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# Mean¶

## Module Interface¶

class torchmetrics.aggregation.MeanMetric(nan_strategy='warn', **kwargs)[source]

Aggregate a stream of value into their mean value.

As input to forward and update the metric accepts the following input

As output of forward and compute the metric returns the following output

• agg (Tensor): scalar float tensor with aggregated (weighted) mean over all inputs received

Parameters:

nan_strategy (Union[str, float]) –

options:
• 'error': if any nan values are encounted will give a RuntimeError

• 'warn': if any nan values are encounted will give a warning and continue

• 'ignore': all nan values are silently removed

• a float: if a float is provided will impude any nan values with this value

Raises:

ValueError – If nan_strategy is not one of error, warn, ignore or a float

Example

>>> from torchmetrics.aggregation import MeanMetric
>>> metric = MeanMetric()
>>> metric.update(1)
>>> metric.update(torch.tensor([2, 3]))
>>> metric.compute()
tensor(2.)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> from torchmetrics.aggregation import MeanMetric
>>> metric = MeanMetric()
>>> metric.update([1, 2, 3])
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> from torchmetrics.aggregation import MeanMetric
>>> metric = MeanMetric()
>>> values = [ ]
>>> for i in range(10):
...     values.append(metric([i, i+1]))
>>> fig_, ax_ = metric.plot(values)


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