4fb315c20c8c7e0a346e3b842e91448274224d58,tests/defences/test_adversarial_trainer.py,TestAdversarialTrainer,test_two_attacks,#TestAdversarialTrainer#,185

Before Change

        // No reason to assert the newer accuracy is higher. It might go down slightly
        self.assertGreaterEqual(acc_new, acc * ACCURACY_DROP)

        logger.info("Accuracy before adversarial training: %.2f%%", (acc * 100))
        logger.info("\nAccuracy after adversarial training: %.2f%%", (acc_new * 100))

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

After Change

        accuracy_new = np.sum(predictions_new == np.argmax(y_test, axis=1)) / NB_TEST
        self.assertGreaterEqual(accuracy_new, accuracy)

        self.assertEqual(accuracy_new, 0.18181818181818182)
        self.assertEqual(accuracy, 0.18181818181818182)

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

Instances