https://github.com/znxlwm/tensorflow-mnist-cgan-cdcgan
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
https://github.com/znxlwm/tensorflow-mnist-cgan-cdcgan
cgan conditional-gan gan generative-adversarial-network mnist tensorflow
Last synced: about 1 month ago
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Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
- Host: GitHub
- URL: https://github.com/znxlwm/tensorflow-mnist-cgan-cdcgan
- Owner: znxlwm
- Created: 2017-07-31T03:59:15.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-08-10T00:19:29.000Z (about 8 years ago)
- Last Synced: 2025-07-16T22:16:09.015Z (3 months ago)
- Topics: cgan, conditional-gan, gan, generative-adversarial-network, mnist, tensorflow
- Language: Python
- Homepage:
- Size: 11.1 MB
- Stars: 152
- Watchers: 8
- Forks: 60
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# tensorflow-MNIST-cGAN-cDCGAN
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) [1] and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MANIST [2] dataset.
* you can download
- MNIST dataset: http://yann.lecun.com/exdb/mnist/
## Implementation details
* cGAN
## Resutls
* Generate using fixed noise (fixed_z_)
cGAN
cDCGAN
* MNIST vs Generated images
MNIST
cGAN after 100 epochs
cDCGAN after 30 epochs
* Training loss
cGAN
cDCGAN
* Learning time
* MNIST cGAN - Avg. per epoch: 3.21 sec; Total 100 epochs: 1800.37 sec
* MNIST cDCGAN - Avg. per epoch: 53.07 sec; Total 30 epochs: 2072.29 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] Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets." arXiv preprint arXiv:1411.1784 (2014).
(Full paper: https://arxiv.org/pdf/1411.1784.pdf)
[2] 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.