Thanks David! You’re right, it should be possible to somehow read out that
prototxt file. I’ll see how that goes for me. :) The rest of your info is
still a bit mysterious for me. But I will figure it out eventually.

Urban

On Fri, May 20, 2016 at 9:49 PM, David Fotland <[email protected]>
wrote:

> The alphaGo network is detailed in their paper.  They have about 50 binary
> inputs, one layer of 5x5 convolutional filters, and about 12 layers of 3x3
> convolutional filters.  Detlef’s net is specified in the prototxt file he
> published here.  It’s wider and deeper, but with fewer inputs.
>
>
>
> The current popular approach is a to use 5x5 or 7x7 filters in the first
> layers, and 3x3 filters in higher layers.  The topmost layer is special,
> typically a 1x1 convolutional filter.  AlphaGo uses position dependent
> biases. RelU seems to work well, and pooling is not used (for obvious
> reasons).
>
>
>
> In my experiments it is essential to have the first layer filters larger
> than 3x3.  In higher layers 3x3 seems to work fine.
>
>
>
> Hope this helps.
>
>
>
> David
>
>
>
> *From:* Computer-go [mailto:[email protected]] *On
> Behalf Of *Urban Hafner
> *Sent:* Friday, May 20, 2016 4:47 AM
> *To:* [email protected]
> *Subject:* [Computer-go] Commonly used neural network architectures
>
>
>
> Hey there,
>
>
>
> just like everyone else I’m currently looking into neural networks for my
> go program. ;) Apart from the AlphaGo paper where I can I find information
> about network architecture? There’s the network from April 2015 from Detlef
> (http://computer-go.org/pipermail/computer-go/2015-April/007573.html) but
> I don’t know enough about caffe to figure out the architecture. Basically,
> I more interested in understanding how to build a network myself than just
> using a pre-trained network.
>
>
>
> Cheers,
>
>
>
> Urban
>
> --
>
> Blog: http://bettong.net/
>
> Twitter: https://twitter.com/ujh
>
> Homepage: http://www.urbanhafner.com/
>
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>



-- 
Blog: http://bettong.net/
Twitter: https://twitter.com/ujh
Homepage: http://www.urbanhafner.com/
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