question about the version of pytorch
zhiqiangzhongddu opened this issue · 8 comments
Hi,
Thanks for your kind sharing.
I want to reproduce your results on my device, but my training results are too bad,
The evaluating HR and NDCG of some epochs are 0.0000, do you have any idea about this case? And could you tell me the Pytorch version that you use in your side?
Thanks.
Could you share your training curve? I tested the repo using pytorch 0.2 but I guess it should work under 0.3 and 0.4 as well.
Hi,
Thanks for your kind sharing.
I want to reproduce your results on my device, but my training results are too bad,
The evaluating HR and NDCG of some epochs are 0.0000, do you have any idea about this case? And could you tell me the Pytorch version that you use in your side?Thanks.
@LaceyChen17 @zhiqiangzhongddu , I got the same issue, see the curves on dataset ml-1m,
Could you share your training curve? I tested the repo using pytorch 0.2 but I guess it should work under 0.3 and 0.4 as well.
Before, I used Pytorch 1.0.1, the latest version.
I tried Pytorch 0.4.0 with Cuda 80&90, but they don't work.
Error:
RuntimeError: input and target shapes do not match: input [1024 x 1], target [1024].
And I'm not success to install Pytorch 0.2 and 0.3 on my computer. It seems Conda doesn't support them anymore.
news: the same bug exists with Pytorch-0.3.1, Cuda=90&80&100 in my computer.
The bug was mainly caused by MSELoss
.
The repo works well under the latest pytorch(1.0.1post2) now.
Thank you for reporting the bug. Also, pay attention to l2_regularization
parameter.
Strong l2_regularization
might lead to model underfitting and thus HR 0 & NDCG 0.
The bug was mainly caused by
MSELoss
.The repo works well under the latest pytorch(1.0.1post2) now.
Thank you for reporting the bug. Also, pay attention to
l2_regularization
parameter.Strong
l2_regularization
might lead to model underfitting and thus HR 0 & NDCG 0.
Thanks so much for fixing this bug.
The bug was mainly caused by
MSELoss
.The repo works well under the latest pytorch(1.0.1post2) now.
Thank you for reporting the bug. Also, pay attention to
l2_regularization
parameter.Strong
l2_regularization
might lead to model underfitting and thus HR 0 & NDCG 0.
Thanks.