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
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.