Neural networks

On September 17 2010 23:33 Glacierz wrote:
Based on your questions, I suggest you start out with Bayesian networks first before getting into neural networks.

On September 17 2010 23:16 Qzy wrote:
Thanks for already answering.

Okay here's a few questions.

I get the basics of it - but ...

How many neurons should you use, with 2 inputs? Do you HAVE to use 2 neurons in the hidden layer, or can you simple use 1? Can you use 5? What's the benefit for using less/more?

You can have more hidden layers - but with what benefits? Should it have the same amount of neurons as the other hidden layers?

When teaching the NN how to play, for instance, tictactoe, do I give it training examples: This is the input, i expect this output...? How many examples does it need to adjust to play decent?

Can it generalize, once it has seen a few examples?

On September 17 2010 23:55 Cambium wrote:


It's been a while since I took ML, so I don't remember too much about NN, I'll give it a shot

You should have at least N+1 nodes in each layer where N is your # of input nodes. You can, of course, build a layer with any number of nodes, you just won't reach the accuracy you desire. I remember at one point, this made intuitive sense to me, but I don't remember it well enough to describe it back to you. There is no hard restriction on the number of neurons in each layer, this is something you have to experiment by running the NN multiple times wrt your training data, and choose the one with the highest accuracy. You can also do this with any number of hidden layers. The reason why you shouldn't use excessive number of neurons and hidden layers is to avoid overfitting (I think...).

Each layer can have a different number of nodes, and the optimal number of layers depend largely on your input data and your activation function (such as gradient descent, sigmoid). Too few nodes cause underfitting, and too many nodes cause overfitting (again, I think...).

Tic-Tac-Toe is actually a difficult problem to solve with NN (I'd use decision tree actually) since it's adaptive Your first task would be to digitize all of the moves in a given game (so every game is one piece of training datum), and the output would be win, lose and tie. Alternatively, you can assign state a value (much like Chess) so that every move can be a row in your training data.

I hope this helps.

On September 18 2010 00:02 Qzy wrote:


Thanks it clears it up a bit..

Right now, I have to implement a ludo player - with a lot of possible states (like chess). Do i simply give it a few examples (inputs, and expected output), and it can generalize from these examples, once properly trained (output has reached desired)?

On September 18 2010 00:09 Cambium wrote:


Well, you first need to classify your inputs and outputs. In a game of tic-tac-toe, say you are red, it would be along the lines of, how many red on each line (attack), how many blacks on each line (defence), and maybe a few more. Your output would be the quantification of the state after you place your piece. If you win or prevent a loss, it would probably be max, and go from there.

You would need a lot more data than a "few lines"; I would think in the order of hundreds if not thousands. I would try to find existing data for tic-tac-toe and see how experts classified the game. The best way to obtain data is to either find it, or to create an online version and ask your friends to play so that you can classify their respective inputs and outputs.

On September 18 2010 00:09 Cambium wrote:


Well, you first need to classify your inputs and outputs. In a game of tic-tac-toe, say you are red, it would be along the lines of, how many red on each line (attack), how many blacks on each line (defence), and maybe a few more. Your output would be the quantification of the state after you place your piece. If you win or prevent a loss, it would probably be max, and go from there.

You would need a lot more data than a "few lines"; I would think in the order of hundreds if not thousands. I would try to find existing data for tic-tac-toe and see how experts classified the game. The best way to obtain data is to either find it, or to create an online version and ask your friends to play so that you can classify their respective inputs and outputs.

On September 18 2010 01:03 Glacierz wrote:
Tic-tac-toe is easily solved by alpha-beta pruning, no need for complex frameworks like NN

On September 18 2010 01:11 ToxNub wrote:
I've written a few neural networks by hand before. I don't really understand how (why) you would get a neural network to play tic-tac-toe tho. NNs are supervised learning algorithms, which means you need to already know the answer in advance. Your network just sorta "remembers" the answers you've trained it to remember. Granted, if you get enough data, you can provide a novel input and you MIGHT get novel output, but for your purposes you will likely just be "remembering" what to do given gamestate a, b, c... Boring. Not an interesting application of NN at all :p

When people used NNs to play chess or whatever, it's really not just a NN. The NN in those games is really just a function approximator that tells you whether or not a given gamestate (board) is "good". Both chess, backgammon, and tic-tac-toe all rely on sequential moves, which means you've added a temporal element. ML techniques like temporal difference learning have successfully been applied in the form of TD-gammon, TD-Chess (and probably TD-tic-tac-toe) and are more suited to your needs.