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All Projects → last-one → DCGAN-Pytorch

last-one / DCGAN-Pytorch

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A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

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DCGAN in Pytorch

PyTorch implementation of Deep Convolutional Generative Adversarial Networks (DCGAN), which is a stabilize Generative Adversarial Networks. The origin code can be found here.

Network architecture

alt tag

  • Generator

    • input: a vector with z_size.
    • hidden layers: Four 4x4 transposed convolutional layers (1024, 512, 256, and 128 kernels, respectively) with ReLU
    • output layer: 4x4 transposed convolutional layer (channel_size kernels, 4096 nodes = 64x64 size image) with Tanh.
    • BatchNormalization is used except for output layer.

  • Discriminator

    • input: a vector with channel_size * image_size * image_size.
    • hidden layers: Four 4x4 convolutional layers (128, 256, 512, and 1024 kernels, respectively) with LeakyReLU (negative slope is 0.2).
    • output layer: 4x4 convolutional layer (1 node) with Sigmoid.
    • BatchNormalization is used except for 1st hidden layer and output layer.

All of the transposed convolutional layer and convolutional layer are initilized by a normal distribution with 0.0 mean and 0.02 std.

MNIST dataset

  • For MNIST image, the channel_size is 1 and image_size is 64.

Results

  • The learning rate is 0.0002, batch size is 128 and the optimizer is Adam.
Gnerated Images DCGAN Loss

CelebA dataset

  • For CelebA image, the channel_size is 3 and image_size is 180 x 180, which has been aligned and cropped. And then, it will be resized to 64 x 64.

Results

  • The learning rate is 0.0002, batch size is 128 and the optimizer is Adam.
Gnerated Images DCGAN Loss

LSUN dataset

  • For LSUN image, the channel_size is 3. And then, it will be resized to 64 x 64.

Results

  • The learning rate is 0.0002, batch size is 1024 and the optimizer is Adam.
Gnerated Images DCGAN Loss

References

  1. https://github.com/soumith/dcgan.torch
  2. https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
  3. https://github.com/togheppi/DCGAN
  4. https://github.com/carpedm20/DCGAN-tensorflow
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