Artificial Neural Networks - Question about hidden layers.

[Shaggy Hiker]

The site I found was using a GA (which is Genetic Algorithm) to train the ANN. I think the design was something along the lines of setting up the ANN and using the GA to evolve for a set of weights such that the ANN produced the correct results. In that case, the genes in the GA would be the weights in whichever sequence you wanted to try them in. This is a pretty clean design for a GA, and the one I posted in the CodeBank, with the caveats I noted about GA design, could be applied to the problem with relatively minor modification.

The link to that would be here, in case you want to see it.

http://www.vbforums.com/showthread.php?t=553187

[namrekka]

I used a dynamic learning rate value. How bigger the error how higher that value. Indeed be careful with those values. The input weights of the neurons can be pushed to far from their values. More then 1 hidden layer I couldn't get it stable. After ca 100 training runs with a certain character set it was able to read the alphabet (capitals) of that character set. However the probability between a "P" and "R" and a "I" and "J" and "1" for example was'n that good. But good enough to make a dession. Reading an other character set was however bad.
After training ca 10000 runs with different charater sets it worked amazing. Even with a test with a new character set the neurons didn't see before.

I didn't use a bias. The pixel values in my system was "1" for white and "0' for black. Was thinking the shift that value with a bias or with a bias directly to the pixel value to +/- 0.5, but Ididn't.

EDIT:
After finding the project again and reading the source I did use a bias input node with a value of -1