I had difficulty understanding how to implement SNGAN and was not able to find resources that does explain the implementation. Hence here I provide a very simplistic implementation on MNIST with only fully connected layers.
python main.py train [options]
--img_size=28 : Size of the images, for MNIST its 28x28
--channels=1 : Number of channels in an image, for MNIST its greyscale images
--data_folder=data : Folder to store the dataset
--samples_folder=data : Folder to store the samples generated during training
--batch_size=128 : Batch size during training
--latent_dim=100 : Size of latent vector that is fed to the generator
--n_cpu=12 : Number of cpu threads to allocate for data processing
--n_critic=5 : Number of iteration to train the discriminator per every one iteration training for generator
--lr=0.01 : Learning-rate of both discriminator and generator, the larger the batch size, the bigger this number can be
--betas=(0.5, 0.9) : Adam optimizers beta hyperparameters
--n_epochs=200 : Maximum number of epochs to train
--sample_interval=500 : Generate samples from generator every `sample_interval` iteration during training.
