tf.contrib.learn.read_batch_features

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,
    num_enqueue_threads=2,
    parse_fn=None,
    name=None,
    read_batch_size=None
)

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

See the guide: Learn (contrib) > Input processing

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 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 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.
• 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. In order to have predictable and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, reader_num_threads should be 1.
• num_enqueue_threads: 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. In order to have predictable and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, num_enqueue_threads should be 1.
• parse_fn: Parsing function, takes Example Tensor returns parsed representation. If None, no parsing is done.
• name: Name of resulting op.
• read_batch_size: An int or scalar Tensor specifying the number of records to read at once. If None, defaults to batch_size.

Returns:

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

Raises:

• ValueError: for invalid inputs.