matlab神经网络评价指标（基于MATLAB的人工神经网络ANN回归代码）

  在之前的文章基于MATLAB的随机森林RF回归与变量重要性影响程度排序代码中，我们对基于MATLAB的随机森林（RF）回归与变量影响程度（重要性）排序的代码加以详细讲解与实践。本次我们继续基于MATLAB，对另一种常用的机器学习方法——神经网络方法加以代码实战。

  首先需要注明的是，在MATLAB中，我们可以直接基于“APP”中的“Neural Net Fitting”工具箱实现在无需代码的情况下，对神经网络算法加以运行：

  基于工具箱的神经网络方法虽然方便，但是一些参数不能调整；同时也不利于我们对算法、代码的理解。因此，本文不利用“Neural Net Fitting”工具箱，而是直接通过代码将神经网络方法加以运行——但是，本文的代码其实也是通过上述工具箱运行后生成的；而这种生成神经网络代码的方法也是MATLAB官方推荐的方式。

  另外，需要注意的是，本文直接进行神经网络算法的执行，省略了前期数据处理、训练集与测试集划分、精度衡量指标选取等。因此建议大家先将本文开头提及的那篇文章阅读后，再阅读本文。

  本文分为两部分，首先是将代码分段、详细讲解，方便大家理解；随后是完整代码，方便大家自行尝试。

  由于机器学习往往需要多次执行，我们就在此先定义循环。

%% ANN Cycle Preparation ANNRMSE=9999; ANNRunNum=0; ANNRMSEMatrix=[]; ANNrAllMatrix=[]; while ANNRMSE>400

  其中，ANNRMSE是初始的RMSE；ANNRunNum是神经网络算法当前运行的次数；ANNRMSEMatrix用来存储每一次神经网络运行后所得到的RMSE结果；ANNrAllMatrix用来存储每一次神经网络运行后所得到的皮尔逊相关系数结果；最后一句表示当所得到的模型RMSE>400时，则停止循环。

  接下来，我们对神经网络的整体结构加以定义。

%% ANN x=TrainVARI'; t=TrainYield'; trainFcn = 'trainlm'; hiddenLayerSize = [10 10 10]; ANNnet = fitnet(hiddenLayerSize,trainFcn);

  其中，TrainVARI、TrainYield分别是我这里训练数据的自变量（特征）与因变量（标签）；trainFcn为神经网络所选用的训练函数方法名称，其名称与对应的方法对照如下表：

  hiddenLayerSize为神经网络所用隐层与各层神经元个数，[10 10 10]代表共有三层隐层，各层神经元个数分别为10，10，10。

  接下来，对输入神经网络模型的数据加以处理。

ANNnet.input.processFcns = {'removeconstantrows','mapminmax'}; ANNnet.output.processFcns = {'removeconstantrows','mapminmax'}; ANNnet.divideFcn = 'dividerand'; ANNnet.divideMode = 'sample'; ANNnet.divideParam.trainRatio = 0.6; ANNnet.divideParam.valRatio = 0.4; ANNnet.divideParam.testRatio = 0.0;

  其中，ANNnet.input.processFcns与ANNnet.output.processFcns分别代表输入模型数据的处理方法，'removeconstantrows'表示删除在各样本中数值始终一致的特征列，'mapminmax'表示将数据归一化处理；divideFcn表示划分数据训练集、验证集与测试集的方法，'dividerand'表示依据所给定的比例随机划分；divideMode表示对数据划分的维度，我们这里选择'sample'，也就是对样本进行划分；divideParam表示训练集、验证集与测试集所占比例，那么在这里，因为是直接用了先前随机森林方法（可以看这篇博客）中的数据划分方式，那么为了保证训练集、测试集的固定，我们就将divideParam.testRatio设置为0.0，然后将训练集与验证集比例划分为0.6与0.4。

  接下来对模型运行过程中的主要参数加以配置。

ANNnet.performFcn = 'mse'; ANNnet.trainParam.epochs=5000; ANNnet.trainParam.goal=0.01;

  其中，performFcn为模型误差衡量函数，'mse'表示均方误差；trainParam.epochs表示训练时Epoch次数，trainParam.goal表示模型所要达到的精度要求（即模型运行到trainParam.epochs次时或误差小于trainParam.goal时将会停止运行。

  这一部分代码大多数与绘图、代码与GUI生成等相关，因此就不再一一解释了，大家可以直接运行。需要注意的是，train是模型训练函数。

% For a list of all plot Functions type: help nnplot ANNnet.plotFcns = {'plotperform','plottrainstate','ploterrhist','plotregression','plotfit'}; [ANNnet,tr] = train(ANNnet,x,t); y = ANNnet(x); e = gsubtract(t,y); performance = perform(ANNnet,t,y); % Recalculate Training, Validation and Test Performance trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(ANNnet,trainTargets,y); valPerformance = perform(ANNnet,valTargets,y); testPerformance = perform(ANNnet,testTargets,y); % view(net) % Plots %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, ploterrhist(e) %figure, plotregression(t,y) %figure, plotfit(net,x,t) % Deployment % See the help for each generation function for more information. if (false) % Generate MATLAB function for neural network for application % deployment in MATLAB scripts or with MATLAB Compiler and Builder % tools, or simply to examine the calculations your trained neural % network performs. genFunction(ANNnet,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x); end if (false) % Generate a matrix-only MATLAB function for neural network code % generation with MATLAB Coder tools. genFunction(ANNnet,'myNeuralNetworkFunction','MatrixOnly','yes'); y = myNeuralNetworkFunction(x); end if (false) % Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools. gensim(ANNnet); end

