# 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.