Skip to content

Working to understand the importance of physical inputs on classification.

Notifications You must be signed in to change notification settings

laurenhay/CNNsimple

Repository files navigation

CNNSimple

Provides simple toy datasets to test on common deep neural network (DNN) architectures and perform layerwise relevance propagation using iNNvestigate using the provided example notebooks. Notebooks also explore the impact of adding informative variables until the network has an AUC of the ROC of 1.0, and a loss of ~1 (exhausting the phase space of the network).

File Purpose
makeJetImages.ipynb Makes toy "jet" images (based on Jet-Images -- Deep Learning Edition.
fourvec_data.ipynb Makes toy "four vector" data in the format (pt, eta, phi, mass).
CNN_2D.ipynb 2D model that classifies the images as "signal" or "background" is defined and trained.
LRP_CNN2D.ipynb Takes 2D CNN and jet images as input and creates heat maps of the relevant input features.
CNN_1D.ipynb 2D model that classifies the toy vectors as "signal" or "background" is defined and trained.
MLP_multin.ipynb Feed-forward dense network with only the variables used to make the jet images, and not the images themselves as inputs.
deprecated Holds older versions of CNN's and data making scripts.

About

Working to understand the importance of physical inputs on classification.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published