DifferentiableInt

Value Members

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

   

   Definition Classes

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2. final def ##(): Int

   

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

   

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4. implicit def Int*Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   

   Returns a Poly.MathMethods.*.Case that accepts two Int Layers.

   Returns a Poly.MathMethods.*.Case that accepts two Int Layers.

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

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods.*(inputIntLayer,anotherIntLayer)
      }

5. implicit def Int+Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   

   Returns a Poly.MathMethods.+.Case that accepts two Int Layers.

   Returns a Poly.MathMethods.+.Case that accepts two Int Layers.

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

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods.+(inputIntLayer,anotherIntLayer)
      }

6. implicit def Int-Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   

   Returns a Poly.MathMethods.-.Case that accepts two Int Layers.

   Returns a Poly.MathMethods.-.Case that accepts two Int Layers.

   The returned Case is used by the polymorphic function Poly.MathMethods.-, which is called in MathOps.

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods.-(inputIntLayer,anotherIntLayer)
      }

7. implicit def Int/Int[Input <: Tape]: Aux[Aux[Input, Tape], Aux[Input, Tape], Aux[Input, Tape]]

   

   Returns a Poly.MathMethods./.Case that accepts two Int Layers.

   Returns a Poly.MathMethods./.Case that accepts two Int Layers.

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

   Example:

   1. import com.thoughtworks.deeplearning.DifferentiableInt._
      import com.thoughtworks.deeplearning.Symbolic
      def myNetwork(implicit inputIntLayer: Int @Symbolic)(anotherIntLayer: Int @Symbolic) = {
        Poly.MathMethods./(inputIntLayer,anotherIntLayer)
      }

8. object Layers

   

9. val Optimizers: DifferentiableDouble.Optimizers.type

   

10. final def asInstanceOf[T0]: T0

    

    Definition Classes

    Any

11. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

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    @throws( ... )

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

    

    Definition Classes

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13. def equals(arg0: Any): Boolean

    

    Definition Classes

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14. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    Annotations

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

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

    

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16. def hashCode(): Int

    

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17. implicit def intToLiteral: Aux[Int, Int, Float]

    

18. implicit def intTrainable: Trainable[Int, Float]

    
    See also

    Trainable

19. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

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21. final def notify(): Unit

    

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22. final def notifyAll(): Unit

    

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

    

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24. def toString(): String

    

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25. final def wait(): Unit

    

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    @throws( ... )

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

    

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    @throws( ... )

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

    

    Definition Classes

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    @throws( ... )