Packages

object Xavier extends InitializationMethod with Product with Serializable

In short, it helps signals reach deep into the network.

During the training process of deep nn:

  1. If the weights in a network start are too small, then the signal shrinks as it passes through each layer until it’s too tiny to be useful.

2. If the weights in a network start too large, then the signal grows as it passes through each layer until it’s too massive to be useful.

Xavier initialization makes sure the weights are ‘just right’, keeping the signal in a reasonable range of values through many layers.

More details on the paper [Understanding the difficulty of training deep feedforward neural networks] (http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)

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Type Members

  1. type Shape = Array[Int]
    Definition Classes
    InitializationMethod

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. 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
    Xavier → InitializationMethod
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  15. def setVarianceNormAverage(v: Boolean): Xavier.this.type
  16. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  17. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  18. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  19. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

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