def dequeue_up_to(self, n, name=None):
"""Dequeues and concatenates `n` elements from this queue.
**Note** This operation is not supported by all queues. If a queue does not
support DequeueUpTo, then a `tf.errors.UnimplementedError` is raised.
This operation concatenates queue-element component tensors along
the 0th dimension to make a single component tensor. If the queue
has not been closed, all of the components in the dequeued tuple
will have size `n` in the 0th dimension.
If the queue is closed and there are more than `0` but fewer than
`n` elements remaining, then instead of raising a
`tf.errors.OutOfRangeError` like [`dequeue_many`](#QueueBase.dequeue_many),
less than `n` elements are returned immediately. If the queue is
closed and there are `0` elements left in the queue, then a
`tf.errors.OutOfRangeError` is raised just like in `dequeue_many`.
Otherwise the behavior is identical to `dequeue_many`.
Args:
n: A scalar `Tensor` containing the number of elements to dequeue.
name: A name for the operation (optional).
Returns:
The tuple of concatenated tensors that was dequeued.
"""
if name is None:
name = "%s_DequeueUpTo" % self._name
ret = gen_data_flow_ops._queue_dequeue_up_to(
self._queue_ref, n=n, component_types=self._dtypes, name=name)
# NOTE(mrry): Not using a shape function because we need access to
# the Queue object.
op = ret[0].op
for output, shape in zip(op.values(), self._shapes):
output.set_shape(
tensor_shape.TensorShape([None]).concatenate(shape))
return self._dequeue_return_value(ret)
def dequeue_up_to(self, n, name=None):
"""Dequeues and concatenates `n` elements from this queue.
**Note** This operation is not supported by all queues. If a queue does not
support DequeueUpTo, then a `tf.errors.UnimplementedError` is raised.
This operation concatenates queue-element component tensors along
the 0th dimension to make a single component tensor. If the queue
has not been closed, all of the components in the dequeued tuple
will have size `n` in the 0th dimension.
If the queue is closed and there are more than `0` but fewer than
`n` elements remaining, then instead of raising a
`tf.errors.OutOfRangeError` like [`dequeue_many`](#QueueBase.dequeue_many),
less than `n` elements are returned immediately. If the queue is
closed and there are `0` elements left in the queue, then a
`tf.errors.OutOfRangeError` is raised just like in `dequeue_many`.
Otherwise the behavior is identical to `dequeue_many`.
Args:
n: A scalar `Tensor` containing the number of elements to dequeue.
name: A name for the operation (optional).
Returns:
The tuple of concatenated tensors that was dequeued.
"""
if name is None:
name = "%s_DequeueUpTo" % self._name
ret = gen_data_flow_ops._queue_dequeue_up_to(
self._queue_ref, n=n, component_types=self._dtypes, name=name)
# NOTE(mrry): Not using a shape function because we need access to
# the Queue object.
op = ret[0].op
for output, shape in zip(op.values(), self._shapes):
output.set_shape(tensor_shape.TensorShape([None]).concatenate(shape))
return self._dequeue_return_value(ret)
