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znxlwm / pytorch-MNIST-CelebA-GAN-DCGAN

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
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The MNIST_DCGAN learning is too slow #1

Open milk-bottle-liyu opened 6 years ago

milk-bottle-liyu commented 6 years ago

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?

znxlwm commented 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.

milk-bottle-liyu commented 6 years ago

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.

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