Hello there, I am using caret and neuralnet to train a neural network to predict times table. I am using 'backprop' algorithm for neuralnet to experiment and learn.
Before using caret, I've trained a neuralnet without using caret, I've normalized my input & outputs using preProcess with 'range' method. Then I predicted my test set, did the multiplication and addition on predictions to get the real values. It gave me good results. What I want to ask is, when I try to train my network using caret, I get an error saying algorithm did not converge. I am thinking that I might be doing something wrong with my pre-processing, How would I go about using preProcess in train? Do I pass my not-normalized data set to the train function and train function handles normalization internally? You can find my R gist here <https://gist.github.com/andreyuhai/f299282f5a827e2a27c586afc9eb4eb5> Thank you, Burak ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
