“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
I have seen an excellent wlakthrough on building Alex Krizhevsky’s cuda-convnet for windows, but difference in configuration and installed packages could be tiresome. So here is complete build of convnet for windows 64:
It require having CUDA compute capability 2.0 or better GPU of cause, Windows 64bit, Visual Studio 64bits and Python 64bit with NumPy installed. The rest of libs and dlls are precomplied. In building it I’ve followed instructions by Yalong Bai (Wyvernbai) from http://www.asiteof.me/archives/50.
Read Readme.md before installation – you may (or may not) require PYTHONPATH environmental variable set.
On side note I’ve used WinPython for both libraries and running the package. WinPython seems a very nice package, which include Spyder IDE. I have some problems with Spyder though – sometimes it behave erratically during debugging/running the code. Could be my inexperience with Spyder though. Another nice package – PythonXY – regretfully can not be used – it has only 32 bit version and will not compile with/run 64 bit modules.
This demo was inspired by Escapist article “I hate magic” by Robert Rath ( http://www.escapistmagazine.com/artic… ) and “continuous game of life” (http://arxiv.org/abs/1111.1567 ) by Stephan Rafler
This is attempt to show how GPGPU could be used to model magic as different physics.
The demo run at 70 fps at laptop with GF GTX 670M. With some tradeoffs it can be made run much faster
require CUDA GPGPU with at least 2.0 Cuda compute capability (practically all modern cards, GF GTX 550 or better)