neuralmonkey.decoders.word_alignment_decoder module

class neuralmonkey.decoders.word_alignment_decoder.WordAlignmentDecoder(encoder: neuralmonkey.encoders.recurrent.RecurrentEncoder, decoder: neuralmonkey.decoders.decoder.Decoder, data_id: str, name: str, reuse: neuralmonkey.model.model_part.ModelPart = None, initializers: List[Tuple[str, Callable]] = None) → None

Bases: neuralmonkey.model.model_part.ModelPart

A decoder that computes soft alignment from an attentive encoder.

Loss is computed as cross-entropy against a reference alignment.

__init__(encoder: neuralmonkey.encoders.recurrent.RecurrentEncoder, decoder: neuralmonkey.decoders.decoder.Decoder, data_id: str, name: str, reuse: neuralmonkey.model.model_part.ModelPart = None, initializers: List[Tuple[str, Callable]] = None) → None

Construct a new parameterized object.

Parameters:

• name – The name for the model part. Will be used in the variable and name scopes.
• reuse – Optional parameterized part with which to share parameters.
• save_checkpoint – Optional path to a checkpoint file which will store the parameters of this object.
• load_checkpoint – Optional path to a checkpoint file from which to load initial variables for this object.
• initializers – An InitializerSpecs instance with specification of the initializers.

alignment_target

cost

decoded

feed_dict(dataset: neuralmonkey.dataset.Dataset, train: bool = False) → Dict[tensorflow.python.framework.ops.Tensor, Any]

Return a feed dictionary for the given feedable object.

Parameters:

• dataset – A dataset instance from which to get the data.
• train – Boolean indicating whether the model runs in training mode.

Returns:

A FeedDict dictionary object.

ref_alignment

runtime_loss

runtime_outputs

train_loss