Hi Alp, - even with correctly programmed back-propagation, it is usually hard to make the net converge. - usually you initialize neuron weights with somewhat random values, when working with back-propagation. - do some debug prints of the net error while training to see how it is going - xor function cannot be trained with a single layer neural net !!! Cheers, Martin PS: I did not check the back-propagation algorithm itself. On Mon, Jun 15, 2009 at 9:58 AM, Alp Mestan <alp@mestan.fr> wrote:

Dear List,

I'm working with a friend of mine on a Neural Net library in Haskell.

There are 3 files : neuron.hs, layer.hs and net.hs. neuron.hs defines the Neuron data type and many utility functions, all of which have been tested and work well. layer.hs defines layer-level functions (computing the output of a whole layer of neurons, etc). Tested and working. net.hs defines net-level functions (computing the output of a whole neural net) and the famous -- but annoying -- back-propagation algorithm.

You can find them there : http://mestan.fr/haskell/nn/html/

The problem is that here when I ask for final_net or test_output (anything after the train call, in net.hs), it seems to loop and loop around, as if it never gets the error under 0.1.

So I was just wondering if there was one or more Neural Nets and Haskell wizard in there to check the back-propagation implementation, given in net.hs, that seems to be wrong.

Thanks a lot !

-- Alp Mestan

_______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe