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Defined in tensorflow/contrib/training/python/training/tensor_queue_dataset.py.

A transformation that prepends a queue to a Dataset and batches results.

A vector of handles to the queue is returned as the first component of the associated iterator. This vector can be passed to enqueue_in_queue_dataset to add new elements to the queue.

Below is an example of how this dataset might be used to split incoming variable-length sequences into "head" and "rest" parts, where "rest" parts are re-enqueued back into the dataset. A more realistic example would perform some calculation on the "head" and modify some components of "rest" with the result (before re-enqueueing).

dataset = tf.data.Dataset.from_tensor_slices([2*x for x in range(10)])
# Make a dataset of variable-length vectors and their lengths.
dataset = dataset.map(lambda count: (count, tf.ones((count,))))
# Emit a queue we can prepend to, and counts/values as padded batch.
dataset = dataset.apply(
dataset = dataset.prefetch(1)

iterator = dataset.make_one_shot_iterator()
queue, (count, padded_value) = iterator.get_next()

# Split the padded_value into two pieces: head and rest
rest_indices = tf.squeeze(tf.where(count > 3), axis=1)
bound = tf.minimum(3, tf.reduce_max(count))
value_head = padded_value[:, :bound]
count_rest = tf.gather(count - 3, rest_indices)
value_rest = tf.gather(padded_value[:, bound:], rest_indices)
queue_rest = tf.gather(queue, rest_indices)
enqueue_rest_op = tf.contrib.training.enqueue_in_queue_dataset(
  queue_rest, (count_rest, value_rest))
with tf.control_dependencies([enqueue_rest_op]):
  calculation = fn(value_head)

while True:  # Will raise OutOfRange when finished with all pieces.


  • batch_size: int64 scalar tensor. The batch size to use when performing padded batching.
  • padding_values: (optional) Nested tuple of scalar tensors. If provided, the structure and dtypes of padding_values should match that of incoming dataset's output_types.
  • padded_shapes: (optional) Nested tuple of int64 vector tensors. If provided, the structure must match that of the incoming dataset's output_types. If not provided, the incoming dataset's output_shapes is used. Any unknown (None or -1) dimensions in the shapes are treated as being unique per-batch: for each batch time, an unknown dimension is replaced with the maximum given value of this dimension across all tensors for the given component in the batch.


A Dataset transformation function, which can be passed to tf.data.Dataset.apply.