com.thoughtworks.deeplearning

DifferentiableInt

object DifferentiableInt

A namespace of common operators for Int layers.

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Type Members

  1. implicit final class ScalaIntOps extends AnyRef

Value Members

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

    Definition Classes
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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. implicit def Int*Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Poly.MathMethods.*.Case that accepts two Int Layers.

    Returns a Poly.MathMethods.*.Case that accepts two Int Layers.

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

    Example:
    1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods.*(inputIntLayer,anotherIntLayer)
      }
  7. implicit def Int+Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Poly.MathMethods.+.Case that accepts two Int Layers.

    Returns a Poly.MathMethods.+.Case that accepts two Int Layers.

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

    Example:
    1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods.+(inputIntLayer,anotherIntLayer)
      }
  8. implicit def Int-Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Poly.MathMethods.-.Case that accepts two Int Layers.

    Returns a Poly.MathMethods.-.Case that accepts two Int Layers.

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

    Example:
    1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods.-(inputIntLayer,anotherIntLayer)
      }
  9. implicit def Int/Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Poly.MathMethods./.Case that accepts two Int Layers.

    Returns a Poly.MathMethods./.Case that accepts two Int Layers.

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

    Example:
    1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods./(inputIntLayer,anotherIntLayer)
      }
  10. object Layers

  11. val Optimizers: DifferentiableDouble.Optimizers.type

  12. final def asInstanceOf[T0]: T0

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  13. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  14. final def eq(arg0: AnyRef): Boolean

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  15. def equals(arg0: Any): Boolean

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  16. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  17. final def getClass(): Class[_]

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  18. def hashCode(): Int

    Definition Classes
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  19. implicit def intToLiteral: Aux[Int, Int, Float]

  20. implicit def intTrainable: Trainable[Int, Float]

    See also

    Trainable

  21. final def isInstanceOf[T0]: Boolean

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

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  23. final def notify(): Unit

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  24. final def notifyAll(): Unit

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

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  26. def toString(): String

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  27. final def wait(): Unit

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    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  29. final def wait(arg0: Long): Unit

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