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Mean Squared Log Error (MSLE)

Module Interface

class torchmetrics.MeanSquaredLogError(compute_on_step=None, **kwargs)[source]

Computes mean squared logarithmic error (MSLE):

\text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2

Where y is a tensor of target values, and \hat{y} is a tensor of predictions.

Parameters
  • 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.

Example

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

compute()[source]

Compute mean squared logarithmic error over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

Functional Interface

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

Computes mean squared log error.

Parameters
  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

Return type

Tensor

Returns

Tensor with RMSLE

Example

>>> from torchmetrics.functional 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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