[Computer-go] Reducing network size? (Was: AlphaGo Zero)

Darren Cook darren at dcook.org
Mon Oct 23 01:39:18 PDT 2017

> The source of AlphaGo Zero is really of zero interest (pun intended).

The source code is the first-hand account of how it works, whereas an
academic paper is a second-hand account. So, definitely not zero use.

> So yes, the database of 29M self-play games would be immensely more
> valuable. (Probably like the last 5M or so is fine, too). I prefer the
> games over the network - with the games it's easier to train a smaller
> network that gives better results on PC's that don't have 4 TPUs in them.

Does anyone know of research/code on the topic of reducing the
size/complexity of deep learning networks? I think it should be possible
to reduce either the number of layers, or the size of each layer, with
only a small drop in accuracy, but it seems like the two fully-connected
networks at the top will then need retraining?

However, this article is showing results, beyond what I thought would be
possible, even on the very deep image networks:


BTW, I notice his PhD thesis has just been published. Might have to add
it to my reading list:  http://stanford.edu/~songhan/


More information about the Computer-go mailing list