neuralmonkey.tf_manager module

TensorFlow Manager.

TensorFlow manager is a helper object in Neural Monkey which manages TensorFlow sessions, execution of the computation graph, and saving and restoring of model variables.

class neuralmonkey.tf_manager.TensorFlowManager(num_sessions: int, num_threads: int, save_n_best: int = 1, minimize_metric: bool = False, gpu_allow_growth: bool = True, per_process_gpu_memory_fraction: float = 1.0, enable_tf_debug: bool = False) → None

Bases: object

Inteface between computational graph, data and TF sessions.

sessions

List of active Tensorflow sessions.

__init__(num_sessions: int, num_threads: int, save_n_best: int = 1, minimize_metric: bool = False, gpu_allow_growth: bool = True, per_process_gpu_memory_fraction: float = 1.0, enable_tf_debug: bool = False) → None

Initialize a TensorflowManager.

At this moment the graph must already exist. This method initializes required number of TensorFlow sessions and initializes them with provided variable files if they are provided.

Parameters:

• num_sessions – Number of sessions to be initialized.
• num_threads – Number of threads sessions will run in.
• save_n_best – How many best models to keep
• minimize_metric – Whether the best model is the one with the lowest or the highest score
• gpu_allow_growth – TF to allocate incrementally, not all at once.
• per_process_gpu_memory_fraction – Limit TF memory use.

best_vars_file

execute(batch: neuralmonkey.dataset.Dataset, feedables: Set[neuralmonkey.model.feedable.Feedable], runners: Sequence[neuralmonkey.runners.base_runner.GraphExecutor], train: bool = False, compute_losses: bool = True, summaries: bool = True) → List[neuralmonkey.runners.base_runner.ExecutionResult]

Execute runners on a batch of data.

First, extract executables from the provided runners, telling the runners whether to compute also losses and summaries. Second, until all executables are satisfied (have the result attribute set), run the executables on the batch.

Parameters:

• batch – A batch of data.
• execution_scripts – List of runners to execute.
• train – Training mode flag (this value is fed to the train_mode placeholders in model parts).
• compute_losses – Flag to runners whether run loss operations.
• summaries – Flag to runners whether to run summary operations.

Returns:

A list of ExecutionResult tuples, one for each executable (runner).

init_saving(vars_prefix: str) → None

initialize_model_parts(runners: Sequence[neuralmonkey.runners.base_runner.GraphExecutor], save: bool = False) → None

Initialize model parts variables from their checkpoints.

initialize_sessions() → None

restore(variable_files: Union[str, List[str]]) → None

restore_best_vars() → None

save(variable_files: Union[str, List[str]]) → None

validation_hook(score: float, epoch: int, batch: int) → None

neuralmonkey.tf_manager.get_default_tf_manager() → neuralmonkey.tf_manager.TensorFlowManager