64171b3cdf5f0fca6bb11e48831946db63263684,tests/attacks/evasion/test_fast_gradient.py,,test_minimal_perturbations_images,#Any#Any#,99

Before Change

        utils_test.assert_almost_equal_mean(x_test_mnist, x_test_adv, 0.03896513, decimal=0.01)
        utils_test.assert_almost_equal_min(x_test_mnist, x_test_adv, -0.30000000, decimal=0.00001)
        utils_test.assert_almost_equal_max(x_test_mnist, x_test_adv, 0.30000000, decimal=0.00001)

        y_test_pred = classifier.predict(x_test_adv)
        tmp = np.argmax(y_test_pred, axis=1)
        y_test_pred_expected = np.asarray([4, 2, 4, 7, 0, 4, 7, 2, 0, 7, 0])

After Change

        attack_params = {"minimal": True, "eps_step": 0.1, "eps": 5.0}
        attack.set_params(**attack_params)

        expected_values = {"x_test_mean": ExpectedValue(0.03896513, 0.01),
                           "x_test_min": ExpectedValue(-0.30000000, 0.00001),
                           "x_test_max": ExpectedValue(0.30000000, 0.00001),
                           "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 2, 4, 7, 0, 4, 7, 2, 0, 7, 0]), 2)}
        utils_attack._backend_norm_images(attack, classifier, fix_get_mnist_subset, expected_values)


@pytest.mark.parametrize("norm", [np.inf, 1, 2])

Instances