DifferentiableINDArray

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

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 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)
      }

9. 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)
      }

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. 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)
       }

17. 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)
       }

18. object Layers

19. object OptimizerFactory

20. object Optimizers

    Optimizers of NDArray

21. 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

22. final def asInstanceOf[T0]: T0

    Definition Classes

    Any

23. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

25. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

26. 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

27. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    Definition Classes

    AnyRef → Any

29. def hashCode(): Int

    Definition Classes

    AnyRef → Any

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

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

32. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

33. 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

34. 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)
       }

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

    Definition Classes

    AnyRef

36. final def notify(): Unit

    Definition Classes

    AnyRef

37. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

39. 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._

40. def toString(): String

    Definition Classes

    AnyRef → Any

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

42. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )