tf.contrib.learn.read_batch_record_features

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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.

file_pattern List of files or patterns of file paths containing Example records. See tf.io.gfile.glob 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.
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.

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

ValueError for invalid inputs.