def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None,
names=None, seed=None, shared_name=None,
name="random_shuffle_queue"):
"""Create a queue that dequeues elements in a random order.
A `RandomShuffleQueue` has bounded capacity; supports multiple
concurrent producers and consumers; and provides exactly-once
delivery.
A `RandomShuffleQueue` holds a list of up to `capacity`
elements. Each element is a fixed-length tuple of tensors whose
dtypes are described by `dtypes`, and whose shapes are optionally
described by the `shapes` argument.
If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.
The `min_after_dequeue` argument allows the caller to specify a
minimum number of elements that will remain in the queue after a
`dequeue` or `dequeue_many` operation completes, to ensure a
minimum level of mixing of elements. This invariant is maintained
by blocking those operations until sufficient elements have been
enqueued. The `min_after_dequeue` argument is ignored after the
queue has been closed.
Args:
capacity: An integer. The upper bound on the number of elements
that may be stored in this queue.
min_after_dequeue: An integer (described above).
dtypes: A list of `DType` objects. The length of `dtypes` must equal
the number of tensors in each queue element.
shapes: (Optional.) A list of fully-defined `TensorShape` objects
with the same length as `dtypes`, or `None`.
names: (Optional.) A list of string naming the components in the queue
with the same length as `dtypes`, or `None`. If specified the dequeue
methods return a dictionary with the names as keys.
seed: A Python integer. Used to create a random seed. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
shared_name: (Optional.) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name for the queue operation.
"""
dtypes = _as_type_list(dtypes)
shapes = _as_shape_list(shapes, dtypes)
names = _as_name_list(names, dtypes)
# If shared_name is provided and an op seed was not provided, we must ensure
# that we use the same seed for all queues with the same shared_name.
if shared_name is not None and seed is None:
seed = hash(shared_name)
seed1, seed2 = random_seed.get_seed(seed)
queue_ref = gen_data_flow_ops._random_shuffle_queue(
component_types=dtypes, shapes=shapes, capacity=capacity,
min_after_dequeue=min_after_dequeue, seed=seed1, seed2=seed2,
shared_name=shared_name, name=name)
super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref)
def __init__(self,
capacity,
min_after_dequeue,
dtypes,
shapes=None,
seed=None,
shared_name=None,
name="random_shuffle_queue"):
"""Create a queue that dequeues elements in a random order.
A `RandomShuffleQueue` has bounded capacity; supports multiple
concurrent producers and consumers; and provides exactly-once
delivery.
A `RandomShuffleQueue` holds a list of up to `capacity`
elements. Each element is a fixed-length tuple of tensors whose
dtypes are described by `dtypes`, and whose shapes are optionally
described by the `shapes` argument.
If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.
The `min_after_dequeue` argument allows the caller to specify a
minimum number of elements that will remain in the queue after a
`dequeue` or `dequeue_many` operation completes, to ensure a
minimum level of mixing of elements. This invariant is maintained
by blocking those operations until sufficient elements have been
enqueued. The `min_after_dequeue` argument is ignored after the
queue has been closed.
Args:
capacity: An integer. The upper bound on the number of elements
that may be stored in this queue.
min_after_dequeue: An integer (described above).
dtypes: A list of `DType` objects. The length of `dtypes` must equal
the number of tensors in each queue element.
shapes: (Optional.) A list of fully-defined `TensorShape` objects,
with the same length as `dtypes` or `None`.
seed: A Python integer. Used to create a random seed. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
shared_name: (Optional.) If non-empty, this queue will be shared under
the given name across multiple sessions.
name: Optional name for the queue operation.
"""
dtypes = _as_type_list(dtypes)
shapes = _as_shape_list(shapes, dtypes)
seed1, seed2 = random_seed.get_seed(seed)
queue_ref = gen_data_flow_ops._random_shuffle_queue(
component_types=dtypes,
shapes=shapes,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
seed=seed1,
seed2=seed2,
shared_name=shared_name,
name=name)
super(RandomShuffleQueue, self).__init__(dtypes, shapes, queue_ref)
