train_util.py 文件源码

def test(checkpoint_path, test_dir, examples_path, hparams,
         num_batches=None):
  """Evaluate the model at a single checkpoint."""
  tf.gfile.MakeDirs(test_dir)

  _trial_summary(hparams, examples_path, test_dir)
  with tf.Graph().as_default():
    transcription_data = _get_data(
        examples_path, hparams, is_training=False)
    unused_loss, losses, labels, predictions, images = model.get_model(
        transcription_data, hparams, is_training=False)

    metrics_to_values, metrics_to_updates = _get_eval_metrics(
        losses, labels, predictions, images, hparams)

    metric_values = slim.evaluation.evaluate_once(
        checkpoint_path=checkpoint_path,
        logdir=test_dir,
        num_evals=num_batches or transcription_data.num_batches,
        eval_op=metrics_to_updates.values(),
        final_op=metrics_to_values.values())

    metrics_to_values = dict(zip(metrics_to_values.keys(), metric_values))
    for metric in metrics_to_values:
      print('%s: %f' % (metric, metrics_to_values[metric]))