DifferentiableFloat

Value Members

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

   Definition Classes

   AnyRef

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

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6. implicit def Float*Float[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   Returns a Poly.MathMethods.*.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods.*

   Returns a Poly.MathMethods.*.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods.*

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableFloat._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputFloatLayer: Float @Symbolic)(anotherFloatLayer: Float @Symbolic) = {
        Poly.MathMethods.*(inputFloatLayer,anotherFloatLayer)
      }

7. implicit def Float+Float[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   Returns a Poly.MathMethods.+.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods.+

   Returns a Poly.MathMethods.+.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods.+

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableFloat._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputFloatLayer: Float @Symbolic)(anotherFloatLayer: Float @Symbolic) = {
        Poly.MathMethods.+(inputFloatLayer,anotherFloatLayer)
      }

8. implicit def Float-Float[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   Returns a Poly.MathMethods.-.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods.-

   Returns a Poly.MathMethods.-.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods.-

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableFloat._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputFloatLayer: Float @Symbolic)(anotherFloatLayer: Float @Symbolic) = {
        Poly.MathMethods.-(inputFloatLayer,anotherFloatLayer)
      }

9. implicit def Float/Float[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

   Returns a Poly.MathMethods./.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods./

   Returns a Poly.MathMethods./.Case that accepts two Float Layers for the polymorphic function Poly.MathMethods./

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableFloat._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputFloatLayer: Float @Symbolic)(anotherFloatLayer: Float @Symbolic) = {
        Poly.MathMethods./(inputFloatLayer,anotherFloatLayer)
      }

10. object Layers

11. object OptimizerFactory

12. object Optimizers

    Optimizers of Float

13. implicit def abs(Float)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch]]

    Returns a Poly.MathFunctions.abs.Case that accepts Float Layer for the polymorphic function Poly.MathFunctions.abs

    Returns a Poly.MathFunctions.abs.Case that accepts Float Layer for the polymorphic function Poly.MathFunctions.abs

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableFloat._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputFloatLayer: Float @Symbolic) = {
         Poly.MathFunctions.abs(inputFloatLayer)
       }

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. final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

17. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

18. implicit def exp(Float)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch]]

    Returns a Poly.MathFunctions.exp.Case that accepts Float Layer for the polymorphic function Poly.MathFunctions.exp

    Returns a Poly.MathFunctions.exp.Case that accepts Float Layer for the polymorphic function Poly.MathFunctions.exp

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableFloat._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputFloatLayer: Float @Symbolic) = {
         Poly.MathFunctions.exp(inputFloatLayer)
       }

19. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

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

20. implicit def floatToLiteral: Aux[Float, Float, Float]

21. implicit def floatTrainable: Trainable[Float, Float]

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(Float)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch]]

    Returns a Poly.MathFunctions.log.Case that accepts Float Layer for the polymorphic function Poly.MathFunctions.log

    Returns a Poly.MathFunctions.log.Case that accepts Float Layer for the polymorphic function Poly.MathFunctions.log

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableFloat._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputFloatLayer: Float @Symbolic) = {
         Poly.MathFunctions.log(inputFloatLayer)
       }

26. implicit def max(Float,Float)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    Returns a Poly.MathFunctions.max.Case that accepts two Float Layers for the polymorphic function Poly.MathFunctions.max

    Returns a Poly.MathFunctions.max.Case that accepts two Float Layers for the polymorphic function Poly.MathFunctions.max

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableFloat._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputFloatLayer: Float @Symbolic)(anotherFloatLayer: Float @Symbolic) = {
         Poly.MathFunctions.max(inputFloatLayer,anotherFloatLayer)
       }

27. implicit def min(Float,Float)[Input <: Batch]: Aux[Aux[Input, Batch], Aux[Input, Batch], Aux[Input, Batch]]

    Returns a Poly.MathFunctions.min.Case that accepts two Float Layers for the polymorphic function Poly.MathFunctions.min

    Returns a Poly.MathFunctions.min.Case that accepts two Float Layers for the polymorphic function Poly.MathFunctions.min

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableFloat._
       import com.thoughtworks.deeplearning.Symbolic
       def myNetwork(implicit inputFloatLayer: Float @Symbolic)(anotherFloatLayer: Float @Symbolic) = {
         Poly.MathFunctions.min(inputFloatLayer,anotherFloatLayer)
       }

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 toFloatLayerOps[From, Input <: Batch](from: From)(implicit toLayer: OfPlaceholder[From, Input, FloatPlaceholder]): FloatLayerOps[Input]

    A helper that contains common boilerplate code for all Float layers.

    A helper that contains common boilerplate code for all Float layers.

    Example:

    1. import com.thoughtworks.deeplearning.DifferentiableFloat._

33. def toString(): String

    Definition Classes

    AnyRef → Any

34. final def wait(): Unit

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    @throws( ... )

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

    Definition Classes

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    @throws( ... )

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

    Definition Classes

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    @throws( ... )