tutorial_tfrecord.py 文件源码

def read_and_decode(filename):
    # generate a queue with a given file name
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)     # return the file and the name of file
    features = tf.parse_single_example(serialized_example,  # see parse_single_sequence_example for sequence example
                                       features={
                                           'label': tf.FixedLenFeature([], tf.int64),
                                           'img_raw' : tf.FixedLenFeature([], tf.string),
                                       })
    # You can do more image distortion here for training data
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [224, 224, 3])
    # img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features['label'], tf.int32)
    return img, label