分类预测 | Matlab实现DRN深度残差网络数据分类预测

04-10 1609阅读

分类预测 | Matlab实现DRN深度残差网络数据分类预测

分类效果

基本介绍

1.Matlab实现DRN深度残差网络数据分类预测（完整源码和数据），运行环境为Matlab2023及以上。

2.多特征输入单输出的二分类及多分类模型。程序内注释详细，直接替换excel数据就可以用;

3.程序语言为matlab，程序可出分类效果图，迭代优化图，混淆矩阵图。

4.代码特点：参数化编程、参数可方便更改、代码编程思路清晰、注释明细。

程序设计

完整程序和数据获取方式资源处直接下载Matlab实现DRN深度残差网络数据分类预测（完整源码和数据）。

%%  清空环境变量
warning off             % 关闭报警信息
close all               % 关闭开启的图窗
clear                   % 清空变量
clc                     % 清空命令行
lgraph = layerGraph();
%% Add Layer Branches
% Add the branches of the network to the layer graph. Each branch is a linear 
% array of layers.
tempLayers = [
    imageInputLayer(inputshape,"Name","input")
    convolution2dLayer([7 7],64,"Name","conv1","Padding",[3 3 3 3],"Stride",[2 2])
    batchNormalizationLayer("Name","bn_conv1","Epsilon",0.001)
    reluLayer("Name","activation_1_relu")
    maxPooling2dLayer([3 3],"Name","max_pooling2d_1","Padding",[1 1 1 1],"Stride",[2 2])];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    convolution2dLayer([1 1],256,"Name","res2a_branch1","BiasLearnRateFactor",0)
    batchNormalizationLayer("Name","bn2a_branch1","Epsilon",0.001)];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    convolution2dLayer([1 1],64,"Name","res2a_branch2a","BiasLearnRateFactor",0)
    batchNormalizationLayer("Name","bn2a_branch2a","Epsilon",0.001)
    reluLayer("Name","activation_2_relu")
    convolution2dLayer([3 3],64,"Name","res2a_branch2b","BiasLearnRateFactor",0,"Padding","same")
    batchNormalizationLayer("Name","bn2a_branch2b","Epsilon",0.001)
    reluLayer("Name","activation_3_relu")
    convolution2dLayer([1 1],256,"Name","res2a_branch2c","BiasLearnRateFactor",0)
    batchNormalizationLayer("Name","bn2a_branch2c","Epsilon",0.001)];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    additionLayer(2,"Name","add_1")
    reluLayer("Name","activation_4_relu")];
lgraph = addLayers(lgraph,tempLayers);