Type

com.thoughtworks.deeplearning.plugins.Builtins

Implicits

Related Doc: package Builtins

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abstract type Implicits <: ImplicitsApi

Definition Classes
Builtins → RawINDArrayLayers → INDArrayWeights → INDArrayLiterals → INDArrayTraining → RawDoubleLayers → DoubleLiterals → DoubleTraining → RawFloatLayers → FloatLiterals → FloatTraining → Training → Operators → Logging → Weights → Layers → ImplicitsSingleton
Source
Builtins.scala
Linear Supertypes
Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, DoubleInstances, Builtins.ImplicitsApi, Builtins.ImplicitsApi, Builtins.ImplicitsApi, 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. DoubleInstances
  9. ImplicitsApi
  10. ImplicitsApi
  11. ImplicitsApi
  12. FloatInstances
  13. ImplicitsApi
  14. ToDeepLearningOps
  15. ImplicitsApi
  16. ImplicitsApi
  17. ImplicitsApi
  18. AnyRef
  19. Any
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Type Members

  1. implicit final class DoubleLayerOps[Operand0] extends AnyRef

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  2. implicit final class FloatLayerOps[Operand0] extends AnyRef

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  3. implicit final class INDArrayLayerOps[Operand0] extends AnyRef

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  4. implicit final class PolymorphicOps[Operand0] extends AnyRef

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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 <: RawDoubleLayers.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 <: RawINDArrayLayers.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 <: RawDoubleLayers.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 <: RawINDArrayLayers.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 <: RawDoubleLayers.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 <: RawINDArrayLayers.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 <: RawDoubleLayers.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 <: RawINDArrayLayers.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 final val DoubleAlgebra: DoubleAlgebra

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  13. implicit final val DoubleTag: BuiltinFloatTag[Double]

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  14. implicit def Float*Float[Operand0, Operand1, Out <: RawFloatLayers.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 <: RawFloatLayers.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 <: RawFloatLayers.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. implicit def Float/Float[Operand0, Operand1, Out <: RawFloatLayers.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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  18. implicit final val FloatAlgebra: FloatAlgebra

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  19. implicit final val FloatTag: BuiltinFloatTag[Float]

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  20. implicit def INDArray*Double[Operand0, Operand1, Out <: RawINDArrayLayers.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 <: RawINDArrayLayers.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 <: RawINDArrayLayers.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 <: RawINDArrayLayers.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 <: RawINDArrayLayers.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 <: RawINDArrayLayers.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 INDArray/Double[Operand0, Operand1, Out <: RawINDArrayLayers.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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  27. implicit def INDArray/INDArray[Operand0, Operand1, Out <: RawINDArrayLayers.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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  28. implicit def abs(Double)[Operand0, Out <: RawDoubleLayers.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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  29. implicit def abs(Float)[Operand0, Out <: RawFloatLayers.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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  30. implicit def abs(INDArray)[Operand0, Out <: RawINDArrayLayers.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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  31. final def asInstanceOf[T0]: T0

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  32. def clone(): AnyRef

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

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

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  35. def equals(arg0: Any): Boolean

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  36. implicit def exp(Double)[Operand0, Out <: RawDoubleLayers.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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  37. implicit def exp(Float)[Operand0, Out <: RawFloatLayers.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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  38. implicit def exp(INDArray)[Operand0, Out <: RawINDArrayLayers.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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  39. def finalize(): Unit

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

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

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  42. def hashCode(): Int

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

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  44. implicit final def indArrayMultiplicativeMonoid: MultiplicativeMonoid[INDArray]

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  45. final def isInstanceOf[T0]: Boolean

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  46. implicit def layerDeepLearning[From, Data0, Delta0](implicit asLayer: <:<[From, Layers.Layer.Aux[Data0, Delta0]]): DeepLearning.Aux[From, Data0, Delta0]

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  47. implicit def log(Double)[Operand0, Out <: RawDoubleLayers.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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  48. implicit def log(Float)[Operand0, Out <: RawFloatLayers.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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  49. implicit def log(INDArray)[Operand0, Out <: RawINDArrayLayers.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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  50. implicit def max(Double,Double)[Operand0, Operand1, Out <: RawDoubleLayers.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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  51. implicit def max(Double,INDArray)[Operand0, Operand1, Out <: RawINDArrayLayers.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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  52. implicit def max(Float,Float)[Operand0, Operand1, Out <: RawFloatLayers.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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  53. implicit def max(INDArray,Double)[Operand0, Operand1, Out <: RawINDArrayLayers.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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  54. implicit def max(INDArray,INDArray)[Operand0, Operand1, Out <: RawINDArrayLayers.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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  55. implicit def min(Double,Double)[Operand0, Operand1, Out <: RawDoubleLayers.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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  56. implicit def min(Double,INDArray)[Operand0, Operand1, Out <: RawINDArrayLayers.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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  57. implicit def min(Float,Float)[Operand0, Operand1, Out <: RawFloatLayers.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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  58. implicit def min(INDArray,Double)[Operand0, Operand1, Out <: RawINDArrayLayers.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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  59. implicit def min(INDArray,INDArray)[Operand0, Operand1, Out <: RawINDArrayLayers.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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  60. final def ne(arg0: AnyRef): Boolean

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  61. final def notify(): Unit

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  62. final def notifyAll(): Unit

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

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  64. 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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  65. def toString(): String

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  66. final def wait(): Unit

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

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

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  69. implicit def weightDeepLearning[SubtypeOfWeight, Optimizer0 <: Optimizer, Data0, Delta0, OptimizerConstructor, WeightParameter, WeightRest, OriginalDeltaParameter, OriginalDeltaRest, SubtypeOfOptimizer](implicit asWeight: <:<[SubtypeOfWeight, Weights.Weight.Aux[Optimizer0, Data0, Delta0]], factory: feature.Factory.Aux[Optimizer0, OptimizerConstructor], partialApplyWeight: feature.PartialApply.Aux[OptimizerConstructor, String("weight"), WeightParameter, WeightRest], asWeightParameter: <:<[SubtypeOfWeight, WeightParameter], partialApplyOriginalDelta: feature.PartialApply.Aux[WeightRest, String("originalDelta"), OriginalDeltaParameter, OriginalDeltaRest], asOriginalDeltaParameter: <:<[Delta0, OriginalDeltaParameter], implicitApplyRest: feature.ImplicitApply.Aux[OriginalDeltaRest, SubtypeOfOptimizer], asOptimizer: <:<[SubtypeOfOptimizer, Optimizer0]): DeepLearning.Aux[SubtypeOfWeight, Data0, Delta0]

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Inherited from Builtins.ImplicitsApi

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Inherited from Builtins.ImplicitsApi

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Inherited from DoubleInstances

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Inherited from FloatInstances

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Inherited from ToDeepLearningOps

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