readers.py 文件源码

def prepare_reader(self, filename_queue, batch_size=1024):
        """Creates a single reader thread for pre-aggregated YouTube 8M Examples.

        Args:
          filename_queue: A tensorflow queue of filename locations.

        Returns:
          A tuple of video indexes, features, labels, and padding data.
        """
        reader = tf.TFRecordReader()
        _, serialized_examples = reader.read_up_to(filename_queue, batch_size)

        # set the mapping from the fields to data types in the proto
        num_features = len(self.feature_names)
        assert num_features > 0, "self.feature_names is empty!"
        assert len(self.feature_names) == len(self.feature_sizes), \
            "length of feature_names (={}) != length of feature_sizes (={})".format( \
                len(self.feature_names), len(self.feature_sizes))

        feature_map = {"video_id": tf.FixedLenFeature([], tf.string),
                       "predictions": tf.FixedLenFeature([self.num_classes], tf.float32),
                       "labels": tf.VarLenFeature(tf.int64)}

        features = tf.parse_example(serialized_examples, features=feature_map)

        return features["predictions"]