MeanSquaredErrorInverse#
- class torch_uncertainty.metrics.regression.MeanSquaredErrorInverse(squared=True, num_outputs=1, unit='km', **kwargs)[source]#
Mean Squared Error of the inverse predictions (iMSE).
\[\text{iMSE} = \frac{1}{N}\sum_i^N(\frac{1}{y_i} - \frac{1}{\hat{y_i}})^2\]Where \(y\) is a tensor of target values, and \(\hat{y}\) is a tensor of predictions. Both are scaled by a factor of
unit_factor
depending on theunit
given.As input to
forward
andupdate
the metric accepts the following input:preds (
Tensor
): Predictions from modeltarget (
Tensor
): Ground truth values
As output of
forward
andcompute
the metric returns the following output:mean_squared_error (
Tensor
): A tensor with the mean squared error
- Parameters:
squared – If True returns MSE value, if False returns RMSE value.
num_outputs – Number of outputs in multioutput setting.
unit – Unit for the computation of the metric. Must be one of ‘mm’, ‘m’, ‘km’. Defauts to ‘km’.
kwargs – Additional keyword arguments.