本文整理汇总了Python中tensorflow.python.ops.data_flow_ops.Queues方法的典型用法代码示例。如果您正苦于以下问题：Python data_flow_ops.Queues方法的具体用法？Python data_flow_ops.Queues怎么用？Python data_flow_ops.Queues使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.data_flow_ops的用法示例。
在下文中一共展示了data_flow_ops.Queues方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import Queues [as 别名]
def __init__(self,
reader_class,
common_queue,
num_readers=4,
reader_kwargs=None):
"""ParallelReader creates num_readers instances of the reader_class.
Each instance is created by calling the `reader_class` function passing
the arguments specified in `reader_kwargs` as in:
reader_class(**read_kwargs)
When you read from a ParallelReader, with its `read()` method,
you just dequeue examples from the `common_queue`.
The readers will read different files in parallel, asynchronously enqueueing
their output into `common_queue`. The `common_queue.dtypes` must be
[tf.string, tf.string]
Because each reader can read from a different file, the examples in the
`common_queue` could be from different files. Due to the asynchronous
reading there is no guarantee that all the readers will read the same
number of examples.
If the `common_queue` is a shuffling queue, then the examples are shuffled.
Usage:
common_queue = tf.RandomShuffleQueue(
capacity=256,
min_after_dequeue=128,
dtypes=[tf.string, tf.string])
p_reader = ParallelReader(tf.TFRecordReader, common_queue)
common_queue = tf.FIFOQueue(
capacity=256,
dtypes=[tf.string, tf.string])
p_reader = ParallelReader(readers, common_queue, num_readers=2)
Args:
reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader
common_queue: a Queue to hold (key, value pairs) with `dtypes` equal to
[tf.string, tf.string]. Must be one of the data_flow_ops.Queues
instances, ex. `tf.FIFOQueue()`, `tf.RandomShuffleQueue()`, ...
num_readers: a integer, number of instances of reader_class to create.
reader_kwargs: an optional dict of kwargs to create the readers.
Raises:
TypeError: if `common_queue.dtypes` is not [tf.string, tf.string].
"""
if len(common_queue.dtypes) != 2:
raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
for dtype in common_queue.dtypes:
if not dtype.is_compatible_with(tf_dtypes.string):
raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
reader_kwargs = reader_kwargs or {}
self._readers = [reader_class(**reader_kwargs) for _ in range(num_readers)]
self._common_queue = common_queue 示例2: __init__
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import Queues [as 别名]
def __init__(self,
reader_class,
common_queue,
num_readers=4,
reader_kwargs=None):
"""ParallelReader creates num_readers instances of the reader_class.
Each instance is created by calling the `reader_class` function passing
the arguments specified in `reader_kwargs` as in:
reader_class(**read_kwargs)
When you read from a ParallelReader, with its `read()` method,
you just dequeue examples from the `common_queue`.
The readers will read different files in parallel, asynchronously enqueueing
their output into `common_queue`. The `common_queue.dtypes` must be
[tf.string, tf.string]
Because each reader can read from a different file, the examples in the
`common_queue` could be from different files. Due to the asynchronous
reading there is no guarantee that all the readers will read the same
number of examples.
If the `common_queue` is a shuffling queue, then the examples are shuffled.
Usage:
common_queue = tf.queue.RandomShuffleQueue(
capacity=256,
min_after_dequeue=128,
dtypes=[tf.string, tf.string])
p_reader = ParallelReader(tf.compat.v1.TFRecordReader, common_queue)
common_queue = tf.queue.FIFOQueue(
capacity=256,
dtypes=[tf.string, tf.string])
p_reader = ParallelReader(readers, common_queue, num_readers=2)
Args:
reader_class: one of the io_ops.ReaderBase subclasses ex: TFRecordReader
common_queue: a Queue to hold (key, value pairs) with `dtypes` equal to
[tf.string, tf.string]. Must be one of the data_flow_ops.Queues
instances, ex. `tf.queue.FIFOQueue()`, `tf.queue.RandomShuffleQueue()`,
...
num_readers: a integer, number of instances of reader_class to create.
reader_kwargs: an optional dict of kwargs to create the readers.
Raises:
TypeError: if `common_queue.dtypes` is not [tf.string, tf.string].
"""
if len(common_queue.dtypes) != 2:
raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
for dtype in common_queue.dtypes:
if not dtype.is_compatible_with(tf_dtypes.string):
raise TypeError('common_queue.dtypes must be [tf.string, tf.string]')
reader_kwargs = reader_kwargs or {}
self._readers = [reader_class(**reader_kwargs) for _ in range(num_readers)]
self._common_queue = common_queue