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vanilla_GAN

PyTorch implementation of Vanilla GAN

1D Gaussian pdf approximation

Results

  • For mu = 0.0, sigma = 1.0:

  • For mu = 1.0, sigma = 1.5:

References

  1. http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
  2. http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html
  3. https://github.com/hwalsuklee/tensorflow-GAN-1d-gaussian-ex

Generating MNIST dataset

Network architecture

  • Generator

    • hidden layers: Three fully-connected (256, 512, and 1024 nodes, respectively), Leaky ReLU activation
    • output layer: Fully-connected (784 nodes), Tanh activation
  • Discriminator

    • hidden layers: Three fully-connected (1024, 512, and 256 nodes, respectively), Leaky ReLU activation
    • output layer: Fully-connected (1 node), Sigmoid activation
    • Dropout: dropout probability = 0.3

Results

  • For learning rate = 0.0002 (Adam optimizer), batch size = 128, # of epochs = 100:
GAN losses Generated images

References

  1. https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
  2. https://github.com/moono/moo-dl-practice/tree/master/Work-place/GAN-MNIST
  3. https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/generative_adversarial_network