AffineLayerModel

Value Members

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

   Definition Classes

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

   Definition Classes

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3. final def ##(): Int

   Definition Classes

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

   Definition Classes

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

   Definition Classes

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6. final def asInstanceOf[T0]: T0

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7. val b: DenseVector[Double]

8. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

   @throws( ... )

9. def computePrevDelta(delta: DenseMatrix[Double], output: DenseMatrix[Double], prevDelta: DenseMatrix[Double]): Unit

   Computes the delta for back propagation.

   Computes the delta for back propagation. Delta is allocated based on the size provided by the LayerModel implementation and the stack (batch) size. Developer is responsible for checking the size of prevDelta when writing to it.

   delta

   delta of this layer

   output

   output of this layer

   prevDelta

   the previous delta (modified in place)

   Definition Classes

   AffineLayerModel → LayerModel

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

    Definition Classes

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11. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

12. def eval(data: DenseMatrix[Double], output: DenseMatrix[Double]): Unit

    Evaluates the data (process the data through the layer).

    Evaluates the data (process the data through the layer). Output is allocated based on the size provided by the LayerModel implementation and the stack (batch) size. Developer is responsible for checking the size of output when writing to it.

    data

    data

    output

    output (modified in place)

    Definition Classes

    AffineLayerModel → LayerModel

13. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

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

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

    Definition Classes

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15. def grad(delta: DenseMatrix[Double], input: DenseMatrix[Double], cumGrad: DenseVector[Double]): Unit

    Computes the gradient.

    Computes the gradient. cumGrad is a wrapper on the part of the weight vector. Size of cumGrad is based on weightSize provided by implementation of LayerModel.

    delta

    delta for this layer

    input

    input data

    cumGrad

    cumulative gradient (modified in place)

    Definition Classes

    AffineLayerModel → LayerModel

16. def hashCode(): Int

    Definition Classes

    AnyRef → Any

17. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

18. val layer: AffineLayer

    layer properties

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

    Definition Classes

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20. final def notify(): Unit

    Definition Classes

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21. final def notifyAll(): Unit

    Definition Classes

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

    Definition Classes

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23. def toString(): String

    Definition Classes

    AnyRef → Any

24. val w: DenseMatrix[Double]

25. final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )

28. val weights: DenseVector[Double]

    weights

    weights

    Definition Classes

    AffineLayerModel → LayerModel