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 DifferentiableInt

    A namespace of common operators for Int layers.

    A namespace of common operators for Int layers.

    Author:

    杨博 (Yang Bo) <[email protected]>

    Definition Classes
    deeplearning
  • object Layers
    Definition Classes
    DifferentiableInt
  • Negative
  • Plus
  • Reciprocal
  • Substract
  • Times
  • Weight

final case class Times[Input0 <: Tape](operand1: Aux[Input0, Tape], operand2: Aux[Input0, Tape]) extends Binary with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, Binary, CumulativeLayer, Layer, AnyRef, Any
Type Hierarchy
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. Times
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Binary
  7. CumulativeLayer
  8. Layer
  9. AnyRef
  10. 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 Times(operand1: Aux[Input0, Tape], operand2: Aux[Input0, Tape])

Type Members

  1. trait BinaryTape extends ReferenceCount
    Attributes
    protected
    Definition Classes
    Binary
  2. trait MonoidTape extends ReferenceCount
    Attributes
    protected
    Definition Classes
    CumulativeLayer
  3. type Output = CumulativeTape.Self

    A cumulative Tape returned by forward.

    A cumulative Tape returned by forward.

    When this Output is backwarding, the delta parameter will not be back-propagated to its upstreams immediately. Instead, the delta parameter will be accumulated internally. Then, when this Output is flushing, the delta accumulator will be processed and back-propagated to its upstreams.

    This Output is reference counted. When the last instance of all this Output's duplicates is closed, flush will be called and all the upstreams will be closed as well.

    Definition Classes
    CumulativeLayer → Layer
  4. trait ReferenceCount extends Tape
    Attributes
    protected
    Definition Classes
    CumulativeLayer
  5. trait SemigroupTape extends ReferenceCount
    Attributes
    protected
    Definition Classes
    CumulativeLayer
  6. type CumulativeTape = IntMonoidTape with MonoidTape with BinaryTape
    Definition Classes
    Times → Binary → CumulativeLayer
  7. type Input = Input0
    Definition Classes
    Times → 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 Times[Input0] to any2stringadd[Times[Input0]] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (Times[Input0], B)
    Implicit
    This member is added by an implicit conversion from Times[Input0] to ArrowAssoc[Times[Input0]] 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: (Times[Input0]) ⇒ Boolean, msg: ⇒ Any): Times[Input0]
    Implicit
    This member is added by an implicit conversion from Times[Input0] to Ensuring[Times[Input0]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  9. def ensuring(cond: (Times[Input0]) ⇒ Boolean): Times[Input0]
    Implicit
    This member is added by an implicit conversion from Times[Input0] to Ensuring[Times[Input0]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  10. def ensuring(cond: Boolean, msg: ⇒ Any): Times[Input0]
    Implicit
    This member is added by an implicit conversion from Times[Input0] to Ensuring[Times[Input0]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  11. def ensuring(cond: Boolean): Times[Input0]
    Implicit
    This member is added by an implicit conversion from Times[Input0] to Ensuring[Times[Input0]] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. def formatted(fmtstr: String): String
    Implicit
    This member is added by an implicit conversion from Times[Input0] to StringFormat[Times[Input0]] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  15. final def forward(input: Input): Output

    Returns the returns the result of rawForward.

    Returns the returns the result of rawForward.

    If this method is called more than once with the same input parameter, during one iteration, the result will be cached and the rawForward will be executed only once.

    Definition Classes
    CumulativeLayer → Layer
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. final def notify(): Unit
    Definition Classes
    AnyRef
  20. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  21. val operand1: Aux[Input0, Tape]
    Definition Classes
    Times → Binary
  22. val operand2: Aux[Input0, Tape]
    Definition Classes
    Times → Binary
  23. def rawForward(input0: Input): CumulativeTape

    Performs the underlying forward pass.

    Performs the underlying forward pass.

    returns

    a Tape that will be cached for subsequent forward

    Attributes
    protected
    Definition Classes
    Times → CumulativeLayer
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. def →[B](y: B): (Times[Input0], B)
    Implicit
    This member is added by an implicit conversion from Times[Input0] to ArrowAssoc[Times[Input0]] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Binary

Inherited from CumulativeLayer

Inherited from Layer

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from Times[Input0] to any2stringadd[Times[Input0]]

Inherited by implicit conversion StringFormat from Times[Input0] to StringFormat[Times[Input0]]

Inherited by implicit conversion Ensuring from Times[Input0] to Ensuring[Times[Input0]]

Inherited by implicit conversion ArrowAssoc from Times[Input0] to ArrowAssoc[Times[Input0]]

Ungrouped