The code implement GAN and WGAN and tested them on the MNIST dataset. We also computed the FID and SSIM scores on the generated images.
GAN_params - GAN parameters (dis-params-n is the paramters for the discriminator after n epochs and gen-params-n is the paramters for the generator after n epochs).
plots - Saved plots of loss and discrimiantor output.
saved_loss - Saved losses as NumPy arrays.
WGAN_params - GAN parameters (dis-params-n is the paramters for the discriminator after n epochs and gen-params-n is the paramters for the generator after n epochs).
evaluating_GAN_generator.ipynb - Evaluate GAN using FID and SSIM scores.
evaluating_WGAN_generator.ipynb - Evaluate WGAN using FID and SSIM scores.
GAN.py - Python script to train GAN.
nn_helper.py - Helper file that store the classes for the generator and discriminator and loss functions.
plotting.ipynb - Python notebook for plotting the losses, discriminator outputs, and generated images.
WGAN.py - Python script to train WGAN.