Thanks, that's pretty much my understanding. Scaling the inputs seems to be important, too, from what I read. I'm also interested in a framework that will trim off redundant inputs.
I have run the mocha tutorial examples, and it looks very promising because the structure is clear, and there are C++ and cuda backends. The C++ backend, with openmp, gives me a good performance boost over the pure Julia backend. However, I'm not so sure that it will allow for trimming redundant inputs. Also, I have some ideas on how to restrict the net to remove observationally equivalent configurations, which should aid in training, and I don't think I could implement those ideas with mocha. >From what I see, the focus of much recent work in neural nets seems to be on classification and labeling of images, and regression examples using the modern tools seem to be scarce. I'm wondering if that's because other tools work better for regression, or simply because it's an old problem that is considered to be well studied. I would like to see some examples of regression nets that work well, using the modern tools, though, if there are any out there. On Saturday, January 30, 2016 at 2:32:16 PM UTC+1, Jason Eckstein wrote: > > I've been using NN for regression and I've experimented with Mocha. I > ended up coding my own network for speed purposes but in general you simply > leave the final output of the neural network as a linear combination > without applying an activation function. That way the output can represent > a real number rather than compress it into a 0 to 1 or -1 to 1 range for > classification. You can leave the rest of the network unchanged. > > On Saturday, January 30, 2016 at 3:45:27 AM UTC-7, michae...@gmail.com > wrote: >> >> I'm interested in using neural networks (deep learning) for multivariate >> multiple regression, with multiple real valued inputs and multiple real >> valued outputs. At the moment, the mocha.jl package looks very promising, >> but the examples seem to be all for classification problems. Does anyone >> have examples of use of mocha (or other deep learning packages for Julia) >> for regression problems? Or any tips for deep learning and regression? >> >
