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“get nan or inf error” in cuda-convnet – possible fix

“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

https://github.com/s271/win_convnet

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

PS

If anyone wondering what cuda-convnet is here is a nice explanation:

http://fastml.com/object-recognition-in-images-with-cuda-convnet/

And here is the main paper about cuda-convnet

http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf

 

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17, January, 2014 - Posted by | Coding | , , ,

3 Comments

  1. Thanks very much for this! I’m trying to implement these changes here, but am working on a Linux machine, and when I try to build I find that the changes in matrix.cpp throw ‘function undefined’ errors. I think this might be because the #define statements for these two “_host” functions are inside a windows-looking #if statement in matrix.h…

    do you have an idea of what i could do at this point? I’ve tried relocating the #define statements but it hasn’t fixed the compiling problems so far…

    Comment by quoyle | 20, January, 2014

  2. Those function are just wrappers around system’s isnan and isinf functions, defined in matrix.h, line 44. My implementation seems win-specific.
    They have different names to make difference with cuda isnan

    Different implementations of isnan are here
    http://stackoverflow.com/questions/2249110/how-do-i-make-a-portable-isnan-isinf-function
    Most simple seems

    #include <cmath>

    int isnan_host(double x) {
    return std::isnan(x);
    }

    int isinf_host(double x) {
    return std::isinf(x);
    }
    Tell me if it help

    Comment by mirror2image | 20, January, 2014

  3. I’ve pushed std::isnan(x) variant into github

    Comment by mirror2image | 20, January, 2014


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