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Minimum

Module Interface

class torchmetrics.MinMetric(nan_strategy='warn', compute_on_step=None, **kwargs)[source]

Aggregate a stream of value into their minimum value.

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

  • compute_on_step (Optional[bool]) –

    Forward only calls update() and returns None if this is set to False.

    Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.

  • kwargs (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

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

Example

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

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

update(value)[source]

Update state with data.

Parameters

value (Union[float, Tensor]) – Either a float or tensor containing data. Additional tensor dimensions will be flattened

Return type

None

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