LSTM回归预测matlab TPA-LSTM时间注意力机制长短期记忆神经网络回归预测多输入单输出含Matlab源码 1984期

% 数据集 列为特征，行为样本数目
% 数据集 列为特征，行为样本数目

%% 清除命令行窗口和工作区变量
clc
clear
close all
%% 路径设置
addpath('./')

%% 数据导入及处理
load('./Train.mat')
Train.weekend = dummyvar(Train.weekend);
Train.month = dummyvar(Train.month);
Train = movevars(Train,{'weekend','month'},'After','demandLag');
Train.ts = [];

% Train.hour = dummyvar(Train.hour);
%自己主动观察右侧工作区变量格式，对前面数据进行更改替换
Train(1,:) =[];
y = Train.demand;
x = Train{:,2:5};
[xnorm,xopt] = mapminmax(x',0,1);
[ynorm,yopt] = mapminmax(y',0,1);
%
% xnorm = [xnorm;Train.weekend';Train.month'];
%%
% x = x';
xnorm = xnorm(:,1:1000);
ynorm = ynorm(1:1000);

k = 24;           % 滞后长度

% 转换成2-D image
for i = 1:length(ynorm)-k

    Train_xNorm(:,i,:) = xnorm(:,i:i k-1);
    Train_yNorm(i) = ynorm(i k-1);
    Train_y(i) = y(i k-1);
end
Train_yNorm= Train_yNorm';



ytest = Train.demand(1001:1170);
xtest = Train{1001:1170,2:5};
[xtestnorm] = mapminmax('apply', xtest',xopt);
[ytestnorm] = mapminmax('apply',ytest',yopt);
% xtestnorm = [xtestnorm; Train.weekend(1001:1170,:)'; Train.month(1001:1170,:)'];
xtest = xtest';
for i = 1:length(ytestnorm)-k
    Test_xNorm(:,i,:) = xtestnorm(:,i:i k-1);
    Test_yNorm(i) = ytestnorm(i k-1);
    Test_y(i) = ytest(i k-1);
end
Test_yNorm = Test_yNorm';

clear k i x y
%
Train_xNorm = dlarray(Train_xNorm,'CBT');
Train_yNorm = dlarray(Train_yNorm,'BC');
Test_xNorm = dlarray(Test_xNorm,'CBT');
Test_yNorm = dlarray(Test_yNorm,'BC');
%% 训练集和验证集划分
TrainSampleLength = length(Train_yNorm);
validatasize = floor(TrainSampleLength * 0.1);
Validata_xNorm = Train_xNorm(:,end - validatasize:end,:);
Validata_yNorm = Train_yNorm(:,TrainSampleLength-validatasize:end);
Validata_y = Train_y(TrainSampleLength-validatasize:end);

Train_xNorm = Train_xNorm(:,1:end-validatasize,:);
Train_yNorm = Train_yNorm(:,1:end-validatasize);
Train_y = Train_y(1:end-validatasize);
%%

%参数设置
inputSize = size(Train_xNorm,1);   %数据输入x的特征维度
outputSize = 1;                    %数据输出y的维度  
numhidden_units1=50;

[params,~] = paramsInit(numhidden_units1,inputSize,outputSize);     % 导入初始化参数

[~,validatastate] = paramsInit(numhidden_units1,inputSize,outputSize);     % 导入初始化参数
[~,TestState] = paramsInit(numhidden_units1,inputSize,outputSize);     % 导入初始化参数
% 训练相关参数
TrainOptions;
numIterationsPerEpoch = floor((TrainSampleLength-validatasize)/minibatchsize);
LearnRate = 0.01;
%% Loop over epochs.
figure
start = tic;
lineLossTrain = animatedline('color','r');
validationLoss = animatedline('color',[0 0 0]./255,'Marker','o','MarkerFaceColor',[150 150 150]./255);
xlabel('Iteration')
ylabel('Loss')

% epoch 更新 
iteration = 0;
for epoch = 1 : numEpochs
   
    [~,state] = paramsInit(numhidden_units1,inputSize,outputSize);       % 每轮epoch，state初始化
    disp(['Epoch: ', int2str(epoch)])
    
    % batch 更新
    for i = 1 : numIterationsPerEpoch
       
        iteration = iteration   1;
        disp(['Iteration: ', int2str(iteration)])
        idx = (i-1)*minibatchsize 1:i*minibatchsize;
        
        dlX = gpuArray(Train_xNorm(:,idx,:));
        dlY = gpuArray(Train_yNorm(idx));
        [gradients,loss,state] = dlfeval(@TPAModel,dlX,dlY,params,state);
        
        % L2正则化
%         L2regulationFactor = 0.000011;
%         gradients = dlupdate( @(g,parameters) L2Regulation(g,parameters,L2regulationFactor),gradients,params);
%         gradients = dlupdate(@(g) thresholdL2Norm(g, gradientThreshold),gradients);
        
        [params,averageGrad,averageSqGrad] = adamupdate(params,gradients,averageGrad,averageSqGrad,iteration,LearnRate);
        
        
        
        % 验证集测试
        if iteration == 1 || mod(iteration,validationFrequency) == 0
            output_Ynorm = TPAModelPredict(gpuArray(Validata_xNorm),params,validatastate);
            lossValidation = mse(output_Ynorm, gpuArray(Validata_yNorm));