ab69a2253b4229081850280c3346e7a7b1e1815c,art/wrappers/randomized_smoothing.py,RandomizedSmoothing,class_gradient,#RandomizedSmoothing#Any#Any#Any#,122

Before Change

            if label is None:
                x_new = self._noisy_samples(x_i)
            else:
                x_new, label = self._noisy_samples(x_i, label[idx])
            class_gradient.append(np.mean(self.classifier.class_gradient(x_new, label, logits), axis=0))
        return np.array(class_gradient)

    def certify(self, x, n):

After Change

        :rtype: `np.ndarray`
        
        logger.info("Apply randomized smoothing.")
        return self.classifier.class_gradient(x, label, logits)

    def certify(self, x, n):
        
        Computes certifiable radius around input `x` and returns radius `r` and prediction.

Instances