MSELoss

class MSELoss(reduction: Literal['mean', 'sum'] = 'mean')

Bases: PointwiseLoss

The mean squared error loss.

Note

The related torch module is torch.nn.MSELoss, but it can not be used interchangeably in PyKEEN because of the extended functionality implemented in PyKEEN’s loss functions.

Initialize the loss.

Parameters:

reduction (Literal['mean', 'sum']) – the reduction, cf. pykeen.nn.modules._Loss

Attributes Summary

synonyms

synonyms of this loss

Methods Summary

forward(x, target[, weight])

Calculate the point-wise loss.

Attributes Documentation

synonyms: ClassVar[set[str] | None] = {'Mean Square Error Loss', 'Mean Squared Error Loss'}

synonyms of this loss

Methods Documentation

forward(x: Tensor, target: Tensor, weight: Tensor | None = None) → Tensor

Calculate the point-wise loss.

Parameters:

• x (Tensor) – The predictions.

• target (Tensor) – The target values (between 0 and 1).

• weight (Tensor | None) – The sample weights.

Returns:

The scalar loss value.

Return type:

Tensor