/TemperatureGAN

Generative Modelling of Regional Atmospheric Temperatures

Primary LanguagePythonMIT LicenseMIT

TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures

Framework_model.png

Link to paper

Paper

Description of files

  • train.py contains the training module that is initialized in master before the training sessions begins
  • master.py loads the trainer and data to begin training
  • gan.py holds the Discriminator and Generator Architectures for the base models that are trained
  • config contains hyper-params, path to training files and other notes/conditions to be imposed by training session

How to run

Sessions can be run locally or on virtual servers. Colab notebook is still in development and not provided yet.

  • Use requirements.txt to create a conda repository.
  • Simply set the config.json files to point to data files and results directory. Set hyperparameters and set up training session.
  • Go to master.py and run the file.
  • Repository includes pre-trained models that can be used to generate samples. Notebook for steps to do this is work in progress, will update soon.
  • Repository does not include data because data files are large. Find training data here.

Contact

If you have any questions, feel free to contact me at ebalogun[AT]stanford[DOT]edu