org.apache.mxnet.javaapi

sample_multinomialParam

Related Doc: package javaapi

class sample_multinomialParam extends AnyRef

This Param Object is specifically used for sample_multinomial

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Instance Constructors

  1. new sample_multinomialParam(data: NDArray)

    data

    Distribution probabilities. Must sum to one on the last axis.

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 getDtype(): String

  12. def getGet_prob(): Boolean

  13. def getOut(): mxnet.NDArray

  14. def getShape(): Shape

  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 setDtype(dtype: String): sample_multinomialParam

    dtype

    DType of the output in case this can't be inferred.

  21. def setGet_prob(get_prob: Boolean): sample_multinomialParam

    get_prob

    Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.

  22. def setOut(out: NDArray): sample_multinomialParam

  23. def setShape(shape: Shape): sample_multinomialParam

    shape

    Shape to be sampled from each random distribution.

  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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