SoftmaxLayerModelWithCrossEntropyLoss

Value Members

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

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

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3. final def ##(): Int

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

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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   protected[java.lang]

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   @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

   SoftmaxLayerModelWithCrossEntropyLoss → LayerModel

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

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10. def equals(arg0: Any): Boolean

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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

    SoftmaxLayerModelWithCrossEntropyLoss → LayerModel

12. def finalize(): Unit

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    protected[java.lang]

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

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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

    SoftmaxLayerModelWithCrossEntropyLoss → LayerModel

15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. 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

    SoftmaxLayerModelWithCrossEntropyLoss → LossFunction

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

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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

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22. def toString(): String

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23. final def wait(): Unit

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

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

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26. val weights: DenseVector[Double]

    Definition Classes

    SoftmaxLayerModelWithCrossEntropyLoss → LayerModel