com.intel.analytics.bigdl.nn

MsraFiller

case class MsraFiller(varianceNormAverage: Boolean = true) extends InitializationMethod with Product with Serializable

A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically accounts for ReLU nonlinearities.

Aside: for another perspective on the scaling factor, see the derivation of [Saxe, McClelland, and Ganguli 2013 (v3)].

It fills the incoming matrix by randomly sampling Gaussian data with std = sqrt(2 / n) where n is the fanIn, fanOut, or their average, depending on the varianceNormAverage parameter.

varianceNormAverage

VarianceNorm use average of (fanIn + fanOut) or just fanOut

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Serializable, Serializable, Product, Equals, InitializationMethod, AnyRef, Any
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Instance Constructors

  1. new MsraFiller(varianceNormAverage: Boolean = true)

    varianceNormAverage

    VarianceNorm use average of (fanIn + fanOut) or just fanOut

Type Members

  1. type Shape = Array[Int]

    Definition Classes
    InitializationMethod

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  9. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  11. def init[T](variable: Tensor[T], dataFormat: VariableFormat)(implicit ev: TensorNumeric[T]): Unit

    Initialize the given weight and bias.

    Initialize the given weight and bias.

    variable

    the weight to initialize

    dataFormat

    the data format of weight indicating the dimension order of the weight. "output_first" means output is in the lower dimension "input_first" means input is in the lower dimension.

    Definition Classes
    MsraFiller → InitializationMethod

  12. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  14. final def notify(): Unit

    Definition Classes
    AnyRef

  15. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  17. val varianceNormAverage: Boolean

    VarianceNorm use average of (fanIn + fanOut) or just fanOut

  18. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
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Inherited from InitializationMethod

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