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### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, read_batch_size=1, name=None)` {#read_batch_features}
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:
* <b>`file_pattern`</b>: List of files or pattern of file paths containing
`Example` records. See `tf.gfile.Glob` for pattern rules.
* <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
* <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
`VarLenFeature` values.
* <b>`reader`</b>: A function or class that returns an object with
`read` method, (filename tensor) -> (example tensor).
* <b>`randomize_input`</b>: Whether the input should be randomized.
* <b>`num_epochs`</b>: 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.initialize_local_variables() as shown in the tests.
* <b>`queue_capacity`</b>: Capacity for input queue.
* <b>`reader_num_threads`</b>: The number of threads to read examples.
* <b>`parser_num_threads`</b>: The number of threads to parse examples.
* <b>`read_batch_size`</b>: An int or scalar `Tensor` specifying the number of
records to read at once
* <b>`name`</b>: Name of resulting op.
##### Returns:
A dict of `Tensor` or `SparseTensor` objects for each in `features`.
If `keep_keys` is `True`, returns tuple of string `Tensor` and above dict.
##### Raises:
* <b>`ValueError`</b>: for invalid inputs.
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