A-Closer-Look-at-the-Optimization-Landscapes-of-Generative-Adversarial-Networks

We provide a conda environment to run the code: conda create -f mnist-exp_environment.yml

The code for computing the eigenvalues and the path-angle is in plot_path_tools.py.

To run the code for the Mixture of Gaussian experiment: python train_miture_gan.py OUTPUT_PATH --deterministic --saving-stats

To run the code for the MNIST experiment: python train_mnist.py

The visualization of the results can be done with mnist_plots.ipynb