com.intel.analytics.bigdl.nn

TimeDistributedMaskCriterion

class TimeDistributedMaskCriterion[T] extends TensorCriterion[T]

This class is intended to support inputs with 3 or more dimensions. Apply Any Provided Criterion to every temporal slice of an input. In addition, it supports padding mask.

eg. if the target is [ [-1, 1, 2, 3, -1], [5, 4, 3, -1, -1] ], and set the paddingValue property to -1, then the loss of -1 would not be accumulated and the loss is only divided by 6 (ont including the amount of -1, in this case, we are only interested in 1, 2, 3, 5, 4, 3)

Linear Supertypes
TensorCriterion[T], AbstractCriterion[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
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Inherited
  1. TimeDistributedMaskCriterion
  2. TensorCriterion
  3. AbstractCriterion
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Instance Constructors

  1. new TimeDistributedMaskCriterion(critrn: TensorCriterion[T], paddingValue: Int = 0)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    critrn

    embedded criterion

    paddingValue

    padding value

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: Tensor[T], target: Tensor[T]): Tensor[T]

    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

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def cloneCriterion(): AbstractCriterion[Tensor[T], Tensor[T], T]

    Deep copy this criterion

    Deep copy this criterion

    returns

    a deep copied criterion

    Definition Classes
    AbstractCriterion
  11. val critrn: TensorCriterion[T]

    embedded criterion

  12. val dimension: Int

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

    Definition Classes
    AnyRef
  14. def equals(other: Any): Boolean

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

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def forward(input: Tensor[T], 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
  17. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  18. var gradInput: Tensor[T]

    Definition Classes
    AbstractCriterion
  19. def hashCode(): Int

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

    Definition Classes
    Any
  21. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  24. var output: T

    Definition Classes
    AbstractCriterion
  25. val paddingValue: Int

    padding value

  26. var results: Array[Future[Unit]]

    Attributes
    protected
  27. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  28. def toString(): String

    Definition Classes
    AnyRef → Any
  29. def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T]

    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
    TimeDistributedMaskCriterionAbstractCriterion
  30. def updateOutput(input: Tensor[T], 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
    TimeDistributedMaskCriterionAbstractCriterion
  31. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from TensorCriterion[T]

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

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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