To run training procedure type in terminal following command:
python training.py --datafile ./data/showers_all_energies_1_5.pkl --max_prev_node 12 --embedding_size 196 --edge_rnn_embedding_size 16
--embedding_size_gcn 4 --num_layers_gcn 3 --mixture_size 12 --lr 1e-4
--project_name shower_generation --work_space schattengenie
Weights of neural networks will be saved on disk each 10 epochs.
--max_prev_node 12 --embedding_size 196 --edge_rnn_embedding_size 16
are architecture parameters of GraphRNN, please refer to original paper.
--embedding_size_gcn 4 --num_layers_gcn 3 --mixture_size 12
define Graph Convolution architecture.
--embedding_size_gcn
corresponds for the dense layer size in EdgeConv layer, --num_layers_gcn 3
corresponds for number of EdgeConv layers used and --mixture_size
define number of Gaussian distributions proposed by Mixture Density Network.
To generate EM-showers open in Jupyter Notebook
showers_generation_vizualizations.ipynb
file. Enter correct path to network weights and you will be able generate new showers.