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tf.keras.utils.SequenceEnqueuer
Base class to enqueue inputs.
tf.keras.utils.SequenceEnqueuer(
    sequence, use_multiprocessing=False
)
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.stop()
The enqueuer.get() should be an infinite stream of datas.
Methods
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).
is_running
  
  is_running()
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()) | 
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() | 
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
 https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/utils/SequenceEnqueuer