Type

com.thoughtworks.deeplearning.plugins.INDArrayLayers

Implicits

Related Doc: package INDArrayLayers

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abstract type Implicits <: ImplicitsApi

Definition Classes
INDArrayLayers → ImplicitsSingleton → DoubleLiterals → DoubleLayers → Layers
Source
INDArrayLayers.scala
Linear Supertypes
INDArrayLayers.ImplicitsApi, INDArrayLayers.ImplicitsApi, INDArrayLayers.ImplicitsApi, AnyRef, Any
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Type Members

  1. implicit final class DoubleLayerOps[Operand0] extends AnyRef

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    An implicit wrapper that adds extension methods for differentiable double types that support the DeepLearning type class.

    An implicit wrapper that adds extension methods for differentiable double types that support the DeepLearning type class.

    Definition Classes
    ImplicitsApi

  2. implicit final class INDArrayLayerOps[Operand0] extends AnyRef

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    An implicit wrapper that adds extension methods for differentiable n-dimensional array types that support the DeepLearning type class.

    An implicit wrapper that adds extension methods for differentiable n-dimensional array types that support the DeepLearning type class.

    Definition Classes
    ImplicitsApi

Value Members

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

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  2. final def ##(): Int

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

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  4. implicit def Double*Double[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.*.Case[Operand0, Operand1] { type Result = Out }

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  5. implicit def Double*INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.*.Case[Operand0, Operand1] { type Result = Out }

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  6. implicit def Double+Double[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

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  7. implicit def Double+INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

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  8. implicit def Double-Double[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

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  9. implicit def Double-INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

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  10. implicit def Double/Double[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

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  11. implicit def Double/INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

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  12. implicit def INDArray*Double[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.*.Case[Operand0, Operand1] { type Result = Out }

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  13. implicit def INDArray*INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.*.Case[Operand0, Operand1] { type Result = Out }

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  14. implicit def INDArray+Double[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

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  15. implicit def INDArray+INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.+.Case[Operand0, Operand1] { type Result = Out }

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  16. implicit def INDArray-Double[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

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  17. implicit def INDArray-INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.-.Case[Operand0, Operand1] { type Result = Out }

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  18. implicit def INDArray/Double[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

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  19. implicit def INDArray/INDArray[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators./.Case[Operand0, Operand1] { type Result = Out }

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  20. implicit def abs(Double)[Operand0, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.abs.Case[Operand0] { type Result = Out }

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  21. implicit def abs(INDArray)[Operand0, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.abs.Case[Operand0] { type Result = Out }

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  22. final def asInstanceOf[T0]: T0

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  23. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  24. implicit def doubleLiteralDeepLearning: DeepLearning.Aux[Double, Double, Double]

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  25. implicit def eagerDoubleDsl[Differentiable, Data, Delta, Constructor, Out <: DoubleLayer](implicit implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Dsl[Eager[Differentiable, Data, Delta], DoubleLayer, Data]

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  26. implicit def eagerINDArrayDsl[Differentiable, Data, Delta, Constructor, Out <: INDArrayLayer](implicit implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Dsl[Eager[Differentiable, Data, Delta], INDArrayLayer, Data]

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

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  28. def equals(arg0: Any): Boolean

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  29. implicit def exp(Double)[Operand0, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.exp.Case[Operand0] { type Result = Out }

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  30. implicit def exp(INDArray)[Operand0, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.exp.Case[Operand0] { type Result = Out }

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  31. def finalize(): Unit

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

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

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  33. def hashCode(): Int

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  34. implicit def implicitEager[Operand0](a: Operand0)(implicit deepLearning: DeepLearning[Operand0]): Eager[Operand0, Data, Delta]

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    @inline()

  35. final def isInstanceOf[T0]: Boolean

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  36. implicit def layerDeepLearning[From, Data0, Delta0](implicit asLayer: <:<[From, LayerApi { ... /* 2 definitions in type refinement */ }]): DeepLearning.Aux[From, Data0, Delta0]

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  37. implicit def log(Double)[Operand0, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.log.Case[Operand0] { type Result = Out }

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  38. implicit def log(INDArray)[Operand0, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.log.Case[Operand0] { type Result = Out }

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  39. implicit def max(Double,Double)[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.max.Case[Operand0, Operand1] { type Result = Out }

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  40. implicit def max(Double,INDArray)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.max.Case[Operand0, Operand1] { type Result = Out }

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  41. implicit def max(INDArray,Double)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.max.Case[Operand0, Operand1] { type Result = Out }

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  42. implicit def max(INDArray,INDArray)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.max.Case[Operand0, Operand1] { type Result = Out }

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  43. implicit def min(Double,Double)[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.min.Case[Operand0, Operand1] { type Result = Out }

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  44. implicit def min(Double,INDArray)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.min.Case[Operand0, Operand1] { type Result = Out }

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  45. implicit def min(INDArray,Double)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.min.Case[Operand0, Operand1] { type Result = Out }

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  46. implicit def min(INDArray,INDArray)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.min.Case[Operand0, Operand1] { type Result = Out }

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

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  48. final def notify(): Unit

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  49. final def notifyAll(): Unit

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  50. implicit def pow(Double,Double)[Operand0, Operand1, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.pow.Case[Operand0, Operand1] { type Result = Out }

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  51. implicit def pow(INDArray,Double)[Operand0, Operand1, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], deepLearning1: DeepLearning.Aux[Operand1, Double, Double], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.pow.Case[Operand0, Operand1] { type Result = Out }

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  52. implicit def sqrt(Double)[Operand0, Out <: DoubleLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Double, Double], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.sqrt.Case[Operand0] { type Result = Out }

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  53. implicit def sqrt(INDArray)[Operand0, Out <: INDArrayLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, INDArray, INDArray], layerImplicits: feature.ImplicitApply.Aux[Rest, Out]): Operators.sqrt.Case[Operand0] { type Result = Out }

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

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  55. def toString(): String

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  56. final def wait(): Unit

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    @throws( ... )

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

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

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Inherited from INDArrayLayers.ImplicitsApi

Inherited from INDArrayLayers.ImplicitsApi

Inherited from INDArrayLayers.ImplicitsApi

Inherited from AnyRef

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