neuralmonkey.trainers package¶
Submodules¶
neuralmonkey.trainers.cross_entropy_trainer module¶
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class
neuralmonkey.trainers.cross_entropy_trainer.CrossEntropyTrainer(decoders: typing.List[typing.Any], decoder_weights: typing.Union[typing.List[typing.Union[tensorflow.python.framework.ops.Tensor, float, NoneType]], NoneType] = None, l1_weight=0.0, l2_weight=0.0, clip_norm=False, optimizer=None, global_step=None) → None¶
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neuralmonkey.trainers.cross_entropy_trainer.xent_objective(decoder, weight=None) → neuralmonkey.trainers.generic_trainer.Objective¶ Get XENT objective from decoder with cost.
neuralmonkey.trainers.generic_trainer module¶
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class
neuralmonkey.trainers.generic_trainer.GenericTrainer(objectives: typing.List[neuralmonkey.trainers.generic_trainer.Objective], l1_weight: float = 0.0, l2_weight: float = 0.0, clip_norm: typing.Union[float, NoneType] = None, optimizer=None, global_step=None) → None¶ Bases:
object-
get_executable(compute_losses=True, summaries=True) → neuralmonkey.runners.base_runner.Executable¶
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class
neuralmonkey.trainers.generic_trainer.Objective(name, decoder, loss, gradients, weight)¶ Bases:
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decoder¶ Alias for field number 1
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gradients¶ Alias for field number 3
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loss¶ Alias for field number 2
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name¶ Alias for field number 0
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weight¶ Alias for field number 4
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class
neuralmonkey.trainers.generic_trainer.TrainExecutable(all_coders, train_op, losses, scalar_summaries, histogram_summaries)¶ Bases:
neuralmonkey.runners.base_runner.Executable-
collect_results(results: typing.List[typing.Dict]) → None¶
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next_to_execute() → typing.Tuple[typing.List[typing.Any], typing.Union[typing.Dict, typing.List], typing.Dict[tensorflow.python.framework.ops.Tensor, typing.Union[int, float, numpy.ndarray]]]¶
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