neuralmonkey.runners.plain_runner module

class neuralmonkey.runners.plain_runner.PlainExecutable(all_coders: Set[neuralmonkey.model.model_part.ModelPart], fetches: Dict[tensorflow.python.framework.ops.Tensor, Union[int, float, numpy.ndarray]], num_sessions: int, vocabulary: neuralmonkey.vocabulary.Vocabulary, postprocess: Union[Callable[[List[List[str]]], List[List[str]]], NoneType]) → None

Bases: neuralmonkey.runners.base_runner.Executable

__init__(all_coders: Set[neuralmonkey.model.model_part.ModelPart], fetches: Dict[tensorflow.python.framework.ops.Tensor, Union[int, float, numpy.ndarray]], num_sessions: int, vocabulary: neuralmonkey.vocabulary.Vocabulary, postprocess: Union[Callable[[List[List[str]]], List[List[str]]], NoneType]) → None

Initialize self. See help(type(self)) for accurate signature.

collect_results(results: List[Dict]) → None

next_to_execute() → Tuple[Set[neuralmonkey.model.model_part.ModelPart], Union[Dict, List], List[Dict[tensorflow.python.framework.ops.Tensor, Union[int, float, numpy.ndarray]]]]

Get the feedables and tensors to run.

class neuralmonkey.runners.plain_runner.PlainRunner(output_series: str, decoder: Union[neuralmonkey.decoders.autoregressive.AutoregressiveDecoder, neuralmonkey.decoders.ctc_decoder.CTCDecoder, neuralmonkey.decoders.classifier.Classifier, neuralmonkey.decoders.sequence_labeler.SequenceLabeler], postprocess: Callable[[List[List[str]]], List[List[str]]] = None) → None

Bases: neuralmonkey.runners.base_runner.BaseRunner

A runner which takes the output from decoder.decoded.

__init__(output_series: str, decoder: Union[neuralmonkey.decoders.autoregressive.AutoregressiveDecoder, neuralmonkey.decoders.ctc_decoder.CTCDecoder, neuralmonkey.decoders.classifier.Classifier, neuralmonkey.decoders.sequence_labeler.SequenceLabeler], postprocess: Callable[[List[List[str]]], List[List[str]]] = None) → None

Initialize self. See help(type(self)) for accurate signature.

get_executable(compute_losses: bool, summaries: bool, num_sessions: int)

loss_names