If you did some of comparison on data1 and data2, then I would agree. But if the way to determine the answer was so easy, we wouldn’t need deep learning for this.
One or more optimizers doesn’t really matter in this case. The rationale for multiple optimizers is really to have different hyperparameters for each network (learning rate namely).
In fact, in my example I depicted the problem as utilizing two separate networks, but there’s no reason why you couldn’t just shove it all under one network instead.