Tensorflow 1.10 TFRecordDataset - recovering TFRecords

Question

Notes:

1. this question extends upon a previous question of mine. In that question I ask about the best way to store some dummy data as Example an

Answer 1

Solved by updating the features to include shape information and remembering that SequenceExample are unnamed FeatureLists.

context_features = {
    'Name' : tf.FixedLenFeature([], dtype=tf.string),
    'Val_1': tf.FixedLenFeature([], dtype=tf.float32),
    'Val_2': tf.FixedLenFeature([], dtype=tf.float32)
}

sequence_features = {
    'sequence': tf.FixedLenSequenceFeature((3,), dtype=tf.int64),
    'pclasses'  : tf.FixedLenSequenceFeature((3,), dtype=tf.float32),
}

def parse(record):
  parsed = tf.parse_single_sequence_example(
        record,
        context_features=context_features,
        sequence_features=sequence_features
  )
  return parsed


filenames = [os.path.join(os.getcwd(),f"dummy_sequences_{i}.tfrecords") for i in range(3)]
dataset = tf.data.TFRecordDataset(filenames).map(lambda r: parse(r))

iterator = tf.data.Iterator.from_structure(dataset.output_types,
                                           dataset.output_shapes)
next_element = iterator.get_next()

training_init_op = iterator.make_initializer(dataset)

for _ in range(2):
  # Initialize an iterator over the training dataset.
  sess.run(training_init_op)
  for _ in range(3):
    ne = sess.run(next_element)
    print(ne)