Generative Adversarial Networks for the MNIST dataset
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Latest commit 1b6f427 Apr 24, 2017

MNIST Generative Adversarial Networks (PyTorch)

Sam Greydanus. April 2017. MIT License.


I use the classic MNIST dataset to achieve ultra-simple GAN results. Think of this repo as a lab where you can get comfortable with GANs before trying them on something more complex (e.g. CIFAR, ImageNet).


Vanilla discriminator (D) and generator (G) networks


CNN discriminator (D) and vanilla generator (G) network



  • All code is written in python 3.6. You will need:
  • Numpy
  • matplotlib
  • PyTorch: much easier to write and debug than TensorFlow!
  • Jupyter