Train a convolutional neural network and analyze the gradients in the model to identify relationships in high dimensional spaces.
Code primarily written by Mitustaro Umehara with conttributions from Dan Guevarra and Helge Stein. Algorithm development by Mitustaro Umehara, Dan Guevarra, Helge Stein, and John Gregoire. For use in scientific publications, please cite the journal article describing the algorithm and its performance:
Code has been tested on 64-bit Windows with Python 3.6, using a virtual environment created with conda 4.5.11 from the open source Anaconda Distribution available at: https://www.anaconda.com/
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After Anaconda installation, launch an "Anaconda Prompt" and clone a local copy of the git repository.
git clone https://github.com/johnmgregoire/CNN_Gradient_Analysis.git -
Run the following command to create a clean environment with the required packages.
conda create -n myenv python=3 numpy matplotlib pandas scipy scikit-learn tensorflow keras jupyter -
Activate the virtual environment.
conda activate myenv -
Launch a local (by default) Jupyter server instance to view and run the
code.ipynbnotebook file located in the repository folder.jupyter notebook
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