CrossEntropyLoss

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

Bases: SetwiseLoss

The cross entropy loss that evaluates the cross entropy after softmax output.

Note

The related torch module is torch.nn.CrossEntropyLoss, 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

Methods Summary

process_lcwa_scores(predictions, labels[, ...])

Process scores from LCWA training loop.

process_slcwa_scores(positive_scores, ...[, ...])

Process scores from sLCWA training loop.

Methods Documentation

process_lcwa_scores(predictions: Tensor, labels: Tensor, label_smoothing: float | None = None, num_entities: int | None = None, weights: Tensor | None = None) → Tensor[source]

Process scores from LCWA training loop.

Parameters:

  • predictions (Tensor) – shape: (*shape) The scores.

  • labels (Tensor) – shape: (*shape) The labels.

  • label_smoothing (float | None) – An optional label smoothing parameter.

  • num_entities (int | None) – The number of entities (required for label-smoothing).

  • weights (Tensor | None) – shape: (*shape) Sample weights.

Returns:

A scalar loss value.

Return type:

Tensor

process_slcwa_scores(positive_scores: Tensor, negative_scores: Tensor, label_smoothing: float | None = None, batch_filter: Tensor | None = None, num_entities: int | None = None, pos_weights: Tensor | None = None, neg_weights: Tensor | None = None) → Tensor[source]

Process scores from sLCWA training loop.

Parameters:

  • positive_scores (Tensor) – shape: (batch_size, 1) The scores for positive triples.

  • negative_scores (Tensor) – shape: (batch_size, num_neg_per_pos) or (num_unfiltered_negatives,) The scores for the negative triples, either in dense 2D shape, or in case they are already filtered, in sparse shape. If they are given in sparse shape, batch_filter needs to be provided, too.

  • label_smoothing (float | None) – An optional label smoothing parameter.

  • batch_filter (Tensor | None) – shape: (batch_size, num_neg_per_pos) An optional filter of negative scores which were kept. Given if and only if negative_scores have been pre-filtered.

  • num_entities (int | None) – The number of entities. Only required if label smoothing is enabled.

  • pos_weights (Tensor | None) – shape: (batch_size, 1) Positive sample weights.

  • neg_weights (Tensor | None) – shape: (batch_size, num_neg_per_pos) Negative sample weights.

Returns:

A scalar loss term.

Return type:

Tensor