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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   The returned Case is used by the polymorphic function *, which is called in MathOps.

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   The returned Case is used by the polymorphic function +, which is called in MathOps.

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   The returned Case is used by the polymorphic function -, which is called in MathOps.

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   Returns a Case that accepts a Double Layer and a INDArray Layer.

   The returned Case is used by the polymorphic function /, which is called in MathOps.

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    The returned Case is used by the polymorphic function *, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts two INDArray Layers.

    Returns a Case that accepts two INDArray Layers.

    The returned Case is used by the polymorphic function *, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    The returned Case is used by the polymorphic function +, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts two INDArray Layers.

    Returns a Case that accepts two INDArray Layers.

    The returned Case is used by the polymorphic function +, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    The returned Case is used by the polymorphic function -, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts two INDArray Layers.

    Returns a Case that accepts two INDArray Layers.

    The returned Case is used by the polymorphic function -, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    Returns a Case that accepts a INDArray Layer and a Double Layer.

    The returned Case is used by the polymorphic function /, which is called in MathOps.

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts two INDArray Layers.

    Returns a Case that accepts two INDArray Layers.

    The returned Case is used by the polymorphic function /, which is called in MathOps.

    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

    If you write something like this:

20. object Optimizers

    A namespaces contains Optimizers for INDArray.

21. implicit def abs(INDArray)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

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

    Returns a Case that accepts INDArray Layers for the polymorphic function 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 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. def conv2d[Layer, Weight, Bias, Input <: Tape](layer: Layer, weight: Weight, bias: Bias, kernel: (Int, Int), stride: (Int, Int), padding: (Int, Int))(implicit layerToLayer: Aux[Layer, Input, INDArray, INDArray], weightToLayer: Aux[Weight, Input, INDArray, INDArray], biasToLayer: Aux[Bias, Input, INDArray, INDArray]): Aux[Input, Tape]

    Calculates the 2D convolution

    Calculates the 2D convolution

    layer

    4 dimensions INDArray input

    weight

    4 dimensions INDArray weight

    bias

    1 dimension bias

    kernel

    the kernel/filter width and height

    stride

    the stride width and height

    padding

    the padding width and height

    returns

    convolution result

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

    Definition Classes

    AnyRef

26. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

27. implicit def exp(INDArray)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts INDArray Layers.

    Returns a Case that accepts INDArray Layers.

    The returned Case is used by the polymorphic function exp, which is called in MathOps.

    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 exp for INDArray layers or any value able to convert to a INDArray layer

    See also

    Poly.LayerPoly1

28. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    Definition Classes

    AnyRef → Any

30. def hashCode(): Int

    Definition Classes

    AnyRef → Any

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

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

    See also

    Trainable

33. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

34. implicit def log(INDArray)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

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

    Returns a Case that accepts INDArray Layers for the polymorphic function 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 log for INDArray layers or any value able to convert to a INDArray layer

    See also

    Poly.LayerPoly1

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

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

    Returns a Case that accepts a INDArray Layer and a Double Layer for the polymorphic function 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)
       }

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

    Definition Classes

    AnyRef

37. final def notify(): Unit

    Definition Classes

    AnyRef

38. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

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

    Implicitly converts any layer to INDArrayLayerOps, which enables common methods for INDArray layers.

    Implicitly converts any layer to INDArrayLayerOps, which enables common methods for INDArray layers.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableINDArray._

41. def toString(): String

    Definition Classes

    AnyRef → Any

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

43. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )