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 )
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,
Example proto using provided
reader, use batch queue to create
batches of examples of size
batch_size and parse example given
All queue runners are added to the queue runners collection, and may be
All ops are added to the default graph.
file_pattern: List of files or patterns of file paths containing
tf.gfile.Globfor pattern rules.
batch_size: An int or scalar
Tensorspecifying the batch size to use.
dictmapping feature keys to
reader: A function or class that returns an object with
readmethod, (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_threadsshould 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_threadsshould be 1.
parse_fn: Parsing function, takes
ExampleTensor returns parsed representation. If
None, no parsing is done.
name: Name of resulting op.
read_batch_size: An int or scalar
Tensorspecifying the number of records to read at once. If
None, defaults to
A dict of
SparseTensor objects for each in
ValueError: for invalid inputs.