02_tfrecord_example.py 文件源码

def read_from_tfrecord(filenames):
    tfrecord_file_queue = tf.train.string_input_producer(filenames, name='queue')
    reader = tf.TFRecordReader()
    _, tfrecord_serialized = reader.read(tfrecord_file_queue)

    # label and image are stored as bytes but could be stored as 
    # int64 or float64 values in a serialized tf.Example protobuf.
    tfrecord_features = tf.parse_single_example(tfrecord_serialized,
                        features={
                            'label': tf.FixedLenFeature([], tf.int64),
                            'shape': tf.FixedLenFeature([], tf.string),
                            'image': tf.FixedLenFeature([], tf.string),
                        }, name='features')
    # image was saved as uint8, so we have to decode as uint8.
    image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
    shape = tf.decode_raw(tfrecord_features['shape'], tf.int32)
    # the image tensor is flattened out, so we have to reconstruct the shape
    image = tf.reshape(image, shape)
    label = tfrecord_features['label']
    return label, shape, image