RobustNeuralNetworks.jl

A Julia package for robust neural networks built from the Recurrent Equilibrium Network (REN) and Lipschitz-Bounded Deep Network (LBDN) model classes. Please visit the docs page for detailed documentation

To install the package, type the following into the REPL.

] add RobustNeuralNetworks

You should now be able to construct robust neural network models. The following example constructs a contracting REN and evalutates it given a batch of random initial states x0 and inputs u0.

using Random
using RobustNeuralNetworks

# Setup
rng = Xoshiro(42)
batches = 10
nu, nx, nv, ny = 4, 2, 20, 1

# Construct a REN
contracting_ren_ps = ContractingRENParams{Float64}(nu, nx, nv, ny; rng)
ren = REN(contracting_ren_ps)

# Some random inputs
x0 = init_states(ren, batches; rng)
u0 = randn(rng, ren.nu, batches)

# Evaluate the REN over one timestep
x1, y1 = ren(x0, u0)

println(round.(y1;digits=2))

The output should be:

[-1.49 0.75 1.34 -0.23 -0.84 0.38 0.79 -0.1 0.72 0.54]

If you use RobustNeuralNetworks.jl for any research or publications, please cite our work as necessary.

@article{barbara2023robustneuralnetworksjl,
   title   = {RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness},
   author  = {Nicholas H. Barbara and Max Revay and Ruigang Wang and Jing Cheng and Ian R. Manchester},
   journal = {arXiv preprint arXiv:2306.12612},
   month   = {6},
   year    = {2023},
   url     = {https://arxiv.org/abs/2306.12612v1},
}

Please contact Nic Barbara (nicholas.barbara@sydney.edu.au) with any questions.