# brainpy.losses module#

This module implements several loss functions.

 cross_entropy_loss(logits, targets[, ...]) This criterion combines LogSoftmax and NLLLoss in one single class. 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. Computes the L2 loss. huber_loss(predicts, targets[, delta]) Huber loss. mean_absolute_error(x, y[, axis]) Computes the mean absolute error between x and y. mean_squared_error(predicts, targets[, axis]) Computes the mean squared error between x and y. mean_squared_log_error`(y_true, y_pred[, axis]) Computes the mean squared logarithmic error between y_true and y_pred.