SoftmaxOutputParam

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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 getIgnore_label(): Float

    

13. def getLabel(): NDArray

    

14. def getMulti_output(): Boolean

    

15. def getNormalization(): String

    

16. def getOut(): mxnet.NDArray

    

17. def getOut_grad(): Boolean

    

18. def getPreserve_shape(): Boolean

    

19. def getSmooth_alpha(): Float

    

20. def getUse_ignore(): Boolean

    

21. def hashCode(): Int

    

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22. final def isInstanceOf[T0]: Boolean

    

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

    

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24. final def notify(): Unit

    

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25. final def notifyAll(): Unit

    

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26. def setGrad_scale(grad_scale: Float): SoftmaxOutputParam

    
    grad_scale

    Scales the gradient by a float factor.

27. def setIgnore_label(ignore_label: Float): SoftmaxOutputParam

    
    ignore_label

    The instances whose labels == ignore_label will be ignored during backward, if use_ignore is set to true).

28. def setMulti_output(multi_output: Boolean): SoftmaxOutputParam

    
    multi_output

    If set to true, the softmax function will be computed along axis 1. This is applied when the shape of input array differs from the shape of label array.

29. def setNormalization(normalization: String): SoftmaxOutputParam

    
    normalization

    Normalizes the gradient.

30. def setOut(out: NDArray): SoftmaxOutputParam

    

31. def setOut_grad(out_grad: Boolean): SoftmaxOutputParam

    
    out_grad

    Multiplies gradient with output gradient element-wise.

32. def setPreserve_shape(preserve_shape: Boolean): SoftmaxOutputParam

    
    preserve_shape

    If set to true, the softmax function will be computed along the last axis (-1).

33. def setSmooth_alpha(smooth_alpha: Float): SoftmaxOutputParam

    
    smooth_alpha

    Constant for computing a label smoothed version of cross-entropyfor the backwards pass. This constant gets subtracted from theone-hot encoding of the gold label and distributed uniformly toall other labels.

34. def setUse_ignore(use_ignore: Boolean): SoftmaxOutputParam

    
    use_ignore

    If set to true, the ignore_label value will not contribute to the backward gradient.

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

    

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36. def toString(): String

    

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37. final def wait(): Unit

    

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

    

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

    

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