neuralmonkey.decoders.sequence_labeler module

class neuralmonkey.decoders.sequence_labeler.SequenceLabeler(name: str, encoder: Union[neuralmonkey.encoders.recurrent.RecurrentEncoder, neuralmonkey.encoders.facebook_conv.SentenceEncoder], vocabulary: neuralmonkey.vocabulary.Vocabulary, data_id: str, dropout_keep_prob: float = 1.0, reuse: neuralmonkey.model.model_part.ModelPart = None, save_checkpoint: str = None, load_checkpoint: str = None, initializers: List[Tuple[str, Callable]] = None) → None

Bases: neuralmonkey.model.model_part.ModelPart

Classifier assing a label to each encoder’s state.

__init__(name: str, encoder: Union[neuralmonkey.encoders.recurrent.RecurrentEncoder, neuralmonkey.encoders.facebook_conv.SentenceEncoder], vocabulary: neuralmonkey.vocabulary.Vocabulary, data_id: str, dropout_keep_prob: float = 1.0, reuse: neuralmonkey.model.model_part.ModelPart = None, save_checkpoint: str = None, load_checkpoint: str = 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.

cost

decoded

decoding_b

decoding_residual_w

decoding_w

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.

logits

logprobs

runtime_loss

train_loss