# onsager_deep_learning

**Repository Path**: mirrors_mborgerding/onsager_deep_learning

## Basic Information

- **Project Name**: onsager_deep_learning
- **Description**: Code to accompany the paper "AMP-Inspired Deep Networks for Sparse Linear Inverse Problems"
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-09
- **Last Updated**: 2025-09-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# What is this?

This project contains scripts to reproduce experiments from the paper
[AMP-Inspired Deep Networks for Sparse Linear Inverse Problems](http://ieeexplore.ieee.org/document/7934066/)
by 
[Mark Borgerding](mailto://borgerding.7@osu.edu)
,
[Phil](mailto://schniter.1@osu.edu)
[Schniter](http://www2.ece.ohio-state.edu/~schniter)
, and [Sundeep Rangan](http://engineering.nyu.edu/people/sundeep-rangan).
To appear in IEEE Transactions on Signal Processing.
See also the related [preprint](https://arxiv.org/pdf/1612.01183)

# The Problem of Interest

Briefly, the _Sparse Linear Inverse Problem_ is the estimation of an unknown signal from indirect, noisy, underdetermined measurements by exploiting the knowledge that the signal has many zeros.  We compare various iterative algorithmic approaches to this problem and explore how they benefit from loop-unrolling and deep learning.

# Overview

The included scripts 
- are generally written in python and require [TensorFlow](http://www.tensorflow.org),
- work best with a GPU,
- generate synthetic data as needed,
- are known to work with CentOS 7 Linux and TensorfFlow 1.1,
- are sometimes be written in octave/matlab .m files.

##  If you are just looking for an implementation of VAMP ...

You might prefer the Matlab code in [GAMP](https://sourceforge.net/projects/gampmatlab/)/code/VAMP/ 
or the python code in [Vampyre](https://github.com/GAMPTeam/vampyre).

# Description of Files

## [save_problem.py](save_problem.py) 

Creates numpy archives (.npz) and matlab (.mat) files with (y,x,A) for the sparse linear problem y=Ax+w.
These files are not really necessary for any of the deep-learning scripts, which generate the problem on demand.
They are merely provided for better understanding the specific realizations used in the experiments.

## [ista_fista_amp.m](ista_fista_amp.m)

Using the .mat files created by save_problem.py, this octave/matlab script tests the performance of non-learned algorithms ISTA, FISTA, and AMP.

e.g.
```
octave:1> ista_fista_amp
loaded Gaussian A problem
AMP reached NMSE=-35dB at iteration 25
AMP terminal NMSE=-36.7304 dB
FISTA reached NMSE=-35dB at iteration 202
FISTA terminal NMSE=-36.7415 dB
ISTA reached NMSE=-35dB at iteration 3420
ISTA terminal NMSE=-36.7419 dB
```

## [LISTA.py](LISTA.py)

This is an example implementation of LISTA _Learned Iterative Soft Thresholding Algorithm_ by (Gregor&LeCun, 2010 ICML).

## [LAMP.py](LAMP.py)

Example of Learned AMP (LAMP) with a variety of shrinkage functions.

## [LVAMP.py](LVAMP.py)

Example of Learned Vector AMP (LVAMP).