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.
  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():
    images, input_seqs, target_seqs, input_mask = Build_Inputs(mode, input_file_pattern)
    net_image_embeddings = Build_Image_Embeddings(mode, images, train_inception)
    net_seq_embeddings = Build_Seq_Embeddings(input_seqs)
    _, target_cross_entropy_losses, target_cross_entropy_loss_weights, network = \
            Build_Model(mode, net_image_embeddings, net_seq_embeddings, target_seqs, input_mask)

    global_step = tf.Variable(
        initial_value=0,
        dtype=tf.int32,
        name="global_step",
        trainable=False,
        collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.VARIABLES])

    # 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(global_step, target_cross_entropy_losses,
                        target_cross_entropy_loss_weights,
                        saver, summary_writer,
                        summary_op)
      time_to_next_eval = start + eval_interval_secs - time.time()
      if time_to_next_eval > 0:
        time.sleep(time_to_next_eval)