Project aim:
Build Generative Adversarial Neural networks to produce images of matter distribution in the universe for different sets of cosmological parameters.
Dataset consisits of N-body cosmology simulations.
The code is built using the LBANN framework.
This has been developed using codes in keras and tensorflow. An earlier version of the code in lbann can be found here.
Data
The original data can be extracted from here.
A subset can be accessed here.
Description of different folders in repo
- 0a_data_preprocessing: Codes for obtaining slices from the original hdf5 files.
- 0b_view_data: Codes to view sample images and explore normalization functions of input images.
- 1_train: Contains the scripts to train the model and batch scripts to run it.
- 3_analysis: Contains scripts that analyze the produced images.
Each folder contains a jupyter notebook to quickly test the code, a folder with the full code, a launch script to run the code on cori GPUs at NERSC, a script to perform post-run computation of metrics for different stored images and a folder with analysis codes to inspect the performance of the code. Below is an example for 2D GAN
| Name | Description |
|---|---|
| 1_train/main_code/ | Folder containing main training and inference code |
| 1_train/run_scripts/launch_lbann_train.ipynb | Notebook that launches script to run training |
| 1_train/run_scripts/launch_lbann_compute_chisqr.ipynb | Notebook that launches script to run post-run metric computation |
| 3_analysis/3a_analysis_pandas.py | Notebook to analyze GAN results and view best epoch-steps |