Exactly the one from the cited paper:
The best network had one convolutional layer with 64 7x7
filters, two convolutional layers with 64 5x5 filters, two lay-
ers with 48 5x5 filters, two layers with 32 5x5 filters, and
one fully connected layer.
I use caffe and the definition of the training
Detleft wrote:
> The idea is, I can do the equivalent of lets say 1000 playouts with a call to
> the CNN for the cost of 2 playouts some time...
That sounds like a good plan :-)
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What network architecture did you use? Can you give us some details?
On Sun, Feb 8, 2015 at 5:22 AM, Detlef Schmicker wrote:
> Hi,
>
> I am working on a CNN for winrate and territory:
>
> approach:
> - input 2 layers for b and w stones
> - 1. output: 1 layer territory (0.0 for owned by whit
Hi,
I am working on a CNN for winrate and territory:
approach:
- input 2 layers for b and w stones
- 1. output: 1 layer territory (0.0 for owned by white, 1.0 for owned
by black (because I missed TANH in the first place I used SIGMOID))
- 2. output: label for -60 to +60 territory leading by
