neuralmonkey.decoders.sequence_regressor module

class neuralmonkey.decoders.sequence_regressor.SequenceRegressor(name: str, encoders: List[neuralmonkey.model.stateful.Stateful], data_id: str, layers: List[int] = None, activation_fn: Callable[[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor] = <function relu>, dropout_keep_prob: float = 1.0, dimension: int = 1, 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

A simple MLP regression over encoders.

The API pretends it is an RNN decoder which always generates a sequence of length exactly one.

__init__(name: str, encoders: List[neuralmonkey.model.stateful.Stateful], data_id: str, layers: List[int] = None, activation_fn: Callable[[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor] = <function relu>, dropout_keep_prob: float = 1.0, dimension: int = 1, 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

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.

input_shapes

input_types

predictions

runtime_loss

train_inputs

train_loss