An update: this apparently has nothing to do with softplus. I've drastically simplified my network and removed many components (reducing the loss function to a simple quadratic) and I still get the same error. The issue seems to come from how I'm doing sub-tensor indexing (which obviously should not introduce a NaN). I'll try to generate some simple sample code that can reproduce the error.
On Sunday, September 18, 2016 at 10:14:31 PM UTC+2, [email protected] wrote: > > I've been banging my head against this problem for several hours so I > wanted to make sure one of my assumptions is not flawed (and hopefully get > some advice). > > I have a relatively simple network that is entirely linear except for the > loss function which is a sum of many softpluses. A snippet of the code is > (here a_emb is a matrix and bias a scalar): > > z= bias - (a_emb - b_emb).norm(2, axis=1) > if clip: > z=z.clip(-bound,bound) > L0 = -T.nnet.softplus(-z) > > L0 is one contribution to the loss. There are a few more from higher rank > tensors that look like this (zn is a 3-tensor): > > L1=T.sum(-T.nnet.softplus(zn), axis=1) > > The only real complexity in the network is the use of subtensor indexing. > Basically I'm training a very large embedding model so to avoid updating > the whole matrix I take all inputs (e.g. indices corresponding to "a_emb", > "b_emb" above), put them in a subtensor, and then extract them out again > (by subindexing). I then only update the subtensor via something like this: > > updates = [(self.V, T.set_subtensor(subV, subV-lr* grads))] > > If it helps I could post code showing how I setup the subtensor (but all > that stuff is just indexing, there's no non-linear operation there). > > There's also a non-linear L2 loss function on the subtensor but I can't > imagine that's causing the problem: > > L2lossV=(subV.norm(2, axis=1)) > > I'm not sure all the above is relevant but the issue is that I'm getting > NaN's very consistently and I'm having a hard time figuring out what > operation is causing it (using Nangaurd just made the code too slow to ever > get to the NaN). > > As you can see from the above I tried to fight the NaN's by clipping the > input to softplus but this doesn't seem to work. I clip the inputs to -10 > to 10 but I still get NaNs. > > My understanding, from reading around a little, was that softplus was > supposed to help avoid NaNs so I'm a bit confused that they're still > cropping up (and I can't see where else they could come from). I would > appreciate any advice as to how to figure out the problem or even code > around it. > > This is all with theano 0.8.2 on Ubuntu 16.04 and I'm using a CPU (but > with float32). > > Thanks in advance for any help. > > > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
