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 DifferentiableFloat

  A namespace of common operators for Float layers.

  A namespace of common operators for Float layers.

  Author:

  杨博 (Yang Bo) <[email protected]>

  Definition Classes

  deeplearning

•  object Optimizers

  Optimizers of Float.

  Optimizers of Float.

  Definition Classes

  DifferentiableFloat

  Example:

  1. implicit val optimizerFactory = new DifferentiableFloat.OptimizerFactory {
       override def floatOptimizer(weight: Weight): Optimizer = {
         new LearningRate with L2Regularization {
     
           var learningRate = 0.00003
     
           override protected def l2Regularization: Float = 0.003
     
           override protected def currentLearningRate(): Float = {
           learningRate * 0.75
           learningRate
          }
        }
      }
     }

• L1Regularization
• L2Regularization
• LearningRate
• Optimizer

com.thoughtworks.deeplearning.DifferentiableFloat.Optimizers

L2Regularization 