CategoricalNLL

class torch_uncertainty.metrics.CategoricalNLL(reduction='mean', **kwargs)

The Negative Log Likelihood Metric.

Parameters:

• reduction (str, optional) –

  Determines how to reduce over the \(B\)/batch dimension:

  • 'mean' [default]: Averages score across samples

  • 'sum': Sum score across samples

  • 'none' or None: Returns score per sample

• kwargs – Additional keyword arguments, see Advanced metric settings.

Inputs:

• probs: \((B, C)\)

• target: \((B)\)

where \(B\) is the batch size and \(C\) is the number of classes.

Warning

Make sure that the probabilities in probs are normalized to sum to one.

Raises:

ValueError – If reduction is not one of 'mean', 'sum', 'none' or None.

compute()

Computes NLL based on inputs passed in to update previously.

update(probs, target)

Update state with prediction probabilities and targets.

Parameters:

• probs (Tensor) – Probabilities from the model.

• target (Tensor) – Ground truth labels.