分类预测 | Matlab实现DRN深度残差网络数据分类预测
目录
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）。
clike
%% 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);参考资料
[2] https://download.csdn.net/download/kjm13182345320/87568090?spm=1001.2014.3001.5501
[3] https://blog.csdn.net/kjm13182345320/article/details/129433463?spm=1001.2014.3001.5501