%% Accuracy of ANN ANNPredictYield=sim(ANNnet,TestVARI')'; ANNRMSE=sqrt(sum(sum((ANNPredictYield-TestYield).^2))/size(TestYield,1)); ANNrMatrix=corrcoef(ANNPredictYield,TestYield); ANNr=ANNrMatrix(1,2); ANNRunNum=ANNRunNum 1; ANNRMSEMatrix=[ANNRMSEMatrix,ANNRMSE]; ANNrAllMatrix=[ANNrAllMatrix,ANNr]; disp(ANNRunNum); end disp(ANNRMSE);

  其中，ANNPredictYield为预测结果；ANNRMSE、ANNrMatrix分别为模型精度衡量指标RMSE与皮尔逊相关系数。结合本文1.1部分可知，我这里设置为当所得神经网络模型RMSE在400以内时，将会停止循环；否则继续开始执行本文1.2部分至1.6部分的代码。

  这一部分就不再赘述了，大家可以参考这篇博客（https://blog.csdn.net/zhebushibiaoshifu/article/details/114806478）。

%% ANN Model Storage ANNModelSavePath='G:\CropYield\02_CodeAndMap\00_SavedModel\'; save(sprintf('%sRF0417ANN0399.mat',ANNModelSavePath),'TestVARI','TestYield','TrainVARI','TrainYield','ANNnet','ANNPredictYield','ANNr','ANNRMSE',... 'hiddenLayerSize');

  完整代码如下：

%% ANN Cycle Preparation ANNRMSE=9999; ANNRunNum=0; ANNRMSEMatrix=[]; ANNrAllMatrix=[]; while ANNRMSE>1000 %% ANN x=TrainVARI'; t=TrainYield'; trainFcn = 'trainlm'; hiddenLayerSize = [10 10 10]; ANNnet = fitnet(hiddenLayerSize,trainFcn); ANNnet.input.processFcns = {'removeconstantrows','mapminmax'}; ANNnet.output.processFcns = {'removeconstantrows','mapminmax'}; ANNnet.divideFcn = 'dividerand'; ANNnet.divideMode = 'sample'; ANNnet.divideParam.trainRatio = 0.6; ANNnet.divideParam.valRatio = 0.4; ANNnet.divideParam.testRatio = 0.0; ANNnet.performFcn = 'mse'; ANNnet.trainParam.epochs=5000; ANNnet.trainParam.goal=0.01; % For a list of all plot functions type: help nnplot ANNnet.plotFcns = {'plotperform','plottrainstate','ploterrhist','plotregression','plotfit'}; [ANNnet,tr] = train(ANNnet,x,t); y = ANNnet(x); e = gsubtract(t,y); performance = perform(ANNnet,t,y); % Recalculate Training, Validation and Test Performance trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(ANNnet,trainTargets,y); valPerformance = perform(ANNnet,valTargets,y); testPerformance = perform(ANNnet,testTargets,y); % view(net) % Plots %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, ploterrhist(e) %figure, plotregression(t,y) %figure, plotfit(net,x,t) % Deployment % See the help for each generation function for more information. if (false) % Generate MATLAB function for neural network for application % deployment in MATLAB scripts or with MATLAB Compiler and Builder % tools, or simply to examine the calculations your trained neural % network performs. genFunction(ANNnet,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x); end if (false) % Generate a matrix-only MATLAB function for neural network code % generation with MATLAB Coder tools. genFunction(ANNnet,'myNeuralNetworkFunction','MatrixOnly','yes'); y = myNeuralNetworkFunction(x); end if (false) % Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools. gensim(ANNnet); end %% Accuracy of ANN ANNPredictYield=sim(ANNnet,TestVARI')'; ANNRMSE=sqrt(sum(sum((ANNPredictYield-TestYield).^2))/size(TestYield,1)); ANNrMatrix=corrcoef(ANNPredictYield,TestYield); ANNr=ANNrMatrix(1,2); ANNRunNum=ANNRunNum 1; ANNRMSEMatrix=[ANNRMSEMatrix,ANNRMSE]; ANNrAllMatrix=[ANNrAllMatrix,ANNr]; disp(ANNRunNum); end disp(ANNRMSE); %% ANN Model Storage ANNModelSavePath='G:\CropYield\02_CodeAndMap\00_SavedModel\'; save(sprintf('%sRF0417ANN0399.mat',ANNModelSavePath),'AreaPercent','InputOutput','nLeaf','nTree',... 'RandomNumber','RFModel','RFPredictConfidenceInterval','RFPredictYield','RFr','RFRMSE',... 'TestVARI','TestYield','TrainVARI','TrainYield','ANNnet','ANNPredictYield','ANNr','ANNRMSE',... 'hiddenLayerSize');