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# Mean Squared Log Error (MSLE)¶

## Module Interface¶

class torchmetrics.MeanSquaredLogError(**kwargs)[source]

Compute mean squared logarithmic error (MSLE).

Where is a tensor of target values, and is a tensor of predictions.

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

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

• mean_squared_log_error (Tensor): A tensor with the mean squared log error

Parameters

kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torch import tensor
>>> from torchmetrics.regression import MeanSquaredLogError
>>> target = tensor([2.5, 5, 4, 8])
>>> preds = tensor([3, 5, 2.5, 7])
>>> mean_squared_log_error = MeanSquaredLogError()
>>> mean_squared_log_error(preds, target)
tensor(0.0397)


Note

Half precision is only support on GPU for this metric

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

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

Plot a single or multiple values from the metric.

Parameters
Return type

Tuple[Figure, Union[Axes, ndarray]]

Returns

Figure and Axes object

Raises

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import MeanSquaredLogError
>>> metric = MeanSquaredLogError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()

>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import MeanSquaredLogError
>>> metric = MeanSquaredLogError()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)


## Functional Interface¶

torchmetrics.functional.mean_squared_log_error(preds, target)[source]

Compute mean squared log error.

Parameters
Return type

Tensor

Returns

Tensor with RMSLE

Example

>>> from torchmetrics.functional.regression import mean_squared_log_error
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_squared_log_error(x, y)
tensor(0.0207)


Note

Half precision is only support on GPU for this metric

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