com.intel.analytics.bigdl.nn

KLDCriterion

class KLDCriterion[T] extends AbstractCriterion[Table, Tensor[T], T]

Computes the KL-divergence of the input normal distribution to a standard normal distribution. The input has to be a table. The first element of input is the mean of the distribution, the second element of input is the log_variance of the distribution. The input distribution is assumed to be diagonal.

Linear Supertypes
AbstractCriterion[Table, Tensor[T], T], Serializable, Serializable, AnyRef, Any
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  1. KLDCriterion
  2. AbstractCriterion
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Instance Constructors

  1. new KLDCriterion()(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

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 backward(input: Table, target: Tensor[T]): Table

    Performs a back-propagation step through the criterion, with respect to the given input.

    Performs a back-propagation step through the criterion, with respect to the given input.

    input

    input data

    target

    target

    returns

    gradient corresponding to input data

    Definition Classes
    AbstractCriterion

  8. def canEqual(other: Any): Boolean

    Definition Classes
    AbstractCriterion

  9. def clone(): AnyRef

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

  10. def cloneCriterion(): AbstractCriterion[Table, Tensor[T], T]

    Deep copy this criterion

    Deep copy this criterion

    returns

    a deep copied criterion

    Definition Classes
    AbstractCriterion

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

    Definition Classes
    AnyRef

  12. def equals(other: Any): Boolean

    Definition Classes
    AbstractCriterion → AnyRef → Any

  13. def finalize(): Unit

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

  14. def forward(input: Table, target: Tensor[T]): T

    Takes an input object, and computes the corresponding loss of the criterion, compared with target.

    Takes an input object, and computes the corresponding loss of the criterion, compared with target.

    input

    input data

    target

    target

    returns

    the loss of criterion

    Definition Classes
    AbstractCriterion

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

    Definition Classes
    AnyRef → Any

  16. var gradInput: Table

    Definition Classes
    AbstractCriterion

  17. def hashCode(): Int

    Definition Classes
    AbstractCriterion → AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  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. var output: T

    Definition Classes
    AbstractCriterion

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

    Definition Classes
    AnyRef

  24. def toString(): String

    Definition Classes
    AnyRef → Any

  25. def updateGradInput(input: Table, target: Tensor[T]): Table

    Computing the gradient of the criterion with respect to its own input.

    Computing the gradient of the criterion with respect to its own input. This is returned in gradInput. Also, the gradInput state variable is updated accordingly.

    input

    input data

    target

    target data / labels

    returns

    gradient of input

    Definition Classes
    KLDCriterion → AbstractCriterion

  26. def updateOutput(input: Table, target: Tensor[T]): T

    Computes the loss using input and objective function.

    Computes the loss using input and objective function. This function returns the result which is stored in the output field.

    input

    input of the criterion

    target

    target or labels

    returns

    the loss of the criterion

    Definition Classes
    KLDCriterion → AbstractCriterion

  27. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AbstractCriterion[Table, Tensor[T], T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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