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Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

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tensorflow-MNIST-GAN-DCGAN

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

Implementation details

  • GAN

GAN

  • DCGAN

Loss

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

  • 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.

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Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

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