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Reads TFRecord, queues, batches and parses Example
proto. (deprecated)
tf.contrib.learn.read_batch_record_features(
file_pattern,
batch_size,
features,
randomize_input=True,
num_epochs=None,
queue_capacity=10000,
reader_num_threads=1,
name='dequeue_record_examples'
)
See more detailed description in read_examples
.
Args:
file_pattern
: List of files or patterns of file paths containingExample
records. Seetf.io.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.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. 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.name
: Name of resulting op.
Returns:
A dict of Tensor
or SparseTensor
objects for each in features
.
Raises:
ValueError
: for invalid inputs.