Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task.
So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network learnt anything at all, I checked the network weights and those have definitely changed. However, when examining the trainednet$fitted.values, those are all the same so it rather looks as if the network is doing a classification. I can even set linout=FALSE and then it outputs "1" (the class?) for each training example. The trainednet$residuals are correct (difference between predicted/fitted example and actual response), but rather useless. The same happens if I run nnet with the formula/data.frame interface, btw. As per the suggestion in the ?nnet page: "If the response is not a factor, it is passed on unchanged to 'nnet.default'", I assume that the network is doing regression since my trainresponse variable is a numerical vector and _not_ a factor. I'm currently lost and I can't see that the AMORE/neuralnet packages are any better (moreover, they don't implement the formula/dataframe/predict things). I've read the manpages of nnet and predict.nnet a gazillion times, but I can't really find an answer there. I don't want to do classification, but regression. Thanks for any help. Georg. -- Research Assistant Otto-von-Guericke-Universität Magdeburg resea...@georgruss.de http://research.georgruss.de ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
