DifferentiableDouble

Related Doc: package deeplearning

object DifferentiableDouble

A namespace of common operators for Double layers.

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

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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 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)
}```

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

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13. def clone(): AnyRef

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Trainable

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

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17. def equals(arg0: Any): Boolean

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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

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

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21. def hashCode(): Int

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22. final def isInstanceOf[T0]: Boolean

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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

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27. final def notify(): Unit

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28. final def notifyAll(): Unit

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

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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

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32. final def wait(): Unit

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

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

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