BCECriterion
Related Docs:
object BCECriterion
| package nn
weights over the input dimension
avgerage or not in each mini-batch
numeric operator
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
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
weights over the input dimension
This loss function measures the Binary Cross Entropy between the target and the output loss(o, t) = - 1/n sum_i (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i])) or in the case of the weights argument being specified: loss(o, t) = - 1/n sum_i weights[i] * (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i]))
By default, the losses are averaged for each mini-batch over observations as well as over dimensions. However, if the field sizeAverage is set to false, the losses are instead summed.
numeric type