neuralmonkey.decoders.sequence_regressor module¶
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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, 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.
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__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, save_checkpoint: str = None, load_checkpoint: str = None, initializers: List[Tuple[str, Callable]] = None) → None¶ Initialize self. See help(type(self)) for accurate signature.
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cost
¶
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decoded
¶
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feed_dict
(dataset: neuralmonkey.dataset.dataset.Dataset, train: bool = False) → Dict[tensorflow.python.framework.ops.Tensor, Any]¶
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predictions
¶
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runtime_loss
¶
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train_loss
¶
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