Neural Network Always Produces Same/Similar Outputs for Any Input

Question

I have a problem where I am trying to create a neural network for Tic-Tac-Toe. However, for some reason, training the neural network causes it to produce nearly the same out

Answer 1

I've had similar problems, but was able to solve by changing these:

• Scale down the problem to manageable size. I first tried too many inputs, with too many hidden layer units. Once I scaled down the problem, I could see if the solution to the smaller problem was working. This also works because when it's scaled down, the times to compute the weights drop down significantly, so I can try many different things without waiting.
• Make sure you have enough hidden units. This was a major problem for me. I had about 900 inputs connecting to ~10 units in the hidden layer. This was way too small to quickly converge. But also became very slow if I added additional units. Scaling down the number of inputs helped a lot.
• Change the activation function and its parameters. I was using tanh at first. I tried other functions: sigmoid, normalized sigmoid, Gaussian, etc.. I also found that changing the function parameters to make the functions steeper or shallower affected how quickly the network converged.
• Change learning algorithm parameters. Try different learning rates (0.01 to 0.9). Also try different momentum parameters, if your algo supports it (0.1 to 0.9).

Hope this helps those who find this thread on Google!