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.