matlab-(含教程)基于Bi-LSTM的遥感数据预测matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); rootDir = ''; trainingData = importdata(fullfile(rootDir,'train.mat')); trainX = trainingData.trainx; trainY = trainingData.trainy+1; Xtrain = cell({}); for i = 1:size(trainX,1) Xtrain{i,1} = squeeze(trainX(i,:,:)); end Ytrain = categorical(trainY'); % Load the validation data valData = importdata(fullfile(rootDir,'test.mat')); valX = valData.testx; valY = valData.testy+1; Yval = categorical(valY'); Xval = cell({}); for i = 1:size(valX,1) Xval{i,1} = squeeze(valX(i,:,:)); end valDataSet = cell({Xval,Yval}); % Create bilstm model numFeatures = 8; numHiddens = 256; numClasses = 5; netLayers = [ sequenceInputLayer(numFeatures,"Name","input") bilstmLayer(numHiddens,"Name","bilstm_1",'OutputMode','last') bilstmLayer(numHiddens,"Name","bilstm_2",'OutputMode','last') dropoutLayer(0.5,"Name","dropout") flattenLayer("Name","flatten") fullyConnectedLayer(numClasses,"Name","fc") softmaxLayer("