Mean Squared Log Error (MSLE)¶

Module Interface¶

class torchmetrics.MeanSquaredLogError(**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.

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

preds (Tensor): Predictions from model
target (Tensor): Ground truth values

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 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.

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