutput] to ToTapeOps[Symbolic[NativeOutput], Data, Delta]

Inherited by implicit conversion ToLayerOps from Symbolic[NativeOutput] to ToLayerOps[Symbolic[NativeOutput], Input, OutputData, OutputDelta]

Inherited by implicit conversion any2stringadd from Symbolic[NativeOutput] to any2stringadd[Symbolic[NativeOutput]]

Inherited by implicit conversion StringFormat from Symbolic[NativeOutput] to StringFormat[Symbolic[NativeOutput]]

Inherited by implicit conversion Ensuring from Symbolic[NativeOutput] to Ensuring[Symbolic[NativeOutput]]

Inherited by implicit conversion ArrowAssoc from Symbolic[NativeOutput] to ArrowAssoc[Symbolic[NativeOutput]]

Ungrouped