With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified linear unit (ReLU) activation function

Related tags

Deep Learningpythonmachine-learningoptimizationrelu-activation
Overview

reluMIP

Embed ReLU neural networks into mixed-integer programs.

About

With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified liner unit (ReLU) activation function. At the moment, only TensorFlow sequential models are supported. Interfaces to either the Pyomo or Gurobi modelling environments are offered.

ReLU ANNs can be used to approximate complex functions from data. In order to embed these functions into optimization problems, strong formulations of the network are needed. This package employs progressive bound tightening procedures to produce MIP encodings for ReLU networks. This allows the user to embed complex and nonlinear functions into mixed-integer programs. Note that the training of ReLU ANNs is not part of this package and has to be done by the user beforehand. A number of illustrative examples are provided to showcase the functionality of this package. For more detailed information, see the slide show in the docs/ folder of the reluMIP Git repository.

Installation

This package is part of PyPI. It can be installed through pip:

pip install reluMIP

After installing, you can use the examples provided in the examples/ folder to become familiar with the package.

Alternatively, you can clone the github repository:

git clone https://github.com/ChemEngAI/ReLU_ANN_MILP.git

You can install all requirements from the project root folder by calling:

pip install -r requirements.txt

You can add the root folder of the repository to your PYTHON_PATH, so that the package can be accessed from anywhere.

Note that in order to use the package, a compatible solver has to be installed. This can be Gurobi (with a valid license) or any MIP solver compatible with Pyomo (we recommend glpk). In our experience, the best performance is obtained when using the Gurobi interface.

Example usages

Two jupyter notebooks describing the use of the package are supplied in the examples/ folder of the Git repository. There, an MIP formulation of a ReLU ANN - trained on a nonliner, nonconvex function - is used to find the global minimum of the network response surface. If you installed the package through pip, you can simply download the example files that you are interested in.

Related work on NLP formulations of ANNs

In this tool, ReLU ANNs are formulated as MILPs. Notably, ANNs can also be formulated as nonlinear problems (NLPs) and solved through deterministic gloabl optimization (Schweidtmann and Mitsos (2019)). Please visit the MeLOn toolbox for more information.

How to cite this work

Please cite our Zenodo DOI if you use this code:

@software{reluMIP.2021,
  title={reluMIP: Open Source Tool for MILP Optimization of ReLU Neural Networks},
  author={Lueg, Laurens and Grimstad, Bjarne and Mitsos, Alexander and Schweidtmann, Artur M.},
  year={2021},
  doi={https://doi.org/10.5281/zenodo.5601907},
  url = {https://github.com/ChemEngAI/ReLU_ANN_MILP},
  version = {1.0.0}
}

References

Grimstad, B., Andersson, H. (2019). ReLU networks as surrogate models in mixed-integer linear programs. Computers & Chemical Engineering (Volume 131, 106580).

Schweidtmann, A. M., & Mitsos, A. (2019). Deterministic global optimization with artificial neural networks embedded. Journal of Optimization Theory and Applications (Volume 180(3), 925-948).

You might also like...
Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

ReLU-GP Residual (RGPR) This repository contains code for reproducing the following NeurIPS 2021 paper: @inproceedings{kristiadi2021infinite, title=

Agustinus Kristiadi 4 Dec 26, 2021
Unofficial pytorch-lightning implement of Mip-NeRF
Unofficial pytorch-lightning implement of Mip-NeRF

mipnerf_pl Unofficial pytorch-lightning implement of Mip-NeRF, Here are some results generated by this repository (pre-trained models are provided bel

Jianxin Huang 159 Dec 23, 2022
Source for the paper "Universal Activation Function for machine learning"

Universal Activation Function Tensorflow and Pytorch source code for the paper Yuen, Brosnan, Minh Tu Hoang, Xiaodai Dong, and Tao Lu. "Universal acti

null 4 Dec 3, 2022
Implementation of parameterized soft-exponential activation function.
Implementation of parameterized soft-exponential activation function.

Soft-Exponential-Activation-Function: Implementation of parameterized soft-exponential activation function. In this implementation, the parameters are

Shuvrajeet Das 1 Feb 23, 2022
School of Artificial Intelligence at the Nanjing University (NJU)School of Artificial Intelligence at the Nanjing University (NJU)

F-Principle This is an exercise problem of the digital signal processing (DSP) course at School of Artificial Intelligence at the Nanjing University (

Thyrix 5 Nov 23, 2022
Linear algebra python - Number of operations and problems in Linear Algebra and Numerical Linear Algebra

Linear algebra in python Number of operations and problems in Linear Algebra and

Alireza 5 Oct 9, 2022
This Artificial Intelligence program can take a black and white/grayscale image and generate a realistic or plausible colorized version of the same picture.

