MarginCriterion
Related Docs:
object MarginCriterion
| package nn
if unspecified, is by default 1.
whether to average the loss
whether to calculate the squared hinge loss
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 data
target
gradient corresponding to input data
Deep copy this criterion
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 data
target
the loss of criterion
if unspecified, is by default 1.
whether to average the loss
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 data
target data / labels
gradient of input
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 of the criterion
target or labels
the loss of the criterion
Creates a criterion that optimizes a two-class classification (squared) hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.
When margin = 1, sizeAverage = True and squared = False, this is the same as hinge loss in keras; When margin = 1, sizeAverage = False and squared = True, this is the same as squared_hinge loss in keras.