org.apache.mxnet.javaapi

MakeLossParam

Related Doc: package javaapi

class MakeLossParam extends AnyRef

This Param Object is specifically used for MakeLoss

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Instance Constructors

  1. new MakeLossParam(data: NDArray)

    data

    Input array.

Value Members

  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. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def getData(): NDArray

  11. def getGrad_scale(): Float

  12. def getNormalization(): String

  13. def getOut(): mxnet.NDArray

  14. def getValid_thresh(): Float

  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def setGrad_scale(grad_scale: Float): MakeLossParam

    grad_scale

    Gradient scale as a supplement to unary and binary operators

  21. def setNormalization(normalization: String): MakeLossParam

    normalization

    If this is set to null, the output gradient will not be normalized. If this is set to batch, the output gradient will be divided by the batch size. If this is set to valid, the output gradient will be divided by the number of valid input elements.

  22. def setOut(out: NDArray): MakeLossParam

  23. def setValid_thresh(valid_thresh: Float): MakeLossParam

    valid_thresh

    clip each element in the array to 0 when it is less than valid_thresh. This is used when normalization is set to 'valid'.

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

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  25. def toString(): String

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  26. final def wait(): Unit

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

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

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