# 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)