com.intel.analytics.bigdl.optim

L1L2Regularizer

class L1L2Regularizer[T] extends Regularizer[T]

Apply both L1 and L2 regularization

T

type parameters Float or Double

Annotations
@SerialVersionUID( 5617491971070914067L )
Linear Supertypes
Regularizer[T], Serializable, Serializable, AnyRef, Any
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  1. L1L2Regularizer
  2. Regularizer
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new L1L2Regularizer(l1: Double, l2: Double)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    l1

    l1 regularization rate

    l2

    l2 regularization rate

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. def accRegularization(parameter: Tensor[T], gradParameter: Tensor[T], scale: Double): Unit

    The method need to be override by the concrete regularizer class It accumulates the gradient of the regularization of parameter to gradParameter

    The method need to be override by the concrete regularizer class It accumulates the gradient of the regularization of parameter to gradParameter

    parameter

    the parameter that is regularized

    gradParameter

    the gradient of the parameter

    scale

    the scale of gradParameters

    Definition Classes
    L1L2RegularizerRegularizer
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def disable(): Unit

    Disable the regularization feature

    Disable the regularization feature

    Definition Classes
    Regularizer
  10. def enable(): Unit

    Enable the regularization feature

    Enable the regularization feature

    Definition Classes
    Regularizer
  11. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  17. val l1: Double

    l1 regularization rate

  18. val l2: Double

    l2 regularization rate

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

    Definition Classes
    AnyRef
  20. final def notify(): Unit

    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  22. def preCheck(parameter: Tensor[T], gradParameter: Tensor[T]): Boolean

    Check the regularization is applied or not

    Check the regularization is applied or not

    parameter

    the parameter that is regularized

    gradParameter

    the gradient of the parameter

    returns

    a boolean, if true, accumulates the gradient of regularization, otherwise not.

    Attributes
    protected
    Definition Classes
    Regularizer
  23. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  24. def toString(): String

    Definition Classes
    AnyRef → Any
  25. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Regularizer[T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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