/DL-HEP

Primary LanguagePython

DL-HEP

Note: all python scripts should be run from the root directory of this repository.

In order to start training the model, the data must first be fetched and put in the data directory. Afterwards, generate the subsets for training and test by running: python data/preprocess_data.py This script will also split each dataset based on their number of the PRI_jet_num feature.

Inside scripts/models the following scripts are available:

  • autoencoder_tf.py and autoencoder_keras.py: Instances an autoencoder using the respective deep learning framework. Note that the TensorFlow version was used in this work and this is reflected in the train scripts.
  • variational_autoencoder_tf.py and variational_autoencoder_keras.py. Instances a variational autoencoder.
  • gan.py. Contains code for instancing the generator and discriminator models of a GAN. These are actually combined with the keras_adversarial python package that can be found here on GitHub (it's also a git submodule in this repository).

These models are instanced in their respective training scripts in:

  • models/autoencoder
  • models/variational_autoencoder
  • models/gan

Additional code is available in the scripts/feature_selection to plot distributions for each feature.