Pytorch implementation of Generative Adversarial Networks
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# pytorch-MNIST-CelebA-GAN-DCGAN
Pytorch implementation of Generative Adversarial Networks (GAN) [1] and Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for MNIST [3] and CelebA [4] datasets.
* If you want to train using cropped CelebA dataset, you have to change isCrop = False to isCrop = True.
* you can download
- MNIST dataset: http://yann.lecun.com/exdb/mnist/
- CelebA dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
* pytorch_CelebA_DCGAN.py requires 64 x 64 size image, so you have to resize CelebA dataset (celebA_data_preprocess.py).
* pytorch_CelebA_DCGAN.py added learning rate decay code.
## Implementation details
* GAN
* DCGAN
## Resutls
### MNIST
文 件 列 表
pytorch-MNIST-CelebA-GAN-DCGAN-master
CelebA_DCGAN_crop_results
CelebA_DCGAN_results
MNIST_DCGAN_results
MNIST_GAN_results
README.md
celebA_data_preprocess.py
pytorch_CelebA_DCGAN.py
pytorch_DCGAN.png
pytorch_GAN.png
pytorch_MNIST_DCGAN.py
pytorch_MNIST_GAN.py
