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All Projects → znxlwm → Tensorflow Mnist Cgan Cdcgan

znxlwm / Tensorflow Mnist Cgan Cdcgan

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

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tensorflowgangenerative-adversarial-networkmnist

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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.

Implementation details

  • cGAN

GAN

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.

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