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

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.

file_pattern List of files or patterns of file paths containing Example records. See 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.compat.v1.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 tuple of:

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

ValueError for invalid inputs.