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)
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Instance Constructors
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new
TimeDistributedMaskCriterion(critrn: TensorCriterion[T], paddingValue: Int = 0)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])
- critrn
embedded criterion
- paddingValue
padding value
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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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
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- AbstractCriterion
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def
canEqual(other: Any): Boolean
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- TimeDistributedMaskCriterion → AbstractCriterion
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def
clone(): AnyRef
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def
cloneCriterion(): AbstractCriterion[Tensor[T], Tensor[T], T]
Deep copy this criterion
- val critrn: TensorCriterion[T]
- val dimension: Int
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(other: Any): Boolean
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def
finalize(): Unit
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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
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final
def
getClass(): Class[_]
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var
gradInput: Tensor[T]
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def
hashCode(): Int
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isInstanceOf[T0]: Boolean
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ne(arg0: AnyRef): Boolean
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notify(): Unit
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def
notifyAll(): Unit
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var
output: T
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- val paddingValue: Int
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var
results: Array[Future[Unit]]
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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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
- TimeDistributedMaskCriterion → AbstractCriterion
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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
- TimeDistributedMaskCriterion → AbstractCriterion
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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