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')

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 the guide: Learn (contrib) > Input processing

Reads TFRecord, queues, batches and parses Example proto.

See more detailed description in read_examples.

Args:

  • file_pattern: List of files or pattern of file paths containing Example records. See tf.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.local_variables_initializer() as shown in the tests.
  • queue_capacity: Capacity for input queue.
  • reader_num_threads: The number of threads to read examples.
  • name: Name of resulting op.

Returns:

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

Raises:

  • ValueError: for invalid inputs.

Defined in tensorflow/contrib/learn/python/learn/learn_io/graph_io.py.