[Computer-go] Representing Komi for neural network
hughperkins at gmail.com
Fri Mar 20 17:24:33 PDT 2015
On 1/12/15, Álvaro Begué <alvaro.begue at gmail.com> wrote:
> A CNN that starts with a board and returns a single number will typically
> have a few fully-connected layers at the end. You could make the komi an
> extra input in the first one of those layers, or perhaps in each of them.
That's an interesting idea. But then, the komi wont really
participate in the hierarchical representation we are hoping that the
network will build, that I suppose we are hoping is the key to
obtaining human-comparable results?
But on the other hand, in the general case, where we want to give a
variety of inputs to the computer, eg a map, and an x/y position, has
anyone come up with a clean, effective way of combining these inputs
into the net? I dont recall seeing any such attempt/paper?
- if we feed the map into a conv net, and the x/y pos into the fc
layers, it seems like the x/y pos wont really participate in any
- if we have 100 conv input planes for each possible value of x, and
another 100 for each possible value of y, seems like overkill ... ?
- feeding reals into neural nets, which have layered activation
functions, empirically doesnt work well, and logically doesnt sound
like it should work that well
- contemplating just feeding them in as visual representations of the
number, printed each on a single plane :-D
Are there some papers/research/approaches in the area of combining
non-image inputs into convnets, in such a way that the non-image
inputs participate in the hierarchical structure, and at the same
without creating hundreds of input planes, for each single natural
input, which planes might contain only 5-10 bits of actual
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