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
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
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