LostTech.TensorFlow : API Documentation

Type SequenceEnqueuer

Namespace tensorflow.keras.utils

Parent PythonObjectContainer

Interfaces ISequenceEnqueuer

Base class to enqueue inputs.

The task of an Enqueuer is to use parallelism to speed up preprocessing. This is done with processes or threads.

Example: The `enqueuer.get()` should be an infinite stream of datas.
Show Example 
enqueuer = SequenceEnqueuer(...)
            enqueuer.start()
            datas = enqueuer.get()
            for data in datas:
                # Use the inputs; training, evaluating, predicting.
                #... stop sometime.
            enqueuer.close()

Methods

Properties

Public instance methods

bool is_running()

object is_running_dyn()

void start(int workers, int max_queue_size)

Starts the handler's workers.
Parameters
int workers
Number of workers.
int max_queue_size
queue size (when full, workers could block on `put()`)

object start_dyn(ImplicitContainer<T> workers, ImplicitContainer<T> max_queue_size)

Starts the handler's workers.
Parameters
ImplicitContainer<T> workers
Number of workers.
ImplicitContainer<T> max_queue_size
queue size (when full, workers could block on `put()`)

void stop(object timeout)

Stops running threads and wait for them to exit, if necessary.

Should be called by the same thread which called `start()`.
Parameters
object timeout
maximum time to wait on `thread.join()`

object stop_dyn(object timeout)

Stops running threads and wait for them to exit, if necessary.

Should be called by the same thread which called `start()`.
Parameters
object timeout
maximum time to wait on `thread.join()`

Public properties

object executor_fn get; set;

object PythonObject get;

PythonClassContainer queue get; set;

PythonClassContainer run_thread get; set;

object sequence get; set;

PythonClassContainer stop_signal get; set;

Nullable<int> uid get; set;

bool use_multiprocessing get; set;

int workers get; set;