[GitHub] juliusshufan opened a new issue #11122: Loss value is "nan" when using gluon vision models training CIFAR10 when model is hybridized.

[GitBox <gi...@apache.org>]

juliusshufan opened a new issue #11122: Loss value is "nan" when using gluon vision models training CIFAR10 when model is hybridized.
URL: https://github.com/apache/incubator-mxnet/issues/11122
 
 
   ## Description
   I am drafting a training script based on the example/gluon/image_classfication.py, and the script can be found https://github.com/juliusshufan/mxnet/blob/master/gluonmodel/image_classification.py
   The major changes is I am trying to get the loss value (cross-entropy loss for this case) during training. (see line 30 and 246), but the reported loss value is always nan. 
   
   However, if I set the --mode as "symbolic", with my changes on line 264~266, the loss value can be reported with a normal value.
   
   ## Environment info (Required)
   CentOS 7.2 CUDA-9.0
   ```
   What to do:
   Running the script:
   python image_classification.py --dataset=cifar10 --model=resnet50_v1 --batch-size=64 --gpus=0
   The loss will always be nan
   If using 
   python image_classification.py --dataset=cifar10 --model=resnet50_v1 --batch-size=64 --gpus=0 **--mode=symbolic**
   Then the loss value is normal 
   ```
   
   Package used (Python/R/Scala/Julia):
   Python
   
   ## Build info (Required if built from source)
   make -j USE_CUDA=1 USE_CUDNN=1 USE_CUDA_PATH=cudapath USE_BLAS=openblas USE_OPENCV=1
   
   Compiler (gcc/clang/mingw/visual studio):
   GCC 4.8.5
   MXNet commit hash:
   (Paste the output of `git rev-parse HEAD` here.)
   
   ## Steps to reproduce
   Clone my script: https://github.com/juliusshufan/mxnet/tree/master/gluonmodel 
   
   Running the script:
   python image_classification.py --dataset=cifar10 --model=resnet50_v1 --batch-size=64 --gpus=0
   The loss will always be nan
   If using 
   python image_classification.py --dataset=cifar10 --model=resnet50_v1 --batch-size=64 --gpus=0 **--mode=symbolic**
   

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