分类预测 | Matlab实现DRN深度残差网络数据分类预测

分类预测 | Matlab实现DRN深度残差网络数据分类预测

目录

• • 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
    • 程序设计
    • 参考资料

      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);
        

        参考资料

        [1] http://t.csdn.cn/pCWSp

        [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