import tensorflow as tf


            # Local logits
            (predict_NOR, predict_ADV, logits_part_nor, logits_part_adv) = sess.run(
                [predict_nor, predict_adv, tsne_logit_nor, tsne_logit_adv],
                feed_dict={image: nor_img, adv_image: adv_img}
            )

            # Local entropy and confidence for nor_img
            (entropy_test_nor_help, labels_nor_help, confidence_test_nor_help) = sess.run(
                [entropy, tf.argmax(predict, axis=1), tf.reduce_max(predict, axis=1)], feed_dict={predict: predict_NOR}
            )

            # Local entropy and confidence for adv_img
            (entropy_test_adv_help, labels_adv_help, confidence_test_adv_help) = sess.run(
                [entropy, tf.argmax(predict, axis=1), tf.reduce_max(predict, axis=1)], feed_dict={predict: predict_ADV}
            )

            entropy_test_adv_all = np.concatenate((entropy_test_adv_all, entropy_test_adv_help), axis=0)
            confidence_test_adv_all = np.concatenate((confidence_test_adv_all, confidence_test_adv_help), axis=0)
            entropy_test_nor_all = np.concatenate((entropy_test_nor_all, entropy_test_nor_help), axis=0)
            confidence_test_nor_all = np.concatenate((confidence_test_nor_all, confidence_test_nor_help), axis=0)
            logits_nor_all = np.concatenate((logits_nor_all, logits_part_nor), axis=0)
            labels_nor_all = np.concatenate((labels_nor_all, labels_nor_help), axis=0)
            logits_adv_all = np.concatenate((logits_adv_all, logits_part_adv), axis=0)
            labels_adv_all = np.concatenate((labels_adv_all, labels_adv_help), axis=0)
            labels_true_all = np.concatenate((labels_true_all, np.argmax(true_label, axis=1)), axis=0)
            L2_distance = np.concatenate((L2_distance,np.sqrt(np.mean(np.square(nor_img-adv_img),axis=(1,2,3)))), axis=0)
            nor_img_all = np.concatenate((nor_img_all, nor_img), axis=0)
