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

For me it was happening exactly like in your case, the output of neural network was always the same no matter the training & number of layers etc.

Turns out my back-propagation algorithm had a problem. At one place I was multiplying by -1 where it wasn't required.

There could be another problem like this. The question is how to debug it?

Steps to debug:

Step1 : Write the algorithm such that it can take variable number of input layers and variable number of input & output nodes.
Step2 : Reduce the hidden layers to 0. Reduce input to 2 nodes, output to 1 node.
Step3 : Now train for binary-OR-Operation.
Step4 : If it converges correctly, go to Step 8.
Step5 : If it doesn't converge, train it only for 1 training sample
Step6 : Print all the forward and prognostication variables (weights, node-outputs, deltas etc)
Step7 : Take pen&paper and calculate all the variables manually.
Step8 : Cross verify the values with algorithm.
Step9 : If you don't find any problem with 0 hidden layers. Increase hidden layer size to 1. Repeat step 5,6,7,8

It sounds like a lot of work, but it works very well IMHO.