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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