On 1/12/15, Álvaro Begué <alvaro.be...@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 hierarchical representation? - 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 information? _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
