This repository implements training and inference methods of GAN with just fc layers on MNIST.

For setting up the mnist dataset:

Follow - https://github.com/explainingai-code/Pytorch-VAE#data-preparation

The directory structure should look like this

$REPO_ROOT -> data -> train -> images -> 0 *.png -> 1 ... -> 9 *.png -> test -> images -> 0 *.png ... -> dataset -> tools 

• Create a new conda environment with python 3.8 then run below commands
• git clone https://github.com/explainingai-code/GANs-Pytorch.git
• cd GANs-Pytorch
• pip install -r requirements.txt
• python -m tools.train_gan for training and saving inference samples

• Ensure dataset is prepared according to the Data Preparation instructions
• Change the IM_CHANNELS field to 3 in train_gan.py
• Uncomment lines 56-59 in the dataset/mnist_dataset.py file

• Dump all *.png files(or whatever format images you have) in the path data/train/images
• Comment https://github.com/explainingai-code/GANs-Pytorch/blob/main/dataset/mnist_dataset.py#L43
• Directory structure should be following:

data -> train -> images *.png 

• Change the IM_PATH field to data/train in train_gan.py
• Change the channels and image sizes accordingly

Outputs will be saved every 50 steps in samples directory .

During training of GAN the following output will be saved

• Latest Model checkpoints for generator and discriminator in $REPO_ROOT directory

During inference every 50 steps the following output will be saved

• Sampled image grid for in samples/*.png

@misc{goodfellow2014generative, title={Generative Adversarial Networks}, author={Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio}, year={2014}, eprint={1406.2661}, archivePrefix={arXiv}, primaryClass={stat.ML} }