- ignite.metrics.Fbeta(beta, average=True, precision=None, recall=None, output_transform=None, device=device(type='cpu'))[source]#
Calculates F-beta score.
where is a positive real factor.
beta (float) – weight of precision in harmonic mean
average (bool) – if True, F-beta score is computed as the unweighted average (across all classes in multiclass case), otherwise, returns a tensor with F-beta score for each class in multiclass case.
precision (Optional[ignite.metrics.precision.Precision]) – precision object metric with average=False to compute F-beta score
recall (Optional[ignite.metrics.recall.Recall]) – recall object metric with average=False to compute F-beta score
output_transform (Optional[Callable]) – a callable that is used to transform the
process_function’s output into the form expected by the metric. It is used only if precision or recall are not provided.
device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
updatearguments ensures the
updatemethod is non-blocking. By default, CPU.
MetricsLambda, F-beta metric
- Return type