ftrl_updateParam

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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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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. def getBeta(): Float

   

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

    

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11. def getClip_gradient(): Float

    

12. def getGrad(): NDArray

    

13. def getLamda1(): Float

    

14. def getLr(): Float

    

15. def getN(): NDArray

    

16. def getOut(): mxnet.NDArray

    

17. def getRescale_grad(): Float

    

18. def getWd(): Float

    

19. def getWeight(): NDArray

    

20. def getZ(): NDArray

    

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 setBeta(beta: Float): ftrl_updateParam

    
    beta

    Per-Coordinate Learning Rate beta.

27. def setClip_gradient(clip_gradient: Float): ftrl_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).

28. def setLamda1(lamda1: Float): ftrl_updateParam

    
    lamda1

    The L1 regularization coefficient.

29. def setOut(out: NDArray): ftrl_updateParam

    

30. def setRescale_grad(rescale_grad: Float): ftrl_updateParam

    
    rescale_grad

    Rescale gradient to grad = rescale_grad*grad.

31. def setWd(wd: Float): ftrl_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.

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

    

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33. def toString(): String

    

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34. final def wait(): Unit

    

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

    

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

    

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