SigmoidLayerModelWithSquaredError

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

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

   Any

6. final def asInstanceOf[T0]: T0

   Definition Classes

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7. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

   @throws( ... )

8. def computePrevDelta(nextDelta: DenseMatrix[Double], input: DenseMatrix[Double], delta: 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

   Definition Classes

   FunctionalLayerModel → LayerModel

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

   Definition Classes

   AnyRef

10. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

11. 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

    FunctionalLayerModel → LayerModel

12. def finalize(): Unit

    Attributes

    protected[java.lang]

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

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

    Definition Classes

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14. 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

    FunctionalLayerModel → LayerModel

15. def hashCode(): Int

    Definition Classes

    AnyRef → Any

16. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

17. val layer: FunctionalLayer

    Definition Classes

    FunctionalLayerModel

18. def loss(output: DenseMatrix[Double], target: DenseMatrix[Double], delta: DenseMatrix[Double]): Double

    Returns the value of loss function.

    Returns the value of loss function. Computes loss based on target and output. Writes delta (error) to delta in place. Delta is allocated based on the outputSize of model implementation.

    output

    actual output

    target

    target output

    delta

    delta (updated in place)

    returns

    loss

    Definition Classes

    SigmoidLayerModelWithSquaredError → LossFunction

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

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23. def toString(): String

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24. final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

27. val weights: DenseVector[Double]

    Definition Classes

    FunctionalLayerModel → LayerModel