A 3-layer network (input, hidden, output) is sufficient to be a universal 
function approximator, so from a theoretical perspective only 3 layers are 
necessary. But the gap between theoretical and practical is quite large.

 

The CNN architecture builds in translation invariance and sensitivity to local  
phenomena. That gives it a big advantage (on a per distinct weight basis) over 
the flat architecture.

 

Additionally, the input layers of these CNN designs are very important. 
Compared to a stone-by-stone representation, the use of high level concepts in 
the input layer allows the network to devote its capacity to advanced concepts 
rather than synthesizing basic concepts.

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
uurtamo .
Sent: Tuesday, December 23, 2014 7:34 PM
To: computer-go
Subject: Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional 
Neural Networks

 

I thought that any layers beyond 3 were irrelevant. Probably I'm subsuming your 
nn into what I learned about nn's and didn't read anything carefully enough.

Can you help correct me?

s.

On Dec 23, 2014 6:47 AM, "Aja Huang" <ajahu...@google.com 
<mailto:ajahu...@google.com> > wrote:

On Mon, Dec 22, 2014 at 12:38 PM, David Silver <davidstarsil...@gmail.com 
<mailto:davidstarsil...@gmail.com> > wrote:

we'll evaluate against Fuego 1.1 and post the results. 

 

I quickly tested our 12-layer CNN against Fuego 1.1 with 5 secs and 10 secs per 
move, 2 threads. The hardware is Intel(R) Xeon(R) CPU E5-2687W 0 @ 3.10GHz.

 

5 secs per move     12-layer CNN scored 55.8% ±5.4

10 secs per move   12-layer CNN scored 32.9% ±3.8

 

Fuego1.1 is clearly much weaker than the latest svn release. And interestingly, 
the network is actually as strong as Fuego 1.1 with 5 secs per move. 

 

Since Clark and Storkey's CNN scored 12% against Fuego 1.1 running on a weaker 
hardware, our best CNN is about 220 to 310 Elo stronger which is consistent to 
the results against GnuGo.

 

Regards,

Aja

 


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