class SmoothL1CriterionWithWeights[T] extends AbstractCriterion[Tensor[T], Table, T]
a smooth version of the AbsCriterion It uses a squared term if the absolute element-wise error falls below 1. It is less sensitive to outliers than the MSECriterion and in some cases prevents exploding gradients (e.g. see "Fast R-CNN" paper by Ross Girshick).
d = (x - y) * w_in loss(x, y, w_in, w_out) | 0.5 * (sigma * d_i)^2 * w_out if |d_i| < 1 / sigma / sigma
1/n \sum | | (|d_i| - 0.5 / sigma / sigma) * w_out otherwise
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- new SmoothL1CriterionWithWeights(sigma: Double, num: Int = 0)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])
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def
backward(input: Tensor[T], target: Table): 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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- var buffer: Tensor[T]
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canEqual(other: Any): Boolean
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def
cloneCriterion(): AbstractCriterion[Tensor[T], Table, T]
Deep copy this criterion
- var diff: Tensor[T]
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def
forward(input: Tensor[T], target: Table): 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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getClass(): Class[_]
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var
gradInput: Tensor[T]
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- var hasWeights: Boolean
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output: T
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- val sigma: Double
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def
toString(): String
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def
updateGradInput(input: Tensor[T], target: Table): 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
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- SmoothL1CriterionWithWeights → AbstractCriterion
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def
updateOutput(input: Tensor[T], target: Table): 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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- SmoothL1CriterionWithWeights → AbstractCriterion
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