I'm not sure if this makes any sense, but is it possible to add, remove, or deactivate certain input nodes in a Conx neural network after it's been trained?
For example, consider a network that takes 3-inputs representing colors red, green, and blue. The network has N outputs, each indicating whether or not the color arraignment matches a particular object. Suppose I've trained this network on a corpus to match r,g,b colors to N objects, but now I want to train it on a different corpus missing the color red. I know in this trivial example it would probably be easiest to build and train a separate network, but imagine if this example were scaled to a larger non-trivial network, one with dozens or hundreds of input nodes. In that scenario I've spent a considerable amount of time training the network, so I wouldn't want to start from scratch if I could help it. Would it be possible in this case to "turn-off" the red input, and train/propagate using only the other two colors, but still have the option to "re-activate" the input later to again use the network with all three colors? Regards, Chris _______________________________________________ Pyro-users mailing list [email protected] http://emergent.brynmawr.edu/mailman/listinfo/pyro-users
