a4376fc2e3c1eec67decf36bc0a1771ad17a771e,src/attacks/newtonfool_unittest.py,TestNewtonFool,test_krclassifier,#TestNewtonFool#,71

Before Change

        print(krc.predict(x_test, logits=True))
        grads = krc.class_gradient(x_test, logits=False)
        import numpy as np
        print("min is: ", np.min(grads), ", max is: ", np.max(grads))

        self.assertFalse(True)
//         // Attack
//         nf = NewtonFool(krc)

After Change

        // Attack
        nf = NewtonFool(krc)
        nf.set_params(max_iter=5)
        x_test_adv = nf.generate(x_test)
        self.assertFalse((x_test == x_test_adv).all())

        y_pred = krc.predict(x_test)

Instances