neuralmonkey.runners.base_runner module¶

class
neuralmonkey.runners.base_runner.
BaseRunner
(output_series: str, decoder: MP) → None¶ Bases:
neuralmonkey.runners.base_runner.GraphExecutor
,typing.Generic
Base class for runners.
Runners are graph executors that retrieve tensors from the model without changing the model parameters. Each runner has a toplevel model part it relates to.

class
Executable
(executor: Executor, compute_losses: bool, summaries: bool, num_sessions: int) → None¶ Bases:
neuralmonkey.runners.base_runner.Executable

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__
(output_series: str, decoder: MP) → None¶ Initialize self. See help(type(self)) for accurate signature.

decoder_data_id
¶

loss_names
¶

class

class
neuralmonkey.runners.base_runner.
ExecutionResult
¶ Bases:
neuralmonkey.runners.base_runner.ExecutionResult
A data structure that represents the result of a graph execution.
The goal of each runner is to populate this structure and set it as its
self._result
.
outputs
¶ A batch of outputs of the runner.

losses
¶ A (possibly empty) list of loss values computed during the run.

scalar_summaries
¶ A TensorFlow summary object with scalar values.

histogram_summaries
¶ A TensorFlow summary object with histograms.

image_summaries
¶ A TensorFlow summary object with images.


class
neuralmonkey.runners.base_runner.
GraphExecutor
(dependencies: Set[neuralmonkey.model.model_part.GenericModelPart]) → None¶ Bases:
neuralmonkey.model.model_part.GenericModelPart
The abstract parent class of all graph executors.
In Neural Monkey, a graph executor is an object that retrieves tensors from the computational graph. The two major groups of graph executors are trainers and runners.
Each graph executor is an instance of GenericModelPart class, which means it has parameterized and feedable dependencies which reference the model part objects needed to be created in order to compute the tensors of interest (called “fetches”).
Every graph executor has a method called get_executable, which returns an GraphExecutor.Executable instance, which specifies what tensors to execute and collects results from the session execution.

class
Executable
(executor: Executor, compute_losses: bool, summaries: bool, num_sessions: int) → None¶ Bases:
typing.Generic
Abstract base class for executables.
Executables are objects associated with the graph executors. Each executable has two main functions: next_to_execute and collect_results. These functions are called in a loop, until the executable’s result has been set.
To make use of Mypy’s type checking, the executables are generic and are parameterized by the type of their graph executor. Since Python does not know the concept of nested classes, each executable receives the instance of the graph executor through its constructor.
When subclassing GraphExecutor, it is also necessary to subclass the Executable class and name it Executable, so it overrides the definition of this class. Following this guideline, the default implementation of the get_executable function on the graph executor will work without the need of overriding it.

__init__
(executor: Executor, compute_losses: bool, summaries: bool, num_sessions: int) → None¶ Initialize self. See help(type(self)) for accurate signature.

collect_results
(results: List[Dict]) → None¶

executor
¶

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.

result
¶

set_result
(outputs: List[Any], losses: List[float], scalar_summaries: tensorflow.core.framework.summary_pb2.Summary, histogram_summaries: tensorflow.core.framework.summary_pb2.Summary, image_summaries: tensorflow.core.framework.summary_pb2.Summary) → None¶


__init__
(dependencies: Set[neuralmonkey.model.model_part.GenericModelPart]) → None¶ Initialize self. See help(type(self)) for accurate signature.

dependencies
¶ Return a list of attribute names regarded as dependents.

feedables
¶

fetches
¶

get_executable
(compute_losses: bool, summaries: bool, num_sessions: int) → neuralmonkey.runners.base_runner.GraphExecutor.Executable¶

parameterizeds
¶

class

neuralmonkey.runners.base_runner.
reduce_execution_results
(execution_results: List[neuralmonkey.runners.base_runner.ExecutionResult]) → neuralmonkey.runners.base_runner.ExecutionResult¶ Aggregate execution results into one.