基于长短期神经网络LSTM的多分类代码，基于LSTM的多分类预测

%% LSTM network %% import data clc clear close all %读取double格式数据 [num1,ax,ay] = xlsread('traindate.xlsx',1); [num2,ax2,ay2] = xlsread('flag.xlsx',1); n = length(num2); num3 = zeros(n,2); for ii = 1:n num3(ii,num2(ii)+1)=1; end n = randperm(length(num1)); m=135; input_train =num1(n(1:m),1:9);%训练数据输出数据 output_train = num3(n(1:m),1:2);%训练数据输入数据 input_test = num1((m+1:end),1:9);%测试数据输出数据 output_test = num2((m+1:end),1);%测试数据输入数据 [inputn,inputps]=mapminmax(input_train',-1,1);%训练数据的输入数据的归一化 [outputn,outputps]=mapminmax(output_train',-1,1);%训练数据的输出数据的归一化de inputn_test=mapminmax('apply',input_test',inputps); %% Define Network Architecture % Define the network architecture. numFeatures = 9;%输入层维度 numResponses = 2;%输出维度 % 200 hidden units numHiddenUnits = 70;%第一层维度 % a fully connected layer of size 50 & a dropout layer with dropout probability 0.5 layers = [ ... sequenceInputLayer(numFeatures)%输入层 lstmLayer(numHiddenUnits,'OutputMode','sequence')%第一层 fullyConnectedLayer(100)%链接层 dropoutLayer(0.3)%遗忘层 fullyConnectedLayer(numResponses)%链接层 regressionLayer];%回归层 % Specify the training options. % Train for 60 epochs with mini-batches of size 20 using the solver 'adam' maxEpochs = 60;%最大迭代次数 miniBatchSize =2;%最小批量 % the learning rate == 0.01 % set the gradient threshold to 1 % set 'Shuffle' to 'never' options = trainingOptions('adam', ... %解算器 'MaxEpochs',maxEpochs, ... %最大迭代次数 'MiniBatchSize',miniBatchSize, ... %最小批次 'InitialLearnRate',0.01, ... %初始学习率 'GradientThreshold',0.03, ... %梯度阈值 'Shuffle','every-epoch', ... %打乱顺序 'Plots','training-progress',... %画图 'Verbose',1); %不输出训练过程 %% Train the Network net = trainNetwork(inputn,outputn,layers,options);%开始训练 % save maydata.mat %% Test the Network y_pred = predict(net,inputn_test,'MiniBatchSize',miniBatchSize)';%测试仿真输出 [mx,y_pred]=max(mapminmax('reverse',y_pred',outputps)); % y_pred0 = predict(net,inputn,'MiniBatchSize',1)';%训练拟合值 % y_pred0=(mapminmax('reverse',y_pred0',outputps))'; y_pred=(double(y_pred))'-1; figure%打开一个图像窗口 plot(y_pred(:,1),'k-o')%黑色实线，点的形状为* hold on%继续画图 plot(output_test(:,1),'r--*')%红色实线，点的形状为o hold off%停止画图 title('测试图')%标题 ylabel('类别')%Y轴名称 legend('测试输出类别','实际类别')%标签 set(gca,'YTick',0:1) error1 = y_pred-output_test;%误差 figure plot(error1(:,1),'k-*') title('测试误差图') [MSE,RMSE,MBE,MAE ] =MSE_RMSE_MBE_MAE(output_test(:,1),y_pred(:,1)); result_table = table; result_table.simd = y_pred(:,1); result_table.trued = output_test(:,1); writetable(result_table,'./结果.csv')