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  • 2022-06-06 11:41
matlab中存档算法代码这是什么? 该项目包含脚本,用于通过,和复制论文中的实验。 出现在“ IEEE信号处理事务”中。 另请参阅相关 利益问题 简而言之,稀疏线性逆问题是通过利用信号具有很多零的知识来估计来自间接,嘈杂,不确定性测量的未知信号。 我们比较了针对此问题的各种迭代算法方法,并探讨了它们如何从循环展开和深度学习中受益。 概述 包含的脚本 通常是用python编写的,并且require, 与GPU搭配使用效果最佳, 根据需要生成综合数据, 已知可与CentOS 7 Linux和TensorfFlow 1.1一起使用, 有时是用octave / matlab .m文件编写的。 如果您只是在寻找VAMP的实现... 您可能更喜欢/ code / VAMP /中的Matlab代码或中的python代码。 文件说明 针对稀疏线性问题y = Ax + w,创建具有(y,x,A)的numpy存档(.npz)和matlab(.mat)文件。 这些文件对于任何深度学习脚本并不是真正必需的,这些脚本会按需生成问题。 提供它们只是为了更好地理解实验中使用的特定实现。 使用save_proble
# What is this? This project contains scripts to reproduce experiments from the paper [AMP-Inspired Deep Networks for Sparse Linear Inverse Problems]( by [Mark Borgerding](mailto:// , [Phil](mailto:// [Schniter]( , and [Sundeep Rangan]( To appear in IEEE Transactions on Signal Processing. See also the related [preprint]( # 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](, - 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]( or the python code in [Vampyre]( # Description of Files ## []( 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, 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 ``` ## []( This is an example implementation of LISTA _Learned Iterative Soft Thresholding Algorithm_ by (Gregor&LeCun, 2010 ICML). ## []( Example of Learned AMP (LAMP) with a variety of shrinkage functions. ## []( Example of Learned Vector AMP (LVAMP).