https://github.com/znxlwm/tensorflow-mnist-gan-dcgan

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.
https://github.com/znxlwm/tensorflow-mnist-gan-dcgan

dcgan gan generative-adversarial-network mnist tensorflow

Last synced: 18 days ago
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Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

• Host: GitHub
• URL: https://github.com/znxlwm/tensorflow-mnist-gan-dcgan
• Owner: znxlwm
• Created: 2017-07-19T08:42:03.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-08-09T07:53:56.000Z (over 7 years ago)
• Last Synced: 2024-12-07T05:12:29.519Z (about 1 month ago)
• Topics: dcgan, gan, generative-adversarial-network, mnist, tensorflow
• Language: Python
• Homepage:
• Size: 18.6 MB
• Stars: 175
• Watchers: 10
• Forks: 91
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# tensorflow-MNIST-GAN-DCGAN

Tensorflow implementation of Generative Adversarial Networks (GAN) [1] and Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for MNIST [3] dataset.

* you can download

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

## Implementation details

* GAN

![GAN](tensorflow_GAN.png)

* DCGAN

![Loss](tensorflow_DCGAN.png)

## Resutls

* Generate using fixed noise (fixed_z_)

 GAN

 DCGAN





* MNIST vs Generated images

 MNIST 

 GAN after 100 epochs 

 DCGAN agter 20 epochs 







* Training loss

  * GAN

![Loss](MNIST_GAN_results/MNIST_GAN_train_hist.png)

* Learning time

    * MNIST GAN - Avg. per epoch: 4.97 sec; Total 100 epochs: 1255.92 sec

    * MNIST DCGAN - Avg. per epoch: 175.84 sec; Total 20 epochs: 3619.97 sec

## Development Environment

* Windows 7

* GTX1080 ti

* cuda 8.0

* Python 3.5.3

* tensorflow-gpu 1.2.1

* numpy 1.13.1

* matplotlib 2.0.2

* imageio 2.2.0

## 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.