# brainpy.losses module#

## Comparison#

 cross_entropy_loss(predicts, targets[, ...]) This criterion combines LogSoftmax and NLLLoss in one single class. cross_entropy_sparse(predicts, targets) Computes the softmax cross-entropy loss. cross_entropy_sigmoid(predicts, targets) Computes the sigmoid cross-entropy loss. l1_loos(logits, targets[, reduction]) Creates a criterion that measures the mean absolute error (MAE) between each element in the logits $$x$$ and targets $$y$$. l2_loss(predicts, targets) Computes the L2 loss. huber_loss(predicts, targets[, delta]) Huber loss. mean_absolute_error(x, y[, axis, reduction]) Computes the mean absolute error between x and y. mean_squared_error(predicts, targets[, ...]) Computes the mean squared error between x and y. mean_squared_log_error(predicts, targets[, ...]) Computes the mean squared logarithmic error between y_true and y_pred. binary_logistic_loss(predicts, targets) Binary logistic loss. multiclass_logistic_loss(label, logits) Multiclass logistic loss. sigmoid_binary_cross_entropy(logits, labels) Computes sigmoid cross entropy given logits and multiple class labels. softmax_cross_entropy(logits, labels) Computes the softmax cross entropy between sets of logits and labels. log_cosh_loss(predicts, targets) Calculates the log-cosh loss for a set of predictions. ctc_loss_with_forward_probs(logits, ...[, ...]) Computes CTC loss and CTC forward-probabilities. ctc_loss(logits, logit_paddings, labels, ...) Computes CTC loss.

## Regularization#

 l2_norm(x[, axis]) Computes the L2 loss. mean_absolute(outputs[, axis]) Computes the mean absolute error between x and y. mean_square(predicts[, axis]) log_cosh(errors) Calculates the log-cosh loss for a set of predictions. smooth_labels`(labels, alpha) Apply label smoothing.