DifferentiableDouble

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 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
      }

5. 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)
      }

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) = {
        Poly.MathMethods.-(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. object Layers

   

9. object OptimizerFactory

   

10. object Optimizers

    

    Optimizers of Double.

    Optimizers of Double.

    Example:

    1. implicit val optimizerFactory = new DifferentiableDouble.OptimizerFactory {
         override def doubleOptimizer(weight: Weight): Optimizer = {
           new LearningRate with L2Regularization {
             var learningRate = 0.00003
             override protected def l2Regularization: Double = 0.003
             override protected def currentLearningRate(): Double = {
             learningRate * 0.75
             learningRate
            }
          }
        }
       }

11. 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)
       }

12. final def asInstanceOf[T0]: T0

    

    Definition Classes

    Any

13. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

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

    
    See also

    Trainable

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

    

    Definition Classes

    AnyRef

17. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

18. 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)
       }

19. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    

    Definition Classes

    AnyRef → Any

21. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

22. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

23. 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)
       }

24. 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)
       }

25. 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)
       }

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

    

    Definition Classes

    AnyRef

27. final def notify(): Unit

    

    Definition Classes

    AnyRef

28. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

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

    

    Definition Classes

    AnyRef

30. 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._

31. def toString(): String

    

    Definition Classes

    AnyRef → Any

32. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )