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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

GAN

Resutls

Generate using fixed noise (fixed_z_)

<table align='center'> <tr align='center'> <td> cGAN </td> <td> cDCGAN </td> </tr> <tr> <td><img src = 'MNIST_cGAN_results/MNIST_cGAN_generation_animation.gif'> <td><img src = 'MNIST_cDCGAN_results/MNIST_cDCGAN_generation_animation.gif'> </tr> </table>

MNIST vs Generated images

<table align='center'> <tr align='center'> <td> MNIST </td> <td> cGAN after 100 epochs </td> <td> cDCGAN after 30 epochs </td> </tr> <tr> <td><img src = 'MNIST_cGAN_results/raw_MNIST_10.png'> <td><img src = 'MNIST_cGAN_results/MNIST_cGAN_100.png'> <td><img src = 'MNIST_cDCGAN_results/MNIST_cDCGAN_30.png'> </tr> </table>

Training loss

<table align='center'> <tr align='center'> <td> cGAN </td> <td> cDCGAN </td> </tr> <tr> <td><img src = 'MNIST_cGAN_results/MNIST_cGAN_train_hist.png'> <td><img src = 'MNIST_cDCGAN_results/MNIST_cDCGAN_train_hist.png'> </tr> </table>

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.