6a00f3e4b8cb38477ab2985c6a5657f1edcff971,tests_tf/test_mnist_tutorial_jsma.py,TestMNISTTutorialJSMA,test_mnist_tutorial_jsma,#TestMNISTTutorialJSMA#,6

Before Change

        np.random.seed(42)
        import tensorflow as tf
        tf.set_random_seed(42)

        from cleverhans_tutorials import mnist_tutorial_jsma

        // Run the MNIST tutorial on a dataset of reduced size

After Change

                              "viz_enabled": False,
                              "source_samples": 1,
                              "nb_epochs": 2}
        g = tf.Graph()
        with g.as_default():
            np.random.seed(42)
            report = mnist_tutorial_jsma.mnist_tutorial_jsma(**jsma_tutorial_args)

        // Check accuracy values contained in the AccuracyReport object
        // We already have JSMA tests in test_attacks.py, so just sanity
        // check the values here.
        self.assertTrue(report.clean_train_clean_eval > 0.65)
        self.assertTrue(report.clean_train_adv_eval < 0.25)

        // There is no adversarial training in the JSMA tutorial
        self.assertTrue(report.adv_train_clean_eval == 0.)
        self.assertTrue(report.adv_train_adv_eval == 0.)

        g = tf.Graph()
        with g.as_default():
            np.random.seed(42)
            report_2 = mnist_tutorial_jsma.mnist_tutorial_jsma(**jsma_tutorial_args)

        atol_fac = 1e-6
        self.assertClose(report.train_clean_train_clean_eval,
                         report_2.train_clean_train_clean_eval,
                         atol=atol_fac * 1)

Instances