This is what have been thinking about, yet unable to find an error.
Currently, I'm working with:
- SGF Database: fuseki info Tygem -> http://tygem.fuseki.info/index.php
(until recently I was working with games of all level from KGS)
- The data is then analyzed by a script which extracts all kind of
features from games. When I'm training a network, I load the features I
want from this analysis to build the batch. I have 2 possible methods
for the batch construction. I can either add moves one after the other
(the fast mode) or pick random moves among different games (slower but
reduces the variance). I set the batch size according to my GPU memory
(200 moves in the case of full sized value/policy network). I don't
think the problem may come from here since the data is the same for all
the networks
- For the input, I’m using the same architecture as
https://github.com/TheDuck314/go-NN (I have been trying a lot of kind of
shapes, from minimalist to alphago)
- For the network, I’m once again using TheDuck314 network
(EvalModels.Conv11PosDepFC1ELU) with the same layers
https://github.com/TheDuck314/go-NN/blob/master/engine/Layers.py, and
the learning rate he recommends
During sime of the tests, all the networks I was training had the same
layers except for the last. So as you suggested, I was also wondering if
this last layer wasn’t the problem. Yet, I haven’t found any error.
Le 20-Jun-17 à 3:19 AM, Gian-Carlo Pascutto a écrit :
On 19-06-17 17:38, Vincent Richard wrote:
During my research, I’ve trained a lot of different networks, first on
9x9 then on 19x19, and as far as I remember all the nets I’ve worked
with learned quickly (especially during the first batches), except the
value net which has always been problematic (diverge easily, doesn't
learn quickly,...) . I have been stuck on the 19x19 value network for a
couple months now. I’ve tried countless of inputs (feature planes) and
lots of different models, even using the exact same code as others. Yet,
whatever I try, the loss value doesn’t move an inch and accuracy stays
at 50% (even after days of training). I've tried to change the learning
rate (increase/decrease), it doesn't change. However, if I feed a stupid
value as target output (for example black always win) it has no trouble
learning.
It is even more frustrating that training any other kind of network
(predicting next move, territory,...) goes smoothly and fast.
Has anyone experienced a similar problem with value networks or has an
idea of the cause?
1) What is the training data for the value network? How big is it, how
is it presented/shuffled/prepared?
2) What is the *exact* structure of the network and training setup?
My best guess would be an error in the construction of the final layers.
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