1acd2c4d48013ea1623ddbf9f28166a85404213a,models/experimental/inception/inception_v3.py,,main,#Any#,678

Before Change

          timeout=FLAGS.eval_timeout,
          timeout_fn=terminate_eval)

    for checkpoint in get_next_checkpoint():
      tf.logging.info("Starting to evaluate.")
      try:
        eval_results = inception_classifier.evaluate(
            input_fn=imagenet_eval.input_fn,
            steps=eval_steps,
            hooks=eval_hooks,
            checkpoint_path=checkpoint)
        tf.logging.info("Evaluation results: %s" % eval_results)
      except tf.errors.NotFoundError:
        // skip checkpoint if it gets deleted prior to evaluation
        tf.logging.info("Checkpoint %s no longer exists ... skipping")

  elif FLAGS.mode == "train_and_eval":
    for cycle in range(FLAGS.train_steps // FLAGS.train_steps_per_eval):
      tf.logging.info("Starting training cycle %d." % cycle)
      inception_classifier.train(

After Change

  if FLAGS.mode == "eval":
    // Run evaluation when there is a new checkpoint
    for checkpoint in evaluation.checkpoints_iterator(FLAGS.model_dir):
      tf.logging.info("Starting to evaluate.")
      try:
        start_timestamp = time.time()  // Includes compilation time
        eval_results = inception_classifier.evaluate(
            input_fn=imagenet_eval.input_fn,
            steps=eval_steps,
            hooks=eval_hooks,
            checkpoint_path=checkpoint)
        elapsed_time = int(time.time() - start_timestamp)
        tf.logging.info(
            "Eval results: %s. Elapsed seconds: %d", eval_results, elapsed_time)

        // Terminate eval job when final checkpoint is reached
        current_step = int(os.path.basename(checkpoint).split("-")[1])
        if current_step >= FLAGS.train_steps:
          tf.logging.info(
              "Evaluation finished after training step %d", current_step)
          break
      except tf.errors.NotFoundError:
        // Since the coordinator is on a different job than the TPU worker,
        // sometimes the TPU worker does not finish initializing until long after
        // the CPU job tells it to start evaluating. In this case, the checkpoint
        // file could have been deleted already.
        tf.logging.info(
            "Checkpoint %s no longer exists, skipping checkpoint", checkpoint)

  elif FLAGS.mode == "train_and_eval":
    for cycle in range(FLAGS.train_steps // FLAGS.train_steps_per_eval):
      tf.logging.info("Starting training cycle %d." % cycle)
      inception_classifier.train(

Instances