“get nan or inf” error happens sometimes on lower-end GPU’s in cuda-convnet. I have traced this error to NaN values in the weights of convolutional layers. I still not clear to me why these NaN values appear in the weights. Are they backpropagate from fully-connected layers or popping up in the convolution kernel? It looks to me latter is more likely. Anyway I made a temporary fix – just scan weight’s gradients with simple cuda kernel and replace NaN’a with zeroes. Didn’t observe the error after that.
I have pushed fix into windows version of cuda-convnet at
Fix activated with option –fix-nan=1
There shouldn’t be any problem with making those changes for linux version – there are several small changes in *.cu and *.py files only
If anyone wondering what cuda-convnet is here is a nice explanation:
And here is the main paper about cuda-convnet