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# Min / Max¶

## Module Interface¶

class torchmetrics.MinMaxMetric(base_metric, **kwargs)[source]

Wrapper Metric that tracks both the minimum and maximum of a scalar/tensor across an experiment. The min/max value will be updated each time .compute is called.

Parameters
Raises

ValueError – If base_metric argument is not a subclasses instance of torchmetrics.Metric

Example::
>>> import torch
>>> from torchmetrics import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> from pprint import pprint
>>> base_metric = BinaryAccuracy()
>>> minmax_metric = MinMaxMetric(base_metric)
>>> preds_1 = torch.Tensor([[0.1, 0.9], [0.2, 0.8]])
>>> preds_2 = torch.Tensor([[0.9, 0.1], [0.2, 0.8]])
>>> labels = torch.Tensor([[0, 1], [0, 1]]).long()
>>> pprint(minmax_metric(preds_1, labels))
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(1.), 'raw': tensor(1.)}
>>> minmax_metric.update(preds_2, labels)
>>> pprint(minmax_metric.compute())
{'max': tensor(1.), 'min': tensor(0.7500), 'raw': tensor(0.7500)}


Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes the underlying metric as well as max and min values for this metric.

Returns a dictionary that consists of the computed value (raw), as well as the minimum (min) and maximum (max) values.

Return type
reset()[source]

Sets max_val and min_val to the initialization bounds and resets the base metric.

Return type

None

update(*args, **kwargs)[source]

Return type

None

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