If you wanted 100% accuracy, technically infinite data points and training time would be required. You could likely get >99% accuracy within a few epochs of tra

If you wanted 100% accuracy, technically infinite data points and training time would be required. You could likely get >99% accuracy within a few epochs of training though. With such a simple function you're trying to emulate, your model will very quickly converge. Neural networks are much better suited for distilling down and compressing very complex high dimensional data though anyways, and you really don't need to be using them for problems like this. It's completely overkill in addition to being very computationally inefficient. There's nothing wrong with just simply using linear regression. In many cases it's the right choice. In your toy problem case you coded above, you are effectively just doing linear regression, except you added in an Adam gradient descent optimizer instead of just doing least squares, which by the way would have been infinitely faster and immediately given you an answer.