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

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

See the guide: Learn (contrib) > Input processing

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

Given file pattern (or list of files), will setup a queue for file names, 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 pattern 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() as shown in the tests.
  • queue_capacity: Capacity for input queue.
  • feature_queue_capacity: Capacity of the parsed features queue. Set this value to a small number, for example 5 if the parsed features are large.
  • reader_num_threads: The number of threads to read examples.
  • parse_fn: Parsing function, takes Example Tensor returns parsed representation. If None, no parsing is done.
  • name: Name of resulting op.

Returns:

A dict of Tensor or SparseTensor objects for each in features.

Raises:

  • ValueError: for invalid inputs.

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