a.py

def main():
  
  # Read TFRecords files for training
  def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    return serialized_example

  # Read TFRecords files for training
  filename_queue = tf.train.string_input_producer(
      tf.train.match_filenames_once(FLAGS.train_tfrecords_file),
      num_epochs=EPOCH_NUMBER)
  serialized_example = read_and_decode(filename_queue)
  batch_serialized_example = tf.train.shuffle_batch(
      [serialized_example],
      batch_size=FLAGS.batch_size,
      num_threads=BATCH_THREAD_NUMBER,
      capacity=BATCH_CAPACITY,
      min_after_dequeue=MIN_AFTER_DEQUEUE)
  features = tf.parse_example(batch_serialized_example,
                              features={
                                  "label": tf.FixedLenFeature([], tf.float32),
                                  "ids": tf.VarLenFeature(tf.int64),
                                  "values": tf.VarLenFeature(tf.float32),
                              })
  batch_labels = features["label"]
  batch_ids = features["ids"]
  batch_values = features["values"]

 
  # Create session to run
  with tf.Session() as sess:
    logging.info("Start to run with mode: {}".format(MODE))
    writer = tf.summary.FileWriter(OUTPUT_PATH, sess.graph)
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer())

    if MODE == "train":
      # Restore session and start queue runner
      restore_session_from_checkpoint(sess, saver, LATEST_CHECKPOINT)
      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(coord=coord, sess=sess)
      start_time = datetime.datetime.now()
      try:
        while not coord.should_stop():
          _, loss_value, step = sess.run([train_op, loss, global_step])
      except tf.errors.OutOfRangeError:
        # Export the model after training
        export_model(sess, saver, model_signature, FLAGS.model_path,
                     FLAGS.model_version)
      finally:
        coord.request_stop()
      coord.join(threads)



def restore_session_from_checkpoint(sess, saver, checkpoint):
  if checkpoint:
    logging.info("Restore session from checkpoint: {}".format(checkpoint))
    saver.restore(sess, checkpoint)
    return True
  else:
    return False