On 23-05-17 10:51, Hideki Kato wrote: > (2) The number of possible positions (input of the value net) in > real games is at least 10^30 (10^170 in theory). If the value > net can recognize all? L&Ds depend on very small difference of > the placement of stones or liberties. Can we provide necessary > amount of training data? Have the network enough capacity? > The answer is almost obvious by the theory of function > approximation. (ANN is just a non-linear function > approximator.)
DCNN clearly have some ability to generalize from learned data and perform OK even with unseen examples. So I don't find this a very compelling argument. It's not like Monte Carlo playouts are going to handle all sequences correctly either. Evaluations are heuristic guidance for the search, and a help when the search terminates in an unresolved position. Having multiple independent ones improves the accuracy of the heuristic - a basic ensemble. > (3) CNN cannot learn exclusive-or function due to the ReLU > activation function, instead of traditional sigmoid (tangent > hyperbolic). CNN is good at approximating continuous (analog) > functions but Boolean (digital) ones. Are you sure this is correct? Especially if we allow leaky ReLU? -- GCP _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
