[Top][All Lists]
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: [Bug-gnubg] User training of the Neural Nets
From: |
Øystein Johansen |
Subject: |
Re: [Bug-gnubg] User training of the Neural Nets |
Date: |
Wed, 23 Aug 2006 20:42:30 +0200 |
User-agent: |
Mozilla Thunderbird 1.0.6 (Windows/20050716) |
Christian Anthon wrote:
> Okay then what level of programming skills would be needed and what
> should a programmer do?
1. Get the gnubg-nn code
cvs -d:something:blah co gnubg-nn
2. make it compile
If I remeber correctly it was:
cd py
gmake safe
I believe there is some documentation on how to compile it.
(Maybe a linux system makes it easier ?)
3. Before you start training anything:
Steal the neural net evaluation code from gnubg, the code
that uses SSE, and apply it to the code ing gnubg-nn.
This step will save you a lot of time in the traing.
(commit the changes back to the cvs)
4. Get the reference databases and the neural nets. It's on
a ftp somewhere. I'll find the address when you need it.
5. Use the training scrips provided.
train.py - trains a net
buildnet.py - builds a net from scratch... really slow.
getth.py - finds post that eval mismatch from n-ply to 0-ply
referr.py - finds the error of a new breeded net against the
reference database.
Here's where I stranded... It worked it worked! I could breed
new nets, but none of the nets I trained was significantly
better than the original onem no matter how long I trained.
6. A programmer can now try out different things, like further
splitting of neural nets, or altering the inputs, or guessing
other algorithms thar might work.
Look at the different hand crafted inputs, can anyone be
removed? Can anything be added? I believe there is code to
dynamically add and remove nn inputs. If you add a input
make sure you add a new 'concept' and not just something
that's linearly depending on some other inputs.
I would love to see someone taking the training further.
-Øystein
signature.asc
Description: OpenPGP digital signature