---
title: Min / Max
description: >-
  Wrapper metric that tracks both the minimum and maximum of a scalar/tensor
  across an experiment.
canonical: 'https://lightning.ai/docs/torchmetrics/stable/wrappers/min_max'
---
# Min / Max {#min-max}

## Module Interface {#module-interface}

### `class torchmetrics.wrappers.MinMaxMetric(base_metric, **kwargs)` {#torchmetrics.wrappers.MinMaxMetric}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/wrappers/minmax.py#L30-L160)

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:

*   **base\_metric** ([`Metric`](/docs/torchmetrics/1.9.0/api-reference/metric.md#torchmetrics.Metric "torchmetrics.metric.Metric")) – The metric of which you want to keep track of its maximum and minimum values.
    
*   **kwargs** ([`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")) – Additional keyword arguments, see [Advanced metric settings](/docs/torchmetrics/1.9.0/user-guide/overview.md#metric-kwargs) for more info.
    

Raises:

[**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError "(in Python v3.15)") – If ```base_metric` argument is not a subclasses instance of ``torchmetrics.Metric```

Example::

```python
>>> import torch
>>> from torchmetrics.wrappers 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)}
```

#### `compute()` {#torchmetrics.wrappers.MinMaxMetric.compute}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/wrappers/minmax.py#L86-L98)

Compute 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:

[`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\]

#### `forward(*args, **kwargs)` {#torchmetrics.wrappers.MinMaxMetric.forward}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/wrappers/minmax.py#L100-L102)

Use the original forward method of the base metric class.

Return type:

[`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")

#### `plot(val=None, ax=None)` {#torchmetrics.wrappers.MinMaxMetric.plot}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/wrappers/minmax.py#L118-L160)

Plot a single or multiple values from the metric.

Parameters:

*   **val** (`Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
    
*   **ax** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)")\]) – An matplotlib axis object. If provided will add plot to that axis
    

Return type:

[`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Figure`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure "(in Matplotlib v3.11.2)"), `Union`\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)"), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.5)")\]\]

Returns:

Figure and Axes object

Raises:

[**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If matplotlib is not installed

```python
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> metric = MinMaxMetric(BinaryAccuracy())
>>> metric.update(torch.randint(2, (20,)), torch.randint(2, (20,)))
>>> fig_, ax_ = metric.plot()
```

[![../../\_images/min\_max-1.png](/docs/torchmetrics/1.9.0/static/_images/min_max-1.png)](/docs/torchmetrics/1.9.0/static/_images/min_max-1.png)

```python
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.wrappers import MinMaxMetric
>>> from torchmetrics.classification import BinaryAccuracy
>>> metric = MinMaxMetric(BinaryAccuracy())
>>> values = [ ]
>>> for _ in range(3):
...     values.append(metric(torch.randint(2, (20,)), torch.randint(2, (20,))))
>>> fig_, ax_ = metric.plot(values)
```

[![../../\_images/min\_max-2.png](/docs/torchmetrics/1.9.0/static/_images/min_max-2.png)](/docs/torchmetrics/1.9.0/static/_images/min_max-2.png)

#### `reset()` {#torchmetrics.wrappers.MinMaxMetric.reset}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/wrappers/minmax.py#L104-L107)

Set `max_val` and `min_val` to the initialization bounds and resets the base metric.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `update(*args, **kwargs)` {#torchmetrics.wrappers.MinMaxMetric.update}

[\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/wrappers/minmax.py#L82-L84)

Update the underlying metric.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

---

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