BO-CNN-GRU/Bayes-CNN-GRU,基于贝叶斯优化CNN-GRU多特征分类预测（MATLAB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 %% 读取数据 res = xlsread('data.xlsx'); %% 分析数据 num_class = length(unique(res(:, end))); % 类别数（Excel最后一列放类别） num_dim = size(res, 2) - 1; % 特征维度 num_res = size(res, 1); % 样本数（每一行，是一个样本） num_size = 0.8; % 训练集占数据集的比例 res = res(randperm(num_res), :); % 打乱数据集（不打乱数据时，注释该行） flag_conusion = 1; % 标志位为1，打开混淆矩阵（要求2018版本及以上） %% 设置变量存储数据 P_train = []; P_test = []; T_train = []; T_test = []; %% 划分数据集 for i = 1 : num_class mid_res = res((res(:, end) == i), :); % 循环取出不同类别的样本 mid_size = size(mid_res, 1); % 得到不同类别样本个数 mid_tiran = round(num_size * mid_size); % 得到该类别的训练样本个数 P_train = [P_train; mid_res(1: mid_tiran, 1: end - 1)]; % 训练集输入 T_train = [T_train; mid_res(1: mid_tiran, end)]; % 训练集输出 P_test = [P_test; mid_res(mid_tiran + 1: end, 1: end - 1)]; % 测试集输入 T_test = [T_test; mid_res(mid_tiran + 1: end, end)]; % 测试集输出 end %% 数据转置 P_train = P_train'; P_test = P_test'; T_train = T_train'; T_test = T_test'; %% 得到训练集和测试样本个数 M = size(P_train, 2); N = size(P_test , 2); %% 数据归一化 [p_train, ps_input] = mapminmax(P_train, 0, 1); p_test = mapminmax('apply', P_test, ps_input); t_train = categorical(T_train)'; t_test = categorical(T_test )'; %% 创建元胞或向量，长度为训练集大小； Lp_train = cell(size(p_train,2),1); for i=1:size(p_train,2) Lp_train{i,1} = p_train(:,i); end % 创建元胞或向量，长度为测试集大小； for i=1:size(p_test,2) Lp_test{i,1} = p_test(:,i); end %% 优化算法参数设置 % 参数取值上界(学习率，隐藏层节点，正则化系数) % 卷积核大小大小 FiltZise = 10; fitness = @fical; %% 贝叶斯优化参数范围 optimVars = [ optimizableVariable('NumOfUnits', [50, 100], 'Type', 'integer') optimizableVariable('InitialLearnRate', [1e-3, 1], 'Transform', 'log') optimizableVariable('L2Regularization', [1e-10, 1e-2], 'Transform', 'log')]; %% 贝叶斯优化网络参数 BayesObject = bayesopt(fitness, optimVars, ... % 优化函数，和参数范围 'MaxTime', Inf, ... % 优化时间（不限制） 'IsObjectiveDeterministic', false, ... 'MaxObjectiveEvaluations', 10, ... % 最大迭代次数 'Verbose', 1, ... % 显示优化过程 'UseParallel', false); %% 得到最优参数 NumOfUnits = BayesObject.XAtMinEstimatedObjective.NumOfUnits; % 最佳隐藏层节点数 InitialLearnRate = BayesObject.XAtMinEstimatedObjective.InitialLearnRate; % 最佳初始学习率 L2Regularization = BayesObject.XAtMinEstimatedObjective.L2Regularization; % 最佳L2正则化系数 %% 创建混合CNN-GRU网络架构 % 创建"CNN-GRU"模型 layers = [... % 输入特征 sequenceInputLayer([num_dim 1 1],'Name','input') sequenceFoldingLayer('Name','fold') % CNN特征提取 convolution2dLayer([FiltZise 1],32,'Padding','same','WeightsInitializer','he','Name','conv','DilationFactor',1); batchNormalizationLayer('Name','bn') eluLayer('Name','elu') averagePooling2dLayer(1,'Stride',FiltZise,'Name','pool1') % 展开层 sequenceUnfoldingLayer('Name','unfold') % 平滑层 flattenLayer('Name','flatten') % GRU特征学习 gruLayer(NumOfUnits,'Name','gru1','RecurrentWeightsInitializer','He','InputWeightsInitializer','He') % GRU输出 gruLayer(32,'OutputMode',"last",'Name','bil4','RecurrentWeightsInitializer','He','InputWeightsInitializer','He') dropoutLayer(0.25,'Name','drop1') % 全连接层 fullyConnectedLayer(num_class,'Name','fc') softmaxLayer('Name','sf') classificationLayer('Name','cf')]; layers = layerGraph(layers); layers = connectLayers(layers,'fold/miniBatchSize','unfold/miniBatchSize'); %% CNNGRU训练选项 % 批处理样本 MiniBatchSize =128; % 最大迭代次数 MaxEpochs = 500; options = trainingOptions( 'adam', ... 'MaxEpochs',500, ... 'GradientThreshold',1, ... 'InitialLearnRate',InitialLearnRate, ... 'LearnRateSchedule','piecewise', ... 'LearnRateDropPeriod',400, ... 'LearnRateDropFactor',0.2, ... 'L2Regularization',L2Regularization,... 'Verbose',false, ... 'Plots','training-progress'); %% 训练模型 [net,traininfo] = trainNetwork(Lp_train, t_train, layers, options); %% 模型预测 t_sim1 = predict(net, Lp_train); t_sim2 = predict(net, Lp_test ); %% 反归一化 T_sim1 = vec2ind(t_sim1'); T_sim2 = vec2ind(t_sim2'); %% 显示网络结构 analyzeNetwork(net) %% 性能评价 error1 = sum((T_sim1 == T_train))/M * 100 ; error2 = sum((T_sim2 == T_test)) /N * 100 ; %% 绘图 figure plot(layers) title("贝叶斯优化CNN-GRU模型结构") figure; yyaxis left; % 激活左边的轴 plot(traininfo.TrainingLoss,'linewidth',1); title('贝叶斯优化CNN-GRU'); xlabel('训练次数'); ylabel('损失值'); % 给左y轴添加轴标签 yyaxis right; % 激活右边的轴 plot(traininfo.TrainingAccuracy,'linewidth',1); ylabel('准确率'); % 给右y轴添加轴标签 figure plot(T_train,'-+','Color',[0 0 0]./255,'linewidth',1) hold on plot(T_sim1,'o','Color',[255 0 255]./255,'linewidth',1) legend('真实值','预测值') xlabel('预测样本') ylabel('预测结果') string = {'训练集预测结果对比'; ['准确率=' num2str(error1) '%']}; title(string) xlim([1, M]) grid %-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- figure plot(T_test,'-+','Color',[0 0 0]./255,'linewidth',2) hold on plot(T_sim2,'o','Color',[250 180 60]./255,'linewidth',2) legend('真实值','预测值') xlabel('预测样本') ylabel('预测结果') string = {'测试集预测结果对比'; ['准确率=' num2str(error2) '%']}; title(string) xlim([1, N]) grid %% 混淆矩阵 if flag_conusion == 1 figure cm = confusionchart(T_train, T_sim1); cm.Title = 'Confusion Matrix for Train Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized'; figure cm = confusionchart(T_test, T_sim2); cm.Title = 'Confusion Matrix for Test Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized'; end