DifferentiableFloat

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

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

   Returns a Case that accepts two Float Layers.

   Returns a Case that accepts two Float Layers.

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

   Example:

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

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

   Returns a Case that accepts two Float Layers.

   Returns a Case that accepts two Float Layers.

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

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts two Float Layers.

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

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   Returns a Case that accepts two Float Layers.

   Returns a Case that accepts two Float Layers.

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

   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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

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

    Returns a Case that accepts Float Layer for the polymorphic function 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

    Any

15. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape]]

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

    Returns a Case that accepts Float Layer for the polymorphic function 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

    AnyRef

    Annotations

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

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

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

    See also

    Trainable

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

    Definition Classes

    AnyRef → Any

23. def hashCode(): Int

    Definition Classes

    AnyRef → Any

24. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

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

    Returns a Case that accepts Float Layer for the polymorphic function 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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

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

    Returns a Case that accepts two Float Layers for the polymorphic function 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 <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

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

    Returns a Case that accepts two Float Layers for the polymorphic function 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

    AnyRef

29. final def notify(): Unit

    Definition Classes

    AnyRef

30. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

32. implicit def toFloatLayerOps[From, Input <: Tape](from: From)(implicit toLayer: OfPlaceholder[From, Input, FloatPlaceholder]): FloatLayerOps[Input]

    Implicitly converts any layer to FloatLayerOps, which enables common methods for Float layers.

    Implicitly converts any layer to FloatLayerOps, which enables common methods for Float layers.

    Example:

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

33. def toString(): String

    Definition Classes

    AnyRef → Any

34. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )