tf.contrib.learn.read_keyed_batch_features(
file_pattern,
batch_size,
features,
reader,
randomize_input=True,
num_epochs=None,
queue_capacity=10000,
reader_num_threads=1,
feature_queue_capacity=100,
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
.
Adds operations to read, queue, batch and parse Example
protos. (deprecated)
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 containingExample
records. Seetf.gfile.Glob
for pattern rules.batch_size
: An int or scalarTensor
specifying the batch size to use.features
: Adict
mapping feature keys toFixedLenFeature
orVarLenFeature
values.reader
: A function or class that returns an object withread
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.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.feature_queue_capacity
: Capacity of the parsed features queue.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, takesExample
Tensor returns parsed representation. IfNone
, no parsing is done.name
: Name of resulting op.read_batch_size
: An int or scalarTensor
specifying the number of records to read at once. IfNone
, defaults tobatch_size
.
Returns:
Returns tuple of:
- Tensor
of string keys.
- A dict of Tensor
or SparseTensor
objects for each in features
.
Raises:
ValueError
: for invalid inputs.