com.intel.analytics.bigdl.nn

Utils

object Utils

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  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def calculateFwdBwdTime[T](times: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)])(implicit arg0: ClassTag[T]): (Long, Long)

    Calculate forward time and backward time.

    Calculate forward time and backward time.

    T
    times
    returns

  8. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  9. def copyModule[T](src: Module[T], dst: Module[T]): Module[T]

    copy src's parameters and running status to dst

    copy src's parameters and running status to dst

    src

    source model

    dst

    destination model

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

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  14. def getNamedModules[T](model: Module[T]): Map[String, Module[T]]

    get all modules and map by name

    get all modules and map by name

    T
    model
    returns

  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def isLayerwiseScaled[T](model: Module[T]): Boolean

    get whether the module is layerwise scaled

    get whether the module is layerwise scaled

    model

    input module

    returns

    whether the module is layerwise scaled

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def recursiveAdd[T](y: Activity, alpha: Double = 1.0, x: Activity)(implicit ev: TensorNumeric[T]): Activity

    Apply a add operation on table x and table y one by one.

    Apply a add operation on table x and table y one by one. y := y + alpha * x

    Table x should have the same size with y.

    y
    alpha
    x
    returns

    y

  22. def recursiveCopy[T](y: Activity, x: Activity)(implicit ev: TensorNumeric[T]): Activity

    copy table x's tensor to table y.

    copy table x's tensor to table y.

    Table x should have the same size with y.

    y
    x
    returns

    y

  23. def recursiveFill[T](x: Activity, value: Double)(implicit ev: TensorNumeric[T]): Unit

    Fill the value to each Tensor in the table recursively

    Fill the value to each Tensor in the table recursively

    x
    value

  24. def recursiveResizeAs[T](target: Activity, src: Activity)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Activity

    Resize table target as table src.

    Resize table target as table src.

    target
    src

  25. def recursiveTensorApply1[T](x: Activity, func: (Tensor[T]) ⇒ Tensor[T])(implicit ev: TensorNumeric[T]): Unit

    Apply function 'func' on all tensor in the table.

    Apply function 'func' on all tensor in the table.

    x
    func

  26. def recursiveTensorApply2[T](x: Activity, y: Activity, func: (Tensor[T], Tensor[T]) ⇒ Tensor[T])(implicit ev: TensorNumeric[T]): Activity

    Apply function 'func' on each tensor in table x and table y recursively.

    Apply function 'func' on each tensor in table x and table y recursively.

    Table x should have the same size with table y.

    x
    y
    func
    returns

  27. def shuffle[T](src: Tensor[T], permutation: Array[Int], buffer: Tensor[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

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

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  29. def toString(): String

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  30. final def wait(): Unit

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    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit

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

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  33. def zeroTableCopy[T](t1: Table, t2: Table)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Table

    This method recursively keep the shape of the source table t2 and set the elements of each tensor to zero, saving the result on the destination table t1 Notice that t1 and t2 can only contain tables or tensors

    This method recursively keep the shape of the source table t2 and set the elements of each tensor to zero, saving the result on the destination table t1 Notice that t1 and t2 can only contain tables or tensors

    t1

    is the destination table

    t2

    is the source table

    returns

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