SoftmaxLayerWithCrossEntropyLoss

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

   Attributes

   protected[java.lang]

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   @throws( ... )

8. def createModel(weights: DenseVector[Double]): LayerModel

   Returns the instance of the layer based on weights provided.

   Returns the instance of the layer based on weights provided. Size of weights must be equal to weightSize

   returns

   the layer model

   Definition Classes

   SoftmaxLayerWithCrossEntropyLoss → Layer

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

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10. def equals(arg0: Any): Boolean

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11. def finalize(): Unit

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    protected[java.lang]

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

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13. def getOutputSize(inputSize: Int): Int

    Returns the output size given the input size (not counting the stack size).

    Returns the output size given the input size (not counting the stack size). Output size is used to allocate memory for the output.

    inputSize

    input size

    returns

    output size

    Definition Classes

    SoftmaxLayerWithCrossEntropyLoss → Layer

14. def hashCode(): Int

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15. val inPlace: Boolean

    If true, the memory is not allocated for the output of this layer.

    If true, the memory is not allocated for the output of this layer. The memory allocated to the previous layer is used to write the output of this layer. Developer can set this to true if computing delta of a previous layer does not involve its output, so the current layer can write there. This also mean that both layers have the same number of outputs.

    Definition Classes

    SoftmaxLayerWithCrossEntropyLoss → Layer

16. def initModel(weights: DenseVector[Double], random: Random): LayerModel

    Returns the instance of the layer with random generated weights.

    Returns the instance of the layer with random generated weights.

    weights

    vector for weights initialization, must be equal to weightSize

    random

    random number generator

    returns

    the layer model

    Definition Classes

    SoftmaxLayerWithCrossEntropyLoss → Layer

17. final def isInstanceOf[T0]: Boolean

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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 weightSize: Int

    Number of weights that is used to allocate memory for the weights vector

    Number of weights that is used to allocate memory for the weights vector

    Definition Classes

    SoftmaxLayerWithCrossEntropyLoss → Layer