neuralmonkey.trainers.delayed_update_trainer module¶
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class
neuralmonkey.trainers.delayed_update_trainer.
DelayedUpdateTrainer
(batches_per_update: int, objectives: List[neuralmonkey.trainers.objective.Objective], l1_weight: float = 0.0, l2_weight: float = 0.0, clip_norm: float = None, optimizer: tensorflow.python.training.optimizer.Optimizer = None, var_scopes: List[str] = None, var_collection: str = None) → None¶ Bases:
neuralmonkey.trainers.generic_trainer.GenericTrainer
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class
Executable
(executor: neuralmonkey.trainers.delayed_update_trainer.DelayedUpdateTrainer, compute_losses: bool, summaries: bool, num_sessions: int) → None¶ Bases:
neuralmonkey.runners.base_runner.Executable
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__init__
(executor: neuralmonkey.trainers.delayed_update_trainer.DelayedUpdateTrainer, compute_losses: bool, summaries: bool, num_sessions: int) → None¶ Initialize self. See help(type(self)) for accurate signature.
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collect_results
(results: List[Dict]) → None¶
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next_to_execute
() → Tuple[Union[Dict, List], List[Dict[tensorflow.python.framework.ops.Tensor, Union[int, float, numpy.ndarray]]]]¶ Get the tensors and additional feed dicts for execution.
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__init__
(batches_per_update: int, objectives: List[neuralmonkey.trainers.objective.Objective], l1_weight: float = 0.0, l2_weight: float = 0.0, clip_norm: float = None, optimizer: tensorflow.python.training.optimizer.Optimizer = None, var_scopes: List[str] = None, var_collection: str = None) → None¶ Initialize self. See help(type(self)) for accurate signature.
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accumulate_ops
¶
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cumulator_counter
¶
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diff_buffer
¶
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existing_grads_and_vars
¶
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gradient_buffers
¶
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objective_buffers
¶
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raw_gradients
¶ Return averaged gradients over buffers.
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reset_ops
¶
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summaries
¶
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class