readfromtfrecord_batch.py 文件源码

def read_and_decode_single_example(filename_queue):

    # Unlike the TFRecordWriter, the TFRecordReader is symbolic
    reader = tf.TFRecordReader()
    # One can read a single serialized example from a filename
    # serialized_example is a Tensor of type string.
    _, serialized_example = reader.read(filename_queue)
    # The serialized example is converted back to actual values.
    # One needs to describe the format of the objects to be returned
    features = tf.parse_single_example(
        serialized_example,
        features={
            # We know the length of both fields. If not the
            # tf.VarLenFeature could be used
            'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
            'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
            'image/class/label': tf.FixedLenFeature(
                [], tf.int64, default_value=tf.zeros([], dtype=tf.int64))
        })
    # now return the converted data
    label = features['image/class/label']
    image = features['image/encoded']

#     image = tf.image.decode_jpeg(image, channels=3)
    image_format = features['image/format']



    return label, image, image_format