neuralmonkey.trainers.generic_trainer module

class neuralmonkey.trainers.generic_trainer.GenericTrainer(objectives: Sequence[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.runners.base_runner.GraphExecutor

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

Bases: neuralmonkey.runners.base_runner.Executable

__init__(executor: neuralmonkey.trainers.generic_trainer.GenericTrainer, 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__(objectives: Sequence[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.

static default_optimizer()

differentiable_loss_sum

Compute the differentiable loss (including regularization).

fetches

gradients

objective_values

Compute unweighted losses for fetching.

raw_gradients

Compute the gradients.

regularization_losses

Compute the regularization losses, e.g. L1 and L2.

summaries

train_op

Construct the training op.

var_list