Shortcuts

CategoricalNLL

class torch_uncertainty.metrics.classification.CategoricalNLL(reduction='mean', **kwargs)[source]

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()[source]

Computes NLL based on inputs passed in to update previously.

update(probs, target)[source]

Update state with prediction probabilities and targets.

Parameters:
  • probs (Tensor) – Probabilities from the model.

  • target (Tensor) – Ground truth labels.