Colorizer The point of this project is to write a program capable of taking a black and white / grayscale image, and generating a realistic or plausib

Maitri Shah 1 Jan 6, 2022
EdMIPS: Rethinking Differentiable Search for Mixed-Precision Neural Networks

EdMIPS is an efficient algorithm to search the optimal mixed-precision neural network directly without proxy task on ImageNet given computation budgets. It can be applied to many popular network architectures, including ResNet, GoogLeNet, and Inception-V3.

Zhaowei Cai 47 Dec 30, 2022
The tool under this branch fork can be used to crack devices above A12 and up to A15. After cracking, you can also use SSH channel strong opening tool to open SSH channel and activate it with Demo or Shell script. The file can be extracted from my Github homepage, and the SSH channel opening tool can be extracted from Dr238 account.

Welcome to C0xy-A12-A15-Attack-Tool The tool under this branch fork can be used to crack devices above A12 and up to A15. After cracking, you can also

null 13 Dec 23, 2022
Releases(v1.0.0)
Owner
ChemEngAI
We bridge chemical engineering and artificial intelligence.
ChemEngAI
GitHub Repository
Efficient neural networks for analog audio effect modeling

micro-TCN Efficient neural networks for audio effect modeling

Christian Steinmetz 94 Dec 29, 2022
A visualisation tool for Deep Reinforcement Learning

DRLVIS - Visualising Deep Reinforcement Learning Created by Marios Sirtmatsis with the support of Alex Bäuerle. DRLVis is an application used for visu

Marios Sirtmatsis 1 Nov 04, 2021
Data and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"

Reproducibility materials for "Importance of Kernel Bandwidth in Quantum Machine Learning" Repo structure: code contains Python scripts used to genera

Ruslan Shaydulin 3 Oct 23, 2022
A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

Imaging Transcriptomics Imaging transcriptomics is a methodology that allows to identify patterns of correlation between gene expression and some prop

Alessio Giacomel 10 Dec 27, 2022
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

174 Dec 19, 2022
Proposed n-stage Latent Dirichlet Allocation method - A Novel Approach for LDA

n-stage Latent Dirichlet Allocation (n-LDA) Proposed n-LDA & A Novel Approach for classical LDA Latent Dirichlet Allocation (LDA) is a generative prob

Anıl Güven 4 Mar 07, 2022
ThunderSVM: A Fast SVM Library on GPUs and CPUs

What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss

Xtra Computing Group 1.4k Dec 22, 2022
The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

This repository is the official PyTorch implementation of SAINT. Find the paper on arxiv SAINT: Improved Neural Networks for Tabular Data via Row Atte

Gowthami Somepalli 284 Dec 21, 2022
Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation"

Implicit-Semantic-Response-Alignment Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation" Prerequisites pyt

4 Dec 19, 2022
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte

Zhuang Liu 4.5k Jan 03, 2023
Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

Philipp Erler 329 Jan 06, 2023
Discovering Interpretable GAN Controls [NeurIPS 2020]

GANSpace: Discovering Interpretable GAN Controls Figure 1: Sequences of image edits performed using control discovered with our method, applied to thr

Erik Härkönen 1.7k Jan 03, 2023
Implementation for "Domain-Specific Bias Filtering for Single Labeled Domain Generalization"

DSBF Introduction This repository contains the implementation code for paper: Domain-Specific Bias Filtering for Single Labeled Domain Generalization

ScottYuan 7 Jan 05, 2023
Evolving neural network parameters in JAX.

Evolving Neural Networks in JAX This repository holds code displaying techniques for applying evolutionary network training strategies in JAX. Each sc

Trevor Thackston 6 Feb 12, 2022
Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch

PyTorch implementation of Continuous Augmented Positional Embeddings (CAPE), by Likhomanenko et al. Enhance your Transformer positional embeddings with easy-to-use augmentations!

Guillermo Cámbara 26 Dec 13, 2022
Voice control for Garry's Mod

WIP: Talonvoice GMod integrations Very work in progress voice control demo for Garry's Mod. HOWTO Install https://talonvoice.com/ Press https://i.imgu

Meta Construct 5 Nov 15, 2022
Hepsiburada - Hepsiburada Urun Bilgisi Cekme

Hepsiburada Urun Bilgisi Cekme from hepsiburada import Marka nike = Marka("nike"

Ilker Manap 8 Oct 26, 2022
Official Pytorch implementation of MixMo framework

MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks Official PyTorch implementation of the MixMo framework | paper | docs Alexandr

79 Nov 07, 2022
Embodied Intelligence via Learning and Evolution

Embodied Intelligence via Learning and Evolution This is the code for the paper Embodied Intelligence via Learning and Evolution Agrim Gupta, Silvio S

Agrim Gupta 111 Dec 13, 2022
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search DropNAS, a grouped operation dropout method for one-level DARTS, with better

weijunhong 4 Aug 15, 2022