DifferentiableINDArray

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

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4. implicit def Double*INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   

   Returns a Poly.MathMethods.*.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods.*

   Returns a Poly.MathMethods.*.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods.*

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods.*(inputINDArrayLayer,anotherDoubleLayer)
      }

5. implicit def Double+INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   

   Returns a Poly.MathMethods.+.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods.+

   Returns a Poly.MathMethods.+.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods.+

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods.+(inputINDArrayLayer,anotherDoubleLayer)
      }

6. implicit def Double-INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   

   Returns a Poly.MathMethods.-.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods.-

   Returns a Poly.MathMethods.-.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods.-

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods.-(inputINDArrayLayer,anotherDoubleLayer)
      }

7. implicit def Double/INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   

   Returns a Poly.MathMethods./.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods./

   Returns a Poly.MathMethods./.Case that accepts a Double Layer and a INDArray Layer for the polymorphic function Poly.MathMethods./

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods./(inputINDArrayLayer,anotherDoubleLayer)
      }

8. implicit def INDArray*Double[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   

   Returns a Poly.MathMethods.*.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods.*

   Returns a Poly.MathMethods.*.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods.*

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods.*(inputINDArrayLayer,anotherDoubleLayer)
      }

9. implicit def INDArray*INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   

   Returns a Poly.MathMethods.*.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods.*

   Returns a Poly.MathMethods.*.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods.*

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherINDArrayLayer: INDArray @Symbolic) = {
        Poly.MathMethods.*(inputINDArrayLayer,anotherINDArrayLayer)
      }

10. implicit def INDArray+Double[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathMethods.+.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods.+

    Returns a Poly.MathMethods.+.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods.+

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
         Poly.MathMethods.+(inputINDArrayLayer,anotherDoubleLayer)
       }

11. implicit def INDArray+INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathMethods.+.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods.+

    Returns a Poly.MathMethods.+.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods.+

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherINDArrayLayer: INDarray @Symbolic) = {
         Poly.MathMethods.+(inputINDArrayLayer,anotherINDArrayLayer)
       }

12. implicit def INDArray-Double[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathMethods.-.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods.-

    Returns a Poly.MathMethods.-.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods.-

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
         Poly.MathMethods.-(inputINDArrayLayer,anotherDoubleLayer)
       }

13. implicit def INDArray-INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathMethods.-.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods.-

    Returns a Poly.MathMethods.-.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods.-

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherINDArrayLayer: INDarray @Symbolic) = {
         Poly.MathMethods.-(inputINDArrayLayer,anotherINDArrayLayer)
       }

14. implicit def INDArray/Double[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathMethods./.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods./

    Returns a Poly.MathMethods./.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathMethods./

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
         Poly.MathMethods./(inputINDArrayLayer,anotherDoubleLayer)
       }

15. implicit def INDArray/INDArray[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathMethods./.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods./

    Returns a Poly.MathMethods./.Case that accepts two INDArray Layers for the polymorphic function Poly.MathMethods./

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherINDArrayLayer: INDArray @Symbolic) = {
         Poly.MathMethods./(inputINDArrayLayer,anotherINDArrayLayer)
       }

16. object Layers

    

17. object OptimizerFactory

    

18. object Optimizers

    

    Optimizers of NDArray

    Optimizers of NDArray

    Example:

    1. implicit val optimizerFactory = new DifferentiableINDArray.OptimizerFactory {
         override def ndArrayOptimizer(weight: Weight): Optimizer = {
           new LearningRate with L2Regularization with Adam {
             var learningRate = 0.00003
             override protected def l2Regularization: Double = 0.00003
             override protected def currentLearningRate(): Double = {
             learningRate * 0.75
             learningRate
            }
          }
        }
       }

19. implicit def abs(INDArray)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathFunctions.abs.Case that accepts INDArray Layers for the polymorphic function Poly.MathFunctions.abs

    Returns a Poly.MathFunctions.abs.Case that accepts INDArray Layers for the polymorphic function Poly.MathFunctions.abs

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray.`abs(INDArray)`
       import com.thoughtworks.deeplearning.Symbolic
       def absNetwork(implicit inputINDArrayLayer: INDArray @Symbolic) = {
         Poly.MathFunctions.abs(indArrayLayer)
       }

    Note

    Importing this method will enable Poly.MathFunctions.abs for INDArray layers or any value able to convert to a INDArray layer

    See also

    Poly.LayerPoly1

20. final def asInstanceOf[T0]: T0

    

    Definition Classes

    Any

21. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

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    Annotations

    @throws( ... )

22. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

23. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

24. implicit def exp(INDArray)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathFunctions.exp.Case that accepts INDArray Layers for the polymorphic function Poly.MathFunctions.exp

    Returns a Poly.MathFunctions.exp.Case that accepts INDArray Layers for the polymorphic function Poly.MathFunctions.exp

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray.`exp(INDArray)`
       import com.thoughtworks.deeplearning.Symbolic
       def expNetwork(implicit inputINDArrayLayer: INDArray @Symbolic) = {
         Poly.MathFunctions.exp(indArrayLayer)
       }

    Note

    Importing this method will enable Poly.MathFunctions.exp for INDArray layers or any value able to convert to a INDArray layer

    See also

    Poly.LayerPoly1

25. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

26. final def getClass(): Class[_]

    

    Definition Classes

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27. def hashCode(): Int

    

    Definition Classes

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28. implicit def indArrayToLiteral: Aux[INDArray, INDArray, INDArray]

    

29. implicit def indArrayTrainable: Trainable[INDArray, INDArray]

    

30. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

31. implicit def log(INDArray)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathFunctions.log.Case that accepts INDArray Layers for the polymorphic function Poly.MathFunctions.log

    Returns a Poly.MathFunctions.log.Case that accepts INDArray Layers for the polymorphic function Poly.MathFunctions.log

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray.`log(INDArray)`
       import com.thoughtworks.deeplearning.Symbolic
       def logNetwork(implicit inputINDArrayLayer: INDArray @Symbolic) = {
         Poly.MathFunctions.log(indArrayLayer)
       }

    Note

    Importing this method will enable Poly.MathFunctions.log for INDArray layers or any value able to convert to a INDArray layer

    See also

    Poly.LayerPoly1

32. implicit def max(INDArray,Double)[Left, Right, Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    

    Returns a Poly.MathFunctions.max.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathFunctions.max

    Returns a Poly.MathFunctions.max.Case that accepts a INDArray Layer and a Double Layer for the polymorphic function Poly.MathFunctions.max

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputINDArrayLayer: INDArray @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
         Poly.MathFunctions.max(inputINDArrayLayer,anotherDoubleLayer)
       }

33. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

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34. final def notify(): Unit

    

    Definition Classes

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35. final def notifyAll(): Unit

    

    Definition Classes

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36. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

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37. implicit def toINDArrayLayerOps[From, Input <: Batch, OutputData, OutputDelta](from: From)(implicit toLayer: Aux[From, Input, OutputData, OutputDelta], constrait: <:<[Aux[Input, Aux[OutputData, OutputDelta]], Aux[Input, Aux[INDArray, INDArray]]]): INDArrayLayerOps[Input]

    

    A helper that contains common boilerplate code for all NDArray layers.

    A helper that contains common boilerplate code for all NDArray layers.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableNDArray._

38. def toString(): String

    

    Definition Classes

    AnyRef → Any

39. implicit def toToINDArrayLayerOps[Element, Input <: Batch](layerVector: Seq[Seq[Element]])(implicit toLayer: OfPlaceholder[Element, Input, DoublePlaceholder]): ToINDArrayLayerOps[Input]

    

40. final def wait(): Unit

    

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    @throws( ... )

41. final def wait(arg0: Long, arg1: Int): Unit

    

    Definition Classes

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    @throws( ... )

42. final def wait(arg0: Long): Unit

    

    Definition Classes

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    @throws( ... )