org.apache.mxnet.javaapi

signum_updateParam

Related Doc: package javaapi

class signum_updateParam extends AnyRef

This Param Object is specifically used for signum_update

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Instance Constructors

  1. new signum_updateParam(weight: NDArray, grad: NDArray, mom: NDArray, lr: Float)

    weight

    Weight

    grad

    Gradient

    mom

    Momentum

    lr

    Learning rate

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  10. def getClip_gradient(): Float

  11. def getGrad(): NDArray

  12. def getLr(): Float

  13. def getMom(): NDArray

  14. def getMomentum(): Float

  15. def getOut(): mxnet.NDArray

  16. def getRescale_grad(): Float

  17. def getWd(): Float

  18. def getWd_lh(): Float

  19. def getWeight(): NDArray

  20. def hashCode(): Int

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  25. def setClip_gradient(clip_gradient: Float): signum_updateParam

    clip_gradient

    Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).

  26. def setMomentum(momentum: Float): signum_updateParam

    momentum

    The decay rate of momentum estimates at each epoch.

  27. def setOut(out: NDArray): signum_updateParam

  28. def setRescale_grad(rescale_grad: Float): signum_updateParam

    rescale_grad

    Rescale gradient to grad = rescale_grad*grad.

  29. def setWd(wd: Float): signum_updateParam

    wd

    Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.

  30. def setWd_lh(wd_lh: Float): signum_updateParam

    wd_lh

    The amount of weight decay that does not go into gradient/momentum calculationsotherwise do weight decay algorithmically only.

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

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