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.
l2_norm(x)	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.