Try to update Theano to the dev version. Maybe you ended up with the few.
Bad commit that produce wrong results.

Le lun. 2 oct. 2017 12:52, Fraser Robinson <fraserjamesrobin...@gmail.com>
a écrit :

> Hi,
>
> I've been trying to work through Convolutional Neural Networks (LeNet)
> <http://deeplearning.net/tutorial/lenet.html#lenet>. At this point I've
> just saved the code shared on the site and tried to run it. It runs without
> too many issues but the validation error stays at around 89%. I've run for
> 49,000 iterations and it still ended at roughly 89%.  Here's a print out of
> the function being called and the first few iterations:
>
> runfile('C:/Users/Fraser/Documents/Python
> Practice/DeepLearning/convolutional_mlp.py',
> wdir='C:/Users/Fraser/Documents/Python Practice/DeepLearning')
> Can not use cuDNN on context None: cannot compile with cuDNN. We got this
> error:
> In file included from C:\Program Files\NVIDIA GPU Computing
> Toolkit\CUDA\v9.0\include/host_defines.h:50:0,
>                  from C:\Program Files\NVIDIA GPU Computing
> Toolkit\CUDA\v9.0\include/driver_types.h:53,
>                  from C:\Program Files\NVIDIA GPU Computing
> Toolkit\CUDA\v9.0\include/cudnn.h:63,
>                  from
> c:\users\fraser\appdata\local\temp\try_flags_muyptb.c:4:
> C:\Program Files\NVIDIA GPU Computing
> Toolkit\CUDA\v9.0\include/crt/host_defines.h:84:0: warning: "__cdecl"
> redefined
>  #define __cdecl
>  ^
> <built-in>: note: this is the location of the previous definition
> C:/ProgramData/Anaconda22/Library/mingw-w64/bin/../lib/gcc/x86_64-w64-mingw32/5.3.0/../../../../x86_64-w64-mingw32/bin/ld.exe:
> cannot find -lcudnn
> collect2.exe: error: ld returned 1 exit status
>
> Mapped name None to device cuda: GeForce GTX 1080 Ti (0000:23:00.0)
> loading data
> building the model
> C:/Users/Fraser/Documents/Python
> Practice/DeepLearning/convolutional_mlp.py:104: UserWarning: DEPRECATION:
> the 'ds' parameter is not going to exist anymore as it is going to be
> replaced by the parameter 'ws'.
>   ignore_border=True
> C:\ProgramData\Anaconda22\lib\site-packages\nose_parameterized\__init__.py:7:
> UserWarning: The 'nose-parameterized' package has been renamed
> 'parameterized'. For the two step migration instructions, see:
> https://github.com/wolever/parameterized#migrating-from-nose-parameterized-to-parameterized
> (set NOSE_PARAMETERIZED_NO_WARN=1 to suppress this warning)
>   "The 'nose-parameterized' package has been renamed 'parameterized'. "
> training
> training @ iter =  0
> epoch 1, minibatch 100/100, validation error 89.130000 %
>      epoch 1, minibatch 100/100, test error of best model 88.510000 %
> training @ iter =  100
> epoch 2, minibatch 100/100, validation error 88.710000 %
>      epoch 2, minibatch 100/100, test error of best model 88.250000 %
> training @ iter =  200
> epoch 3, minibatch 100/100, validation error 88.720000 %
> training @ iter =  300
> epoch 4, minibatch 100/100, validation error 88.670000 %
>      epoch 4, minibatch 100/100, test error of best model 88.090000 %
> training @ iter =  400
> epoch 5, minibatch 100/100, validation error 88.700000 %
> training @ iter =  500
> epoch 6, minibatch 100/100, validation error 88.820000 %
> training @ iter =  600
>
> As I said, I've left it running for around 500 epochs and it still just
> sits around the 88 - 89 mark. I can post more epochs if needed. I really
> don't know where to start in debugging this, has anyone else implemented
> this tutorial on the same version of theano? Suggestion?
>
> Here's my system:
>
> Windows 10 Pro
> AMD Ryzen 7 1800X
> GTX 1080ti (only graphics installed - no onboard)
>
> Python
> Version: 2.7.13
>
> NumPy
> Version: 1.13.1
>
> SciPy
> Version: 0.19.1
>
> Nose
> Version 1.37
>
> Theano
> Version: 0.9.0.dev-c697eeab84e5b8a74908da654b66ec9eca4f1291
>
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