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

•  object Layers

  Definition Classes

  Symbolic

• Identity
• Literal

com.thoughtworks.deeplearning.Symbolic.Layers

Literal 