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This repository has been archived by the owner on Nov 17, 2023. It is now read-only.
For bugs or installation issues, please provide the following information.
The more information you provide, the more likely people will be able to help you.
Environment info
Operating System: Linux idc01-rank-gpu-01 2.6.32-431.el6.x86_64 #1 SMP Sun Nov 10 22:19:54 EST 2013 x86_64 x86_64 x86_64 GNU/Linux
Compiler: gcc-4.8.5
Package used (Python/R/Scala/Julia): Python
MXNet version: 0.9.4
Or if installed from source: yes
MXNet commit hash (git rev-parse HEAD):
If you are using python package, please provide
Python version and distribution: 2.7.10
If you are using R package, please provide
R sessionInfo():
Error Message:
Please paste the full error message, including stack trace.
Minimum reproducible example
if you are using your own code, please provide a short script that reproduces the error.
###################################
I am trying the new mxnet to do the inception-bn-full test. I was tried successful in mxnet 0.5.0, but I found still not work in mxnet 0.9.4, even try to set fix_gamma = false.
And I found the train acc is very strange which vary unstable. (when doing test in 0.5.0 it increase very stable).
Also I also doing a simple test in mnist, I turn the mnist data to images, then processing then to rec. using alexnet to training the network. It does not work at all.
So I guess is there something wrong with the code in train_imagenet.py or etc. or sth bug exist.
The text was updated successfully, but these errors were encountered:
@piiswrong thanks,
Before I report the problem, I have already turn into 0.9.4, and failed to do image classification tasks.
Actually, I have some tasks in image classification the scale is around ImageNet tasks, So how can I try to do the train if not turn back to 0.5.0.
@piiswrong
I have been frustrated here, and I try to detect the error, I just found one significant difference in new version training process, the acc is changing unstable while in old version (0.5.0) the changing is very stable and can finally increase to a good score.
I guess something with cuda matters? or can you give some building suggestion?
For bugs or installation issues, please provide the following information.
The more information you provide, the more likely people will be able to help you.
Environment info
Operating System: Linux idc01-rank-gpu-01 2.6.32-431.el6.x86_64 #1 SMP Sun Nov 10 22:19:54 EST 2013 x86_64 x86_64 x86_64 GNU/Linux
Compiler: gcc-4.8.5
Package used (Python/R/Scala/Julia): Python
MXNet version: 0.9.4
Or if installed from source: yes
MXNet commit hash (
git rev-parse HEAD
):If you are using python package, please provide
Python version and distribution: 2.7.10
If you are using R package, please provide
R
sessionInfo()
:Error Message:
Please paste the full error message, including stack trace.
Minimum reproducible example
if you are using your own code, please provide a short script that reproduces the error.
###################################
I am trying the new mxnet to do the inception-bn-full test. I was tried successful in mxnet 0.5.0, but I found still not work in mxnet 0.9.4, even try to set fix_gamma = false.
And I found the train acc is very strange which vary unstable. (when doing test in 0.5.0 it increase very stable).
Also I also doing a simple test in mnist, I turn the mnist data to images, then processing then to rec. using alexnet to training the network. It does not work at all.
So I guess is there something wrong with the code in train_imagenet.py or etc. or sth bug exist.
The text was updated successfully, but these errors were encountered: