parameters: (sess,
              eval_correct,
              images_placeholder,
              labels_placeholder,
              data_set)
docstring: Runs one evaluation against the full epoch of data.
  
    Args:
      sess: The session in which the model has been trained.
      eval_correct: The Tensor that returns the number of correct predictions.
      images_placeholder: The images placeholder.
      labels_placeholder: The labels placeholder.
      data_set: The set of images and labels to evaluate, from
        input_data.read_data_sets().
docstring_summary: Runs one evaluation against the full epoch of data.
docstring_tokens: Runs one evaluation against the full epoch of data .
function: def do_eval(sess,
              eval_correct,
              images_placeholder,
              labels_placeholder,
              data_set):
    """Runs one evaluation against the full epoch of data.
  
    Args:
      sess: The session in which the model has been trained.
      eval_correct: The Tensor that returns the number of correct predictions.
      images_placeholder: The images placeholder.
      labels_placeholder: The labels placeholder.
      data_set: The set of images and labels to evaluate, from
        input_data.read_data_sets().
    """
    # And run one epoch of eval.
    true_count = 0  # Counts the number of correct predictions.
    steps_per_epoch = data_set.num_examples // FLAGS.batch_size
    num_examples = steps_per_epoch * FLAGS.batch_size
    for step in xrange(steps_per_epoch):
      feed_dict = fill_feed_dict(data_set,
                                 images_placeholder,
                                 labels_placeholder)
      true_count += sess.run(eval_correct, feed_dict=feed_dict)
    precision = true_count / num_examples
    print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
          (num_examples, true_count, precision))
function_tokens: def do_eval ( sess , eval_correct , images_placeholder , labels_placeholder , data_set ) : # And run one epoch of eval. true_count = 0 # Counts the number of correct predictions. steps_per_epoch = data_set . num_examples // FLAGS . batch_size num_examples = steps_per_epoch * FLAGS . batch_size for step in xrange ( steps_per_epoch ) : feed_dict = fill_feed_dict ( data_set , images_placeholder , labels_placeholder ) true_count += sess . run ( eval_correct , feed_dict = feed_dict ) precision = true_count / num_examples print ( '  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' % ( num_examples , true_count , precision ) )