thoughtworks

Value Members

1. 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.

   See also

   Compose