mwptoolkit.loss.masked_cross_entropy_loss

class mwptoolkit.loss.masked_cross_entropy_loss.MaskedCrossEntropyLoss[source]

Bases: AbstractLoss

eval_batch(outputs, target, length)[source]

calculate loss

Parameters

  • outputs (Tensor) – output distribution of model.

  • target (Tensor) – target classes.

  • length (Tensor) – length of target.

get_loss()[source]

return loss

Returns

loss (float)

mwptoolkit.loss.masked_cross_entropy_loss.masked_cross_entropy(logits, target, length)[source]
Parameters

  • logits – A Variable containing a FloatTensor of size (batch, max_len, num_classes) which contains the unnormalized probability for each class.

  • target – A Variable containing a LongTensor of size (batch, max_len) which contains the index of the true class for each corresponding step.

  • length – A Variable containing a LongTensor of size (batch,) which contains the length of each data in a batch.

Returns

An average loss value masked by the length.

Return type

loss

mwptoolkit.loss.masked_cross_entropy_loss.sequence_mask(sequence_length, max_len=None)[source]