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, 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. DoubleInstances
  9. DoubleInstances
  10. ImplicitsApi
  11. ImplicitsApi
  12. ImplicitsApi
  13. FloatInstances
  14. FloatInstances
  15. ImplicitsApi
  16. ToDeepLearningOps
  17. ImplicitsApi
  18. ImplicitsApi
  19. ImplicitsApi
  20. AnyRef
  21. 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. val DoubleMinMaxLattice: DistributiveLattice[Double]

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  13. 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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  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. val FloatMinMaxLattice: DistributiveLattice[Float]

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  18. 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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  19. 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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  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 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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  27. 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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  28. 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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  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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  35. implicit val doubleAlgebra: Field[Double]

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

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

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  38. def equals(arg0: Any): Boolean

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  39. 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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  40. 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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  41. 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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  42. def finalize(): Unit

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  43. implicit val floatAlgebra: Field[Float]

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

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

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  46. def hashCode(): Int

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

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  48. implicit final def indArrayMultiplicativeMonoid: MultiplicativeMonoid[INDArray]

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  49. final def isInstanceOf[T0]: Boolean

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

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  51. 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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  52. 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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  53. 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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  54. 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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  55. 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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  56. 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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  57. 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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  58. 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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  59. 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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  60. 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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  61. 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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  62. 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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  63. 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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  64. final def ne(arg0: AnyRef): Boolean

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  65. final def notify(): Unit

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  66. final def notifyAll(): Unit

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

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  68. 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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  69. def toString(): String

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  70. final def wait(): Unit

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

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

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  73. implicit def weightDeepLearning[SubtypeOfWeight, Data0, Delta0, OriginalDeltaRest, SubtypeOfOptimizer](implicit asWeight: <:<[SubtypeOfWeight, Weights.Weight.Aux[OriginalDeltaRest, Data0, Delta0]], implicitApplyRest: feature.ImplicitApply.Aux[OriginalDeltaRest, SubtypeOfOptimizer], asOptimizer: <:<[SubtypeOfOptimizer, Weights.Optimizer.Aux[Delta0]]): DeepLearning.Aux[SubtypeOfWeight, Data0, Delta0]

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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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