I don't think the problem is with trainNetwork, but rather epoch. You might try adjusting your network datatype and sub datatypes in the manner of data Network = Network !Int !Int until you narrow down which piece is causing the problem. On Mon, Jan 26, 2015 at 7:19 AM, Hans Georg Schaathun < georg+haskell@schaathun.net> wrote:
Hi,
can someone give some hints on how to get around a stack space overflow?
My problem is with the training function for a neural network:
trainNetwork :: Double -> Samples -> Int -> Network -> Network trainNetwork _ _ 0 n = n trainNetwork eta samples c n = trainNetwork eta samples (c-1) $! epoch eta n samples epoch :: Double -> Network -> Samples -> Network
So trainNetwork runs epoch c times, each time taking a Network in and modifying the Network as output. Clearly, space complexity can be made constant in c, but I get stack overflow if and only if c is too large.
As you can see, I have tried to make the epoch evaluation strict ($!). I have also tried bang patterns on the input parameter n, and I have tried rewriting with foldr/foldl/foldl', and I have tried switchin the inner and outer calls (epoch vs. trainNetwork), all to no avail.
I reckon this loop like pattern should be fairly common ... does it have a common solution too?
TIA -- :-- Hans Georg _______________________________________________ Beginners mailing list Beginners@haskell.org http://www.haskell.org/mailman/listinfo/beginners