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.
The mean and log_variance are both assumed to be two dimensional tensors. The first dimension are interpreted as batch. The output is the average/sum of each observation.
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Instance Constructors
- new KLDCriterion(sizeAverage: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])
Value Members
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final
def
!=(arg0: Any): Boolean
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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
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def
canEqual(other: Any): Boolean
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def
clone(): AnyRef
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def
cloneCriterion(): AbstractCriterion[Table, Tensor[T], T]
Deep copy this criterion
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equals(other: Any): Boolean
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finalize(): Unit
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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
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final
def
getClass(): Class[_]
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var
gradInput: Table
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def
hashCode(): Int
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isInstanceOf[T0]: Boolean
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notifyAll(): Unit
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output: T
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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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
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- KLDCriterion → AbstractCriterion
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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
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- KLDCriterion → AbstractCriterion
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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