Deep Jointly-Informed Neural Networks

DJINN: Deep jointly-informed neural networks

DJINN is an easy-to-use algorithm for training deep neural networks on supervised regression tasks. For additional information, refer to the paper "Deep neural network initialization with decision trees", cited below.

Getting Started

DJINN requires TensorFlow (v1.0.1 or later) and
scikit-learn (v0.18 or later is recommended). DJINN also uses numpy, matplotlib, and cPickle. Sphinx is required to view the html documentation.

Note that the sklearn version used when training a DJINN model must be the same version used when reloading/evaluating the saved model.

To use DJINN, clone the repo and install:

$ git clone https://github.com/LLNL/DJINN.git
$ cd DJINN
$ pip install -r requirements.txt
$ pip install .

Try it out! Examples for training DJINN models are included in the tests folder.

-python djinn_example.py (single output)

-python djinn_multiout_example.py (multiple outputs)

For Mac users with Anaconda installs, it might be necessary to manually install matplotlib via pip:

$ pip install matplotlib

If matplotlib will not import, try running "pythonw", for example:

$ pythonw djinn_example.py

Python3 and Tensorflow2 There is a branch (tf2-py3) that offers a python3 friendly version of djinn that runs on tensorflow2. You can make the tensorflow1 version of djinn python3 friendly by modifying the "djinn.py" script by changing: $ from djinn_fns to $ from .djinn_fns

Documentation

To view the DJINN documentation:

cd docs
make html

Open docs/_build/html/index.html in a browser

Source Repo

DJINN is available at https://github.com/LLNL/DJINN

Citing DJINN

If you use DJINN in your research, please cite the following paper:

K. D. Humbird, J. L. Peterson and R. G. Mcclarren, "Deep Neural Network Initialization With Decision Trees," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 5, pp. 1286-1295, May 2019. doi: 10.1109/TNNLS.2018.2869694, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8478232&isnumber=8695188

Release

Copyright (c) 2018, Lawrence Livermore National Security, LLC.

Produced at the Lawrence Livermore National Laboratory

Written by K. Humbird (humbird1@llnl.gov), L. Peterson (peterson76@llnl.gov).

LLNL-CODE-754815 OCEC-18-117

All rights reserved.

Unlimited Open Source- BSD Distribution.

For release details and restrictions, please read the RELEASE, LICENSE, and NOTICE files, linked below:

• RELEASE
• LICENSE
• NOTICE