b2892275c7365da5df5ea96cbef587ea1ba6407e,art/utils.py,,compute_success,#Any#Any#Any#Any#Any#Any#,287

Before Change

    :return: Percentage of successful adversarial samples.
    :rtype: `float`
    
    adv_preds = np.argmax(classifier.predict(x_adv, batch_size=batch_size), axis=1)
    if targeted:
        rate = np.sum(adv_preds == np.argmax(labels, axis=1)) / x_adv.shape[0]
    else:
        preds = np.argmax(classifier.predict(x_clean, batch_size=batch_size), axis=1)
        rate = np.sum(adv_preds != preds) / x_adv.shape[0]

    return rate


def compute_accuracy(preds, labels, abstain=True):
    

After Change

    :return: Percentage of successful adversarial samples.
    :rtype: `float`
    
    attack_success = compute_success_array(classifier, x_clean, labels, x_adv, targeted, batch_size)
    return np.sum(attack_success) / x_adv.shape[0]


def compute_accuracy(preds, labels, abstain=True):
    

Instances