deepfool.py 文件源码

def _deepfool2(model, x, epochs, eta, clip_min, clip_max, min_prob):
    y0 = tf.stop_gradient(tf.reshape(model(x), [-1])[0])
    y0 = tf.to_int32(tf.greater(y0, 0.5))

    def _cond(i, z):
        xadv = tf.clip_by_value(x + z*(1+eta), clip_min, clip_max)
        y = tf.stop_gradient(tf.reshape(model(xadv), [-1])[0])
        y = tf.to_int32(tf.greater(y, 0.5))
        return tf.logical_and(tf.less(i, epochs), tf.equal(y0, y))

    def _body(i, z):
        xadv = tf.clip_by_value(x + z*(1+eta), clip_min, clip_max)
        y = tf.reshape(model(xadv), [-1])[0]
        g = tf.gradients(y, xadv)[0]
        dx = - y * g / tf.norm(g)
        return i+1, z+dx

    _, noise = tf.while_loop(_cond, _body, [0, tf.zeros_like(x)],
                             name='_deepfool2_impl', back_prop=False)
    return noise