This repository contains code for constructing various neural networks to classify particles from the Micky et. al. dataset based on energy deposition. The map below provides a brief explanation of each file, and a summary of the notebooks that were important to our project's development. An in-depth discussion of the network architectures can be found in the paper "Classifying Electromagnetic Showers from Calorimeter Images with CNNs", by Nicole Hartman and Sean Mullane.
Main Branch:
-dataProcessing.py --> code to organize dataset into layers for use during training/testing
-imageNumber_analysis.py --> functions to test how the total number of training images affects the validation accuracy
-learn_rate_optimize.py --> code to optimize the learning rate for a given model
-models.py --> central file for all models used in the project
-plottingFcts.py --> central file for all plotting functions used in the project
-train.py --> training code, largely taken from CS231N homeworks
models folder: contains stored models from various training notebooks
figures folder: contains saved figures from the model visualization notebooks
Notebooks folder: central folder for all iPython notebooks used in the project. Includes:
-Data-Processsing.ipynb --> initial notebook to test data processing functions
-Ensembling.ipynb --> an attempt at ensembling/average models to asses overall performance
-Final_Models_testSet.ipynb --> notebook that ran the test set on the final 2d RCNN models
-Final_Test_Vis.ipynb --> notebook that ran visualization functions on the final 2d RCNN models
-Visualizations.ipynb --> notebook that ran visualization functions on the final 3d CNN models
3d_models folder: contains notebooks that were used to construct and tune the 3d CNN models
Baseline_CNN_models: contains notebooks that were used to construct and tune the baseline CNN models
Initial_Notebooks: contains notebooks that were used at the beginning of our project to explore the dataset and construct the first fully connected networks
Recurrent_CNN_Models: contains notebooks that were used to construct and tune the 2d RCNN models
Authors: Nicole Hartman and Sean Mullane, Stanford University Department of Physics
Most esteemed mentor: Michael Kagan, SLAC/CERN-ATLAS
Our starter code was only taken from CS231N assignments. This included the check_accuracy, check_loss, and train functions in train.py (the function train_ThreeCNN, while structured on train, was not explicitly taken from the class).
We will be using the jet images of the showers in the EM calorimeter produced by Micky, et. al. https://data.mendeley.com/datasets/pvn3xc3wy5/1