def evaluation(logits, labels):
  """Evaluate the quality of the logits at predicting the label.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).
  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
  print('Evaluation..')
  # For a classifier model, we can use the in_top_k Op.
  # It returns a bool tensor with shape [batch_size] that is true for
  # the examples where the label's is was in the top k (here k=1)
  # of all logits for that example.
  correct = tf.nn.in_top_k(logits, labels, 1)
  num_correct = tf.reduce_sum(tf.cast(correct, tf.float32))

  acc_percent = num_correct / FLAGS.batch_size

  # Return the number of true entries.
  return acc_percent * 100.0, num_correct
dataset/ETHPy150Open HamedMP/ImageFlow/example_project/my_cifar.py/evaluation/original