Finally found the problem. In the end, it was as stupid as expected:
When I pick a game for the batch creation I select randomly a limited
number of moves inside the game. In the case of the value network I use
like 8-16 moves to not overfit the data (I can't take 1 or then the I/O
operations slow down the training) and for other networks, I would
simply take all the moves. Or at least this was what I thought my code
was doing. Instead of picking N random moves in the game, it was picking
the first N moves in a random order. So... my value network was trained
to tell me the game is balanced at the beginning...
Le 20-Jun-17 à 5:48 AM, Gian-Carlo Pascutto a écrit :
On 19/06/2017 21:31, Vincent Richard wrote:
- 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).
You absolutely need the latter, especially as for outcome prediction the
moves from the same game are not independent samples.
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.
...
However, if I feed a stupid
value as target output (for example black always win) it has no trouble
learning.
A problem with side to move/won side marking in the input or feature
planes, or with the expected outcome (0 vs 1 vs -1)?
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