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Analytical Bounds on the Local Lipschitz Constants of ReLU Networks

This is the code for the paper:

Analytical Bounds on the Local Lipschitz Constants of ReLU Networks

Trevor Avant & Kristi A. Morgansen, IEEE Transactions on Neural Networks and Learning Systems, 2023

dependencies

  • Python 3 (we used version 3.9), Pytorch (we used version 1.8)

  • the following non-standard python packages: torch, torchvision, numpy, PIL, scipy, tqdm, matplotlib

using GPU or CPU

This code will automatically run on a CUDA-support GPU if you have one, and on a CPU otherwise. However, you can explicitly tell the code to run on the CPU by uncommenting device = 'cpu' in the my_config.py file.

simulations

lipschitz v. epsilon

Run the following commands.

  • python calculate_bounds.py
  • python calculate_bounds_plot.py

Note that to change the network, change import networks.mnist as exp to something different. Also note that the random and gradient methods may take a long time to run. You can toggle whether these methods are run by chagning the compute_rand and compute_grad variables.

adversarial bounds:

Run the following command:

  • python calculate_bounds_adv.py

Note that to change the network, change import networks.mnist as exp to something different.

minimal working example / using your own network

A minimal working example showing how to compute the local Lipschitz bound of a feedforward network, and how to use that result to compute an adversarial bound, is shown in the simple_example.py file.

comparison with other methods

We compare our method to several other methods to compute/bound/estimate Lipschitz constants. The network we use is contained in the file networks/compnet.py. We were not able to test on LiPopt (Latorre et al., 2020) because it does not support networks with bias.

  • Our Method: Run $ python networks/compnet.py.

  • SeqLip (Scaman & Virmaux, 2018): Run other_methods/lipestimation/custom_get_sv.py with the correct experiment name uncommented (e.g. exp = 'alexnet'). Then run other_methods/lipestimation/custom.py with the same network uncommented.

  • LipSDP (Fazlyab et al., 2019): The original implementation of LipSDP uses Matlab. However, we will use a Python implementation which we wrote, which we have previously verified to produce the same results as the original Matlab version. First, run $ python other_methods/make_weight_file.py to generate the weight file. Then run $ python other_methods/lipsdp_python/solve_sdp.py --form network --weight-path data/compnet/lipsdp/weights.mat to produce the estimate.

  • lipMIP (Jordan & Dimakis, 2020): First, you need to install Gurobi (free for students) and the gurobipy Python package. Then run python other_methods/lipMIP/run.py.

  • RecurJac (Zhang et al., 2019): This method requires Tensorflow. First, convert the Pytorch model to a Tensorflow model and save it by running $ python other_methods/RecurJac-and-CROWN/convert_and_save_model.py. Then, run $ python other_methods/RecurJac-and-CROWN/run.py to calculate the Lipschitz constant.

extra

The tests/ directory contains some additional scripts. These scripts include brute-force checks on some of the analytical results, as well as checks on some of our computational methods.