Content

In this Repository we implement the concept of first-order convolutional rules (DCDL) , which are logical rules that can be extracted using a convolutional neural network (CNN).

The complexity of the DCDL approach depends on the size of the convolutional filter and not on the dimensionality of the input. Our approach is based on rule extraction from binary neural networks with stochastic local search (SLS).

Structure of the repository

NN_DCDL_SLS_Blackbox_comparison

compares the DCDL approach with the SLS black box approach and the neural network.

visualize_rules_found Module

Visualizes the rules found with the SLS algorithm.

compare_dither

Implementation of various dither algorithms and test them on the neural net

tests_sls_label_order

Experiment the influence of the performance of the SLS algorithm, if the one-hot-label are inverted from [1,0] to [0,1]. Good starting point, if SLS Algorithm should be used alone.

SLS_Algorithm.py

Interface to the SLS implementation on Python side. Converts lists to binary arays calls C++ code of the SLS implementation.

parallel_sls module

C++ implementation of the SLS algorithm

model module

Structure of the neural networks for the experiments compare_dither and visualize_rules_found.

Structure for storing the extracted logical rules.

install necessary packages

sudo pip install -r requirements.txt

Contact

For questions, comments or bugs please contact jbrugge AT students.uni-mainz.de