README.md

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

• DCGAN

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

• 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.