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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