Actually the DCNN plays on 9x9 acceptably well (somewhere in the
single-digit kyus).

On Friday, March 18, 2016, Benjamin Teuber <bsteu...@gmail.com> wrote:

> This is really cool. Now it just needs to learn 9x9 via reinforcement
> learning ;-)
>
> Josef Moudrik <j.moud...@gmail.com
> <javascript:_e(%7B%7D,'cvml','j.moud...@gmail.com');>> schrieb am Fr.,
> 18. März 2016 10:21:
>
>> Aha! Thanks for the clarification.
>>
>> Josef
>>
>> Dne pá 18. 3. 2016 9:59 uživatel Darren Cook <dar...@dcook.org
>> <javascript:_e(%7B%7D,'cvml','dar...@dcook.org');>> napsal:
>>
>>> > If I remember correctly, it is not browser implementation, but rather a
>>> > frontend. The actual computation runs on server, browser only
>>> communicates the
>>> > moves and shows the results.
>>>
>>> No, a quick test shows once it loads it has not made any server calls.
>>> It has a 14MB file which looks like [1]
>>>
>>> Darren
>>>
>>>
>>> [1]:
>>> // 8 layer network trained on GoGoD data, truncated to 6 decimal places
>>> to reduce size
>>> var json_net = {"layers": [{"layer_type": "input", "out_sy": 25,
>>> "out_depth": 8, "out_sx": 25}, {"layer_type"
>>> : "conv", "sy": 25, "sx": 25, "out_sx": 19, "out_sy": 19, "stride": 1,
>>> "pad": 0, "biases": {"depth":
>>>  64, "sx": 1, "sy": 1, "w": [0.519023, -1.379795, -0.495255, -0.051380,
>>> -0.466160, -1.380873, -0.630742
>>> , -0.174662, -0.743714, -1.288785, -0.607110, -0.536119, -0.819585,
>>> -0.248130, -0.629681, -0.004683,
>>>  -0.408890, -1.701742, -0.011255, -0.833270, -0.665327, -0.127002,
>>> -0.793772, -0.518614, -1.390844, -1
>>> .982825, -0.012530, -0.140848, -1.255086, -0.761665, -0.077154,
>>> -0.748323, -0.086952, -0.175683, -1.526860
>>> , 0.098685, -0.030402, -0.903232, -
>>> ...
>>>
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