evaluate.py 文件源码

def run():
  """Runs evaluation in a loop, and logs summaries to TensorBoard."""
  # Create the evaluation directory if it doesn't exist.
  eval_dir = FLAGS.eval_dir
  if not tf.gfile.IsDirectory(eval_dir):
    tf.logging.info("Creating eval directory: %s", eval_dir)
    tf.gfile.MakeDirs(eval_dir)

  g = tf.Graph()
  with g.as_default():
    # Build the model for evaluation.
    model_config = configuration.ModelConfig()
    model_config.input_file_pattern = FLAGS.input_file_pattern
    model = show_and_tell_model.ShowAndTellModel(model_config, mode="eval")
    model.build()

    # Create the Saver to restore model Variables.
    saver = tf.train.Saver()

    # Create the summary operation and the summary writer.
    summary_op = tf.merge_all_summaries()
    summary_writer = tf.train.SummaryWriter(eval_dir)

    g.finalize()

    # Run a new evaluation run every eval_interval_secs.
    while True:
      start = time.time()
      tf.logging.info("Starting evaluation at " + time.strftime(
          "%Y-%m-%d-%H:%M:%S", time.localtime()))
      run_once(model, saver, summary_writer, summary_op)
      time_to_next_eval = start + FLAGS.eval_interval_secs - time.time()
      if time_to_next_eval > 0:
        time.sleep(time_to_next_eval)