3 ms·
As a non mathematician, this is non obvious to me. Thinking about it a bit (I haven't finished reading the article yet)... Since the size of the hidden layer i
by dicroce 11y ago
As a non mathematician, this is non obvious to me.
Thinking about it a bit (I haven't finished reading the article yet)... Since the size of the hidden layer isn't specified, I suppose you could have a hidden layer node for every possible input... So, of course any function is computable with a neural network. Really the magical thing here is finding the smallest set of nodes that computes the function...
- 49531 11y agoI've been toying with a NN trying to get it to play 2048 based on game data I recorded. I still have about 60% error rate, but I found that with 16 inputs (the tiles on the game), and 4 outputs (directions to move), it works best like a funnel. I currently have 2 hidden layers, of 12 and 8 and it's the best I've gotten so far.
- gizmo686 11y agoThat is essentially correct. The subtlety is that the hidden layer still has a finite (but arbitrarily large) number of nodes, while there are an infinite number of inputs. The solution to this is that you can keep adding nodes until the space between the nodes is close enough to the function you are modelling.