Shortcuts

# Min / Max¶

## Module Interface¶

class torchmetrics.MinMaxMetric(base_metric, compute_on_step=None, **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 Accuracy
>>> from pprint import pprint
>>> base_metric = Accuracy()
>>> 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]

Updates the underlying metric.

Return type

None

© Copyright Copyright (c) 2020-2022, PyTorchLightning et al... Revision d27f1710`.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: v0.8.1
Versions
latest
stable
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.