neuralmonkey.trainers.delayed_update_trainer module

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

class Executable(executor: neuralmonkey.trainers.delayed_update_trainer.DelayedUpdateTrainer, compute_losses: bool, summaries: bool, num_sessions: int) → None

Bases: neuralmonkey.runners.base_runner.Executable

__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.

collect_results(results: List[Dict]) → None

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.

__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.

accumulate_ops

cumulator_counter

diff_buffer

existing_grads_and_vars

gradient_buffers

objective_buffers

raw_gradients

Return averaged gradients over buffers.

reset_ops

summaries