Type

com.thoughtworks.deeplearning.plugins.Builtins

Implicits

Related Doc: package Builtins

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abstract type Implicits <: ImplicitsApi

Definition Classes
Builtins → Products → HLists → INDArrayLayers → INDArrayWeights → INDArrayLiterals → INDArrayTraining → DoubleLayers → DoubleLiterals → DoubleTraining → FloatLayers → FloatLiterals → FloatTraining → Training → Operators → Names → Logging → Weights → Layers → ImplicitsSingleton
Source
Builtins.scala
Linear Supertypes
Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, DoubleInstances, DoubleInstances, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, FloatInstances, FloatInstances, Builtins.ImplicitsApi, ToDeepLearningOps, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, AnyRef, Any
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  1. Implicits
  2. ImplicitsApi
  3. ImplicitsApi
  4. ImplicitsApi
  5. ImplicitsApi
  6. ImplicitsApi
  7. ImplicitsApi
  8. ImplicitsApi
  9. ImplicitsApi
  10. DoubleInstances
  11. DoubleInstances
  12. ImplicitsApi
  13. ImplicitsApi
  14. ImplicitsApi
  15. FloatInstances
  16. FloatInstances
  17. ImplicitsApi
  18. ToDeepLearningOps
  19. ImplicitsApi
  20. ImplicitsApi
  21. ImplicitsApi
  22. AnyRef
  23. 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 FloatLayerOps[Operand0] extends AnyRef

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

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

    Definition Classes
    ImplicitsApi

  3. 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

  4. implicit final class PolymorphicOps[Operand0] extends AnyRef

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    An implicit wrapper that adds extension methods of common mathematics operations.

    An implicit wrapper that adds extension methods of common mathematics operations.

    Definition Classes
    ImplicitsApi

Value Members

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

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    AnyRef → Any

  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 <: DoubleLayers.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 <: INDArrayLayers.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 <: DoubleLayers.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 <: INDArrayLayers.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 <: DoubleLayers.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 <: INDArrayLayers.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 <: DoubleLayers.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 <: INDArrayLayers.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. val DoubleMinMaxLattice: DistributiveLattice[Double]

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

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

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

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

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  17. val FloatMinMaxLattice: DistributiveLattice[Float]

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  18. implicit def INDArray*Double[Operand0, Operand1, Out <: INDArrayLayers.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 <: INDArrayLayers.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 INDArray+Double[Operand0, Operand1, Out <: INDArrayLayers.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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  21. implicit def INDArray+INDArray[Operand0, Operand1, Out <: INDArrayLayers.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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  22. implicit def INDArray-Double[Operand0, Operand1, Out <: INDArrayLayers.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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  23. implicit def INDArray-INDArray[Operand0, Operand1, Out <: INDArrayLayers.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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  24. implicit def INDArray/Double[Operand0, Operand1, Out <: INDArrayLayers.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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  25. implicit def INDArray/INDArray[Operand0, Operand1, Out <: INDArrayLayers.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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  26. implicit def abs(Double)[Operand0, Out <: DoubleLayers.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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  27. implicit def abs(Float)[Operand0, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.abs.Case[Operand0] { type Result = Out }

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  28. implicit def abs(INDArray)[Operand0, Out <: INDArrayLayers.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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  29. final def asInstanceOf[T0]: T0

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  30. implicit val catsKernelStdGroupForDouble: CommutativeGroup[Double]

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  31. implicit val catsKernelStdGroupForFloat: CommutativeGroup[Float]

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  32. implicit val catsKernelStdOrderForDouble: Order[Double]

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  33. implicit val catsKernelStdOrderForFloat: Order[Float]

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  34. def clone(): AnyRef

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    protected[java.lang]
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  35. implicit val doubleAlgebra: Field[Double]

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

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

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  38. implicit def eagerFloatDsl[Differentiable, Data, Delta, Constructor, Out <: FloatLayers.FloatLayer](implicit implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Dsl[Eager[Differentiable, Data, Delta], FloatLayers.FloatLayer, Data]

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

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

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  41. def equals(arg0: Any): Boolean

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  42. implicit def exp(Double)[Operand0, Out <: DoubleLayers.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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  43. implicit def exp(Float)[Operand0, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.exp.Case[Operand0] { type Result = Out }

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  44. implicit def exp(INDArray)[Operand0, Out <: INDArrayLayers.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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  45. def finalize(): Unit

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  46. implicit val floatAlgebra: Field[Float]

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  47. implicit def floatLiteralDeepLearning: DeepLearning.Aux[Float, Float, Float]

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

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  49. def hashCode(): Int

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  50. implicit def hconsDeepLearning[Head, Tail <: HList, HeadData, TailData <: HList, HeadDelta, TailDelta <: HList](implicit headDeepLearning: DeepLearning.Aux[Head, HeadData, HeadDelta], tailDeepLearning: DeepLearning.Aux[Tail, TailData, TailDelta]): DeepLearning.Aux[::[Head, Tail], ::[HeadData, TailData], ::[HeadDelta, TailDelta]]

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  51. implicit def hnilDeepLearning[L <: HNil]: DeepLearning.Aux[L, HNil, HNil]

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

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    @inline()

  53. implicit def indArrayLiteralDeepLearning: DeepLearning.Aux[INDArray, INDArray, INDArray]

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  54. implicit final def indArrayMultiplicativeMonoid: MultiplicativeMonoid[INDArray]

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  55. final def isInstanceOf[T0]: Boolean

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

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  57. implicit def log(Double)[Operand0, Out <: DoubleLayers.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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  58. implicit def log(Float)[Operand0, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.log.Case[Operand0] { type Result = Out }

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  59. implicit def log(INDArray)[Operand0, Out <: INDArrayLayers.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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  60. implicit def max(Double,Double)[Operand0, Operand1, Out <: DoubleLayers.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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  61. implicit def max(Double,INDArray)[Operand0, Operand1, Out <: INDArrayLayers.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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  62. implicit def max(Float,Float)[Operand0, Operand1, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.max.Case[Operand0, Operand1] { type Result = Out }

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  63. implicit def max(INDArray,Double)[Operand0, Operand1, Out <: INDArrayLayers.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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  64. implicit def max(INDArray,INDArray)[Operand0, Operand1, Out <: INDArrayLayers.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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  65. implicit def min(Double,Double)[Operand0, Operand1, Out <: DoubleLayers.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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  66. implicit def min(Double,INDArray)[Operand0, Operand1, Out <: INDArrayLayers.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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  67. implicit def min(Float,Float)[Operand0, Operand1, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.min.Case[Operand0, Operand1] { type Result = Out }

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  68. implicit def min(INDArray,Double)[Operand0, Operand1, Out <: INDArrayLayers.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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  69. implicit def min(INDArray,INDArray)[Operand0, Operand1, Out <: INDArrayLayers.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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  70. final def ne(arg0: AnyRef): Boolean

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  71. final def notify(): Unit

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  72. final def notifyAll(): Unit

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  73. implicit def pow(Double,Double)[Operand0, Operand1, Out <: DoubleLayers.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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  74. implicit def pow(Float,Float)[Operand0, Operand1, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], deepLearning1: DeepLearning.Aux[Operand1, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.pow.Case[Operand0, Operand1] { type Result = Out }

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  75. implicit def pow(INDArray,Double)[Operand0, Operand1, Out <: INDArrayLayers.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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  76. implicit def productDeepLearning[Operand, L, DataHList <: HList, DeltaHList <: HList, DataTuple, DeltaTuple](implicit generic: shapeless.Generic.Aux[Operand, L], deepLearning: DeepLearning.Aux[L, DataHList, DeltaHList], dataTupler: shapeless.ops.hlist.Tupler.Aux[DataHList, DataTuple], deltaTupler: shapeless.ops.hlist.Tupler.Aux[DeltaHList, DeltaTuple], genericDelta: shapeless.Generic.Aux[DeltaTuple, DeltaHList]): DeepLearning.Aux[Operand, DataTuple, DeltaTuple]

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  77. implicit def sqrt(Double)[Operand0, Out <: DoubleLayers.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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  78. implicit def sqrt(Float)[Operand0, Out <: FloatLayers.FloatLayer](implicit deepLearning0: DeepLearning.Aux[Operand0, Float, Float], implicitApply: feature.ImplicitApply.Aux[Rest, Out]): Operators.sqrt.Case[Operand0] { type Result = Out }

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  79. implicit def sqrt(INDArray)[Operand0, Out <: INDArrayLayers.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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  80. final def synchronized[T0](arg0: ⇒ T0): T0

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  81. implicit def toDeepLearningOps[Differentiable](target: Differentiable)(implicit tc: DeepLearning[Differentiable]): Ops[Differentiable] { type TypeClassType = com.thoughtworks.deeplearning.DeepLearning[Differentiable]{type Data = tc.Data; type Delta = tc.Delta} }

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    @SuppressWarnings()

  82. def toString(): String

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  83. final def wait(): Unit

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

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    @throws( ... )

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

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    @throws( ... )

  86. implicit def weightDeepLearning[SubtypeOfWeight, Data0, Delta0, OriginalDeltaRest, SubtypeOfOptimizer](implicit asWeight: <:<[SubtypeOfWeight, Weights.WeightApi { ... /* 3 definitions in type refinement */ }], implicitApplyRest: feature.ImplicitApply.Aux[OriginalDeltaRest, SubtypeOfOptimizer], asOptimizer: <:<[SubtypeOfOptimizer, OptimizerApi { type Delta = Delta0 }]): DeepLearning.Aux[SubtypeOfWeight, Data0, Delta0]

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    ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from DoubleInstances

Inherited from DoubleInstances

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from FloatInstances

Inherited from FloatInstances

Inherited from Builtins.ImplicitsApi

Inherited from ToDeepLearningOps

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from Builtins.ImplicitsApi

Inherited from AnyRef

Inherited from Any

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