Load data from a TFRecords database and feed into a graph

load_and_feed_from_TFRecord.py

def decode(serialized_example):
    # Decode examples stored in TFRecord
    # NOTE: make sure to specify the correct dimensions for the images
    features = tf.parse_single_example(
        serialized_example,
        features={'train/image': tf.FixedLenFeature([128, 224, 224, 1], tf.float32),
                  'train/label': tf.FixedLenFeature([], tf.int64)})

    # NOTE: No need to cast these features, as they are already `tf.float32` values.
    return features['train/image'], features['train/label']

dataset = tf.data.TFRecordDataset(train_filename).map(decode)
dataset = dataset.repeat(None)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(1)

iterator = dataset.make_initializable_iterator()
features, labels = iterator.get_next()
nx = iterator.get_next()

with tf.train.MonitoredTrainingSession() as sess_rec:
    sess_rec.run(iterator.initializer)

    for i in range(iterations):
        try:
            # Get next features-labels pair
            rec_batch_feat, rec_batch_lbl = sess_rec.run([features, labels])

        except tf.errors.OutOfRangeError:
            pass