def test_5_keras_mnist(self):
        """
        Second test with the KerasClassifier.
        :return:
        """
        x_test_original = self.x_test_mnist.copy()

        # Build KerasClassifier
        krc = get_image_classifier_kr()

        # Targeted attack
        # zoo = ZooAttack(classifier=krc, targeted=True, batch_size=5, verbose=False)
        # params = {'y': random_targets(self.y_test, krc.nb_classes)}
        # x_test_adv = zoo.generate(self.x_test, **params)
        #
        # self.assertFalse((self.x_test == x_test_adv).all())
        # self.assertLessEqual(np.amax(x_test_adv), 1.0)
        # self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
        # target = np.argmax(params['y'], axis=1)
        # y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
        # logger.debug('ZOO target: %s', target)
        # logger.debug('ZOO actual: %s', y_pred_adv)
        # logger.info('ZOO success rate on MNIST: %.2f', (sum(target == y_pred_adv) / float(len(target))))

        # Untargeted attack
        # zoo = ZooAttack(classifier=krc, targeted=False, max_iter=20, verbose=False)
        zoo = ZooAttack(classifier=krc, targeted=False, batch_size=5, max_iter=10, binary_search_steps=3, verbose=False)
        # x_test_adv = zoo.generate(x_test)
        params = {"y": random_targets(self.y_test_mnist, krc.nb_classes)}
        x_test_mnist_adv = zoo.generate(self.x_test_mnist, **params)

        # x_test_adv_true = [0.00000000e+00, 2.50167388e-04, 1.50529508e-04, 4.69674182e-04,
        #                    0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
        #                    1.67321396e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
        #                    0.00000000e+00, 2.08451956e-06, 0.00000000e+00, 0.00000000e+00,
        #                    2.53360748e-01, 9.60119188e-01, 9.85227525e-01, 2.53600776e-01,
        #                    0.00000000e+00, 0.00000000e+00, 5.23251540e-04, 0.00000000e+00,
        #                    0.00000000e+00, 0.00000000e+00, 1.08632184e-05, 0.00000000e+00]
        #
        # for i in range(14):
        #     self.assertAlmostEqual(x_test_adv_true[i], x_test_adv[0, 14, i, 0])

        # self.assertFalse((x_test == x_test_adv).all())
        self.assertLessEqual(np.amax(x_test_mnist_adv), 1.0)
        self.assertGreaterEqual(np.amin(x_test_mnist_adv), 0.0)
        y_pred_adv = np.argmax(krc.predict(x_test_mnist_adv), axis=1)
        y_pred = np.argmax(krc.predict(self.x_test_mnist), axis=1)
        logger.debug("ZOO actual: %s", y_pred_adv)
        logger.info("ZOO success rate on MNIST: %.2f", (sum(y_pred != y_pred_adv) / float(len(y_pred))))

        # Check that x_test has not been modified by attack and classifier
        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)

        # Clean-up
        k.clear_session()

        Second test with the KerasClassifier.
        :return:
        
tests/attacks/test_zoo.py