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train_mnist.py doesn't work on Mac OS X's vecLib #704
When I executed train_mnist.py on Mac OS X, accuracy seems too low. Here's the result:
I doubt the vecLib (default Blas library in Mac OS X) as the root cause of this problem, because when I install OpenBlas, training accuracy go up to around 99%, it seems working correctly.
Maybe this problem is related to #584, but I'm not sure.
If this problem is reproducible, it's nice to add some notes to use other BLAS library in the installation procedure.
I'm tested latest Chainer (188.8.131.52) on CPU mode, (because I have no GPU). OS is Mac OS X 10.11 (el capitan).
Hi, I tried #712. Here's the result.
Unfortunately, performance is still worse... How about on your environment?
In my environment it maybe works well:
I uninstalled mkl, and open blas.
Here is my
here's my result:
I notice the difference between mine and yours, say, I'm using miniconda python.
Here's my new result of numpy.show_config()
The result of train_mnist.py is still bad... (accuracy is always under 0.7, even for training data)
I have no clue for now, I hope someone will resolve the issue in the near future.
Maybe so, maybe not.
Since it's hard to install LLVM 5.1, I installed gcc (5.2.0) from homebrew instead. And then, I removed OpenBlas, python and numpy, then installed my python (by pyenv) and numpy (from source). This means, all code other than vecLib are compiled by gcc.
The result of train_mnist.py is still bad (accuracy for both of train/test is around 0.7.)
Then, I installed OpenBlas and numpy from source. Surprisingly, the result of train_mnist.py is bad, too.
Maybe it's a good time to reinstall my OS...