Calorimeter Shower ID with Deep learning

Steps

1. Download the data from here. Save the individual files to a directory, say, /path/to/data (you now should have /path/to/data/{gammma, eplus, piplus}.hdf5).
2. Edit the configuration file config.json (or make a copy) to point to this directory, and edit the config to point to a location where you want model metadata & logging to occur (say, /path/to/save/things).
3. From the directory, run python trainer.py config.json, and profit!

Requirements

Just run pip install -r requirements.txt (or, pip install -r requirements-gpu.txt if you've got a CUDA-enabled graphics card).

[update 10/13/20] This requires some old software, so please consider using a virtual environment. The specific versions of Keras and TensorFlow matter.

We recommend using Python 3. If you need to use Python 2, please downgrade the TensorFlow version to 1.15.0.

• Keras==2.0.8
• Keras-contrib (from our fork, on branch densenet-mod)
• Pandas
• Numpy
• Scikit learn
• h5py
• TensorFlow<=1.15.4 (make sure to install the GPU version if you can)