Open milk-bottle-liyu opened 6 years ago
Hi,
I don't know exactly why learning time is different.
I ran my code again using these settings - 64 x 64 MNIST 60,000 images, 128 batch size, 'd' = 128 and got a similar result.
Check your data, batch size, and dimension parameter of network ( 'd' value in my network) etc.
Dear Kang: My apologize, I didnt check my email in time. I finally found out that, someone else was also using GPU, so the GPU is busy, thats the reaso. PS: Im new with pytorch. I used caffe. I was wondering why PyTorch's Volatile GPU-Util is quite high as by using caffe I can only achieve around 70%. But when using PyTorch, it is quite easy to achieve 90%+. Do you have any idea with that?
2017-11-23 10:26 GMT+08:00 Hyeonwoo Kang notifications@github.com:
Hi,
I don't know exactly why learning time is different.
I ran my code again using these settings - 64 x 64 MNIST 60,000 images, 128 batch size, 'd' = 128 and got a similar result.
Check your data, batch size, and dimension parameter of network ( 'd' value in my network) etc.
— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN/issues/1#issuecomment-346519759, or mute the thread https://github.com/notifications/unsubscribe-auth/AQ_3nuZJS1yU1KZGmaTKCzJca-iYsq3tks5s5NflgaJpZM4Qm8iJ .
Sir, as u said the avg time for an epoch is around 180s, while on my server, it shows:
[1/20] - ptime: 372.38, loss_d: 0.597, loss_g: 5.759
My environment is: ubuntu 16.04+cuda8.0+cudnn 6+ pytorch 0.2 +Titan XP
I also set the worker_num for train data loader to 2, so it shouldn't be a problem of IO. Do u have any idea of what's going wrong , Sir?