Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  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)
      }

5.  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)
      }

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 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)
      }

9.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

10.  def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

11.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

12.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

13.  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)
       }

14.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

15.  implicit def floatToLiteral: Aux[Float, Float, Float]
16.  implicit def floatTrainable: Trainable[Float, Float]
17.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

18.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

19.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

20.  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)
       }

21.  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)
       }

22.  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)
       }

23.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

24.  final def notify(): Unit

    Definition Classes

    AnyRef

25.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

27.  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._

28.  def toString(): String

    Definition Classes

    AnyRef → Any

29.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

31.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

32.  object Layers
33.  object OptimizerFactory
34.  object Optimizers

    Optimizers of Float