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基于Matlab实现人工神经网络(ANN)回归的示例详解

疯狂学习GIS 人气:0

在之前的文章MATLAB实现随机森林(RF)回归与自变量影响程度分析中,我们对基于MATLAB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。因此建议大家先将文章MATLAB实现随机森林(RF)回归与自变量影响程度分析阅读后,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。

1 分解代码

1.1 循环准备

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RMSEANNRunNum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。

1.2 神经网络构建

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,TrainVARITrainYield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101010

1.3 数据处理

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.processFcnsANNnet.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0.60.4

1.4 模型训练参数配置

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)。

1.5 神经网络实现

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

1.6 精度衡量

%% 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;ANNRMSEANNrMatrix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RMSE400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。

1.7 保存模型

这一部分就不再赘述了,大家可以参考文章MATLAB实现随机森林(RF)回归与自变量影响程度分析

%% ANN Model Storage

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

2 完整代码

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');

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