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? >
