tf.contrib.learn.read_keyed_batch_features_shared_queue

tf.contrib.learn.read_keyed_batch_features_shared_queue(
    file_pattern,
    batch_size,
    features,
    reader,
    randomize_input=True,
    num_epochs=None,
    queue_capacity=10000,
    reader_num_threads=1,
    feature_queue_capacity=100,
    num_queue_runners=2,
    parse_fn=None,
    name=None
)

Defined in tensorflow/contrib/learn/python/learn/learn_io/graph_io.py.

Adds operations to read, queue, batch and parse Example protos. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.data.

Given file pattern (or list of files), will setup a shared queue for file names, setup a worker queue that gets filenames from the shared queue, read Example proto using provided reader, use batch queue to create batches of examples of size batch_size and parse example given features specification.

All queue runners are added to the queue runners collection, and may be started via start_queue_runners.

All ops are added to the default graph.

Args:

  • file_pattern: List of files or patterns of file paths containing Example records. See tf.gfile.Glob for pattern rules.
  • batch_size: An int or scalar Tensor specifying the batch size to use.
  • features: A dict mapping feature keys to FixedLenFeature or VarLenFeature values.
  • reader: A function or class that returns an object with read method, (filename tensor) -> (example tensor).
  • randomize_input: Whether the input should be randomized.
  • num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. NOTE - If specified, creates a variable that must be initialized, so call tf.local_variables_initializer() and run the op in a session.
  • queue_capacity: Capacity for input queue.
  • reader_num_threads: The number of threads to read examples.
  • feature_queue_capacity: Capacity of the parsed features queue.
  • num_queue_runners: Number of threads to enqueue the parsed example queue. Using multiple threads to enqueue the parsed example queue helps maintain a full queue when the subsequent computations overall are cheaper than parsing.
  • parse_fn: Parsing function, takes Example Tensor returns parsed representation. If None, no parsing is done.
  • name: Name of resulting op.

Returns:

Returns tuple of: - Tensor of string keys. - A dict of Tensor or SparseTensor objects for each in features.

Raises:

  • ValueError: for invalid inputs.