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 theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.