brainpy.losses module

Comparison

cross_entropy_loss	This criterion combines LogSoftmax and NLLLoss` in one single class.
cross_entropy_sparse	Computes the softmax cross-entropy loss.
cross_entropy_sigmoid	Computes the sigmoid cross-entropy loss.
nll_loss	The negative log likelihood loss.
l1_loss	Creates a criterion that measures the mean absolute error (MAE) between each element in the logits \(x\) and targets \(y\).
l2_loss	Computes the L2 loss.
huber_loss	Huber loss.
mean_absolute_error	Computes the mean absolute error between x and y.
mean_squared_error	Computes the mean squared error between x and y.
mean_squared_log_error	Computes the mean squared logarithmic error between y_true and y_pred.
binary_logistic_loss	Binary logistic loss.
multiclass_logistic_loss	Multiclass logistic loss.
sigmoid_binary_cross_entropy	Computes sigmoid cross entropy given logits and multiple class labels.
softmax_cross_entropy	Computes the softmax cross entropy between sets of logits and labels.
log_cosh_loss	Calculates the log-cosh loss for a set of predictions.
ctc_loss_with_forward_probs	Computes CTC loss and CTC forward-probabilities.
ctc_loss	Computes CTC loss.
multi_margin_loss	Computes multi-class margin loss, also called multi-class hinge loss.

CrossEntropyLoss	This criterion computes the cross entropy loss between input logits and target.
NLLLoss	The negative log likelihood loss.
L1Loss	Creates a criterion that measures the mean absolute error (MAE) between each element in the input \(x\) and target \(y\).
MAELoss
MSELoss	Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input \(x\) and target \(y\).

Regularization

l2_norm	Computes the L2 loss.
mean_absolute	Computes the mean absolute error between x and y.
mean_square
log_cosh	Calculates the log-cosh loss for a set of predictions.
smooth_labels	Apply label smoothing.