https://github.com/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
https://github.com/znxlwm/pytorch-mnist-celeba-gan-dcgan

celeba dcgan gan generative-adversarial-network mnist pytorch

Last synced: 6 days ago
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Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

• Host: GitHub
• URL: https://github.com/znxlwm/pytorch-mnist-celeba-gan-dcgan
• Owner: znxlwm
• Created: 2017-07-18T03:07:16.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-01-21T18:33:57.000Z (12 months ago)
• Last Synced: 2024-12-21T07:05:41.869Z (13 days ago)
• Topics: celeba, dcgan, gan, generative-adversarial-network, mnist, pytorch
• Language: Python
• Homepage:
• Size: 17.3 MB
• Stars: 518
• Watchers: 20
• Forks: 145
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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README

        
# pytorch-MNIST-CelebA-GAN-DCGAN

Pytorch implementation of Generative Adversarial Networks (GAN) [1] and Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for MNIST [3] and CelebA [4] datasets.

* If you want to train using cropped CelebA dataset, you have to change isCrop = False to isCrop = True.

* you can download

  - MNIST dataset: http://yann.lecun.com/exdb/mnist/

  - CelebA dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

* pytorch_CelebA_DCGAN.py requires 64 x 64 size image, so you have to resize CelebA dataset (celebA_data_preprocess.py).

* pytorch_CelebA_DCGAN.py added learning rate decay code.

## Implementation details

* GAN

![GAN](pytorch_GAN.png)

* DCGAN

![Loss](pytorch_DCGAN.png)

## Resutls

### MNIST

* Generate using fixed noise (fixed_z_)

 GAN

 DCGAN





* MNIST vs Generated images

 MNIST 

 GAN after 100 epochs 

 DCGAN after 20 epochs 







* Training loss

  * GAN

![Loss](MNIST_GAN_results/MNIST_GAN_train_hist.png)

* Learning Time

  * MNIST DCGAN - Avg. per epoch: 197.86 sec; (if you want to reduce learning time, you can change 'generator(128)' and 'discriminator(128)' to 'generator(64)' and 'discriminator(64)' ... then Avg. per epoch: about 67sec in my development environment.)

  

### CelebA

* Generate using fixed noise (fixed_z_)

 DCGAN 

 DCGAN crop 





* CelebA vs Generated images

 CelebA 

 DCGAN after 20 epochs 

 DCGAN crop after 30 epochs 







* Learning Time

  * CelebA DCGAN - Avg. per epoch: 732.54 sec; total 20 epochs ptime: 14744.66 sec

## Development Environment

* Ubuntu 14.04 LTS

* NVIDIA GTX 1080 ti

* cuda 8.0

* Python 2.7.6

* pytorch 0.1.12

* torchvision 0.1.8

* matplotlib 1.3.1

* imageio 2.2.0

* scipy 0.19.1

## Reference

[1] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.

(Full paper: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)

[2] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).

(Full paper: https://arxiv.org/pdf/1511.06434.pdf)

[3] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.

[4] Liu, Ziwei, et al. "Deep learning face attributes in the wild." Proceedings of the IEEE International Conference on Computer Vision. 2015.