Neural network accuracy improves on retraining without weight reinitialisation

Apologies as a similar question has been asked before, but it was never resolved. I am trying to create a neural network for use in a regression problem using nftool/nntool. I find that, on the first training run, the network sometimes performs quite poorly, but that with subsequent training runs regression accuracy seems to increase (pretty much with each successive run), although the weights have not been reinitialised. Why does this happen (answers in terms of the error surface and backpropogation would be illustrative though I don't need that much detail)? When the weights are not reinitialised, does each training run in MATLAB somehow 'build on' the previous?

Thanks

Answer 1

[Greg Heath]

A net with former weights will continue training from those weights.

If you wish to reinitialize to get an alternate design use the function configure at the top of the loop.

I have posted zillions of examples in both the NEWSGROUP (now only available in comp.soft-sys.matlab) and ANSWERS

Try searching

 greg configure

Hope this helps.

Thank you for formally accepting my answer

Greg

[Frederick Turner]

Thanks Greg, this is exactly what I was looking for!