Class SequenceEnqueuer

Defined in tensorflow/python/keras/utils/data_utils.py.

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:

    enqueuer = SequenceEnqueuer(...)
    enqueuer.start()
    datas = enqueuer.get()
    for data in datas:
        # Use the inputs; training, evaluating, predicting.
        # ... stop sometime.
    enqueuer.close()

The enqueuer.get() should be an infinite stream of datas.

__init__

__init__(
    sequence,
    use_multiprocessing=False
)

Initialize self. See help(type(self)) for accurate signature.

Methods

tf.keras.utils.SequenceEnqueuer.get

get()

Creates a generator to extract data from the queue.

Skip the data if it is None.

Returns

Generator yielding tuples `(inputs, targets)`
    or `(inputs, targets, sample_weights)`.

tf.keras.utils.SequenceEnqueuer.is_running

is_running()

tf.keras.utils.SequenceEnqueuer.start

start(
    workers=1,
    max_queue_size=10
)

Starts the handler's workers.

Arguments:

• workers: Number of workers.
• max_queue_size: queue size (when full, workers could block on put())

tf.keras.utils.SequenceEnqueuer.stop

stop(timeout=None)

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

Should be called by the same thread which called start().

Arguments:

• timeout: maximum time to wait on thread.join()