DifferentiableDouble

Value Members

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

   Definition Classes

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

   Definition Classes

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3. final def ##(): Int

   Definition Classes

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

   Definition Classes

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

   Definition Classes

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

   Returns a Case that accepts two Double Layers.

   Returns a Case that accepts two Double Layers.

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

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableDouble._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputDoubleLayer: Double @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        inputDoubleLayer * anotherDoubleLayer
      }

7. implicit def Double+Double[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts two Double Layers.

   Returns a Case that accepts two Double Layers.

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

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableDouble._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputDoubleLayer: Double @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods.+(inputDoubleLayer,anotherDoubleLayer)
      }

8. implicit def Double-Double[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts two Double Layers.

   Returns a Case that accepts two Double Layers. The returned Case is used by the polymorphic function -, which is called in MathOps.

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableDouble._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputDoubleLayer: Double @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods.-(inputDoubleLayer,anotherDoubleLayer)
      }

9. implicit def Double/Double[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts two Double Layers.

   Returns a Case that accepts two Double Layers.

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

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableDouble._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputDoubleLayer: Double @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
        Poly.MathMethods./(inputDoubleLayer,anotherDoubleLayer)
      }

10. object Layers

11. object OptimizerFactory

12. object Optimizers

    Optimizers of Double.

13. implicit def abs(Double)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts Double Layer for the polymorphic function abs

    Returns a Case that accepts Double Layer for the polymorphic function abs

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableDouble._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputDoubleLayer: Double @Symbolic) = {
         Poly.MathFunctions.abs(inputDoubleLayer)
       }

14. final def asInstanceOf[T0]: T0

    Definition Classes

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15. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( ... )

16. implicit def doubleToLiteral: Aux[Double, Double, Double]

17. implicit def doubleTrainable: Trainable[Double, Double]

    See also

    Trainable

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

    Definition Classes

    AnyRef

19. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

20. implicit def exp(Double)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts Double Layer for the polymorphic function exp

    Returns a Case that accepts Double Layer for the polymorphic function exp

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableDouble._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputDoubleLayer: Double @Symbolic) = {
         Poly.MathFunctions.exp(inputDoubleLayer)
       }

21. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

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

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

    Definition Classes

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23. def hashCode(): Int

    Definition Classes

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24. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

25. implicit def log(Double)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts Double Layer for the polymorphic function log

    Returns a Case that accepts Double Layer for the polymorphic function log

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableDouble._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputDoubleLayer: Double @Symbolic) = {
         Poly.MathFunctions.log(inputDoubleLayer)
       }

26. implicit def max(Double,Double)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts two Double Layers for the polymorphic function max

    Returns a Case that accepts two Double Layers for the polymorphic function max

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableDouble._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputDoubleLayer: Double @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
         Poly.MathFunctions.max(inputDoubleLayer,anotherDoubleLayer)
       }

27. implicit def min(Double,Double)[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

    Returns a Case that accepts two Double Layers for the polymorphic function min

    Returns a Case that accepts two Double Layers for the polymorphic function min

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableDouble._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputDoubleLayer: Double @Symbolic)(anotherDoubleLayer: Double @Symbolic) = {
         Poly.MathFunctions.min(inputDoubleLayer,anotherDoubleLayer)
       }

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

    Definition Classes

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29. final def notify(): Unit

    Definition Classes

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30. final def notifyAll(): Unit

    Definition Classes

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

    Definition Classes

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32. implicit def toDoubleLayerOps[From, Input <: Tape](from: From)(implicit toLayer: OfPlaceholder[From, Input, DoublePlaceholder]): DoubleLayerOps[Input]

    Implicitly converts any layer to DoubleLayerOps, which enables common methods for Double layers.

    Implicitly converts any layer to DoubleLayerOps, which enables common methods for Double layers.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableDouble._

33. def toString(): String

    Definition Classes

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34. final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )