Training is always an adventure—there are so many pitfalls when developing GAN architectures. In this training class, we aim to provide a simple setup to train a GAN that takes a 2D image and creates a 3D model.
This is the final recipe in our chapter, so we've got a few files to create—the train.py, run.py, and run.sh files. Before continuing, check to make sure you have the exact same directory structure in your directory:
├── data
├── docker
│ ├── build.sh
│ ├── clean.sh
│ ├── Dockerfile
│ └── kaggle.json
├── out
├── README.md
├── run_autoencoder.sh
├── run.sh
└── src
├── discriminator.py
├── encoder_model.h5
├── encoder.py
├── gan.py
├── generator.py
├── run.py
├── train.py
├── x_test_encoded.npy
└── x_train_encoded.npy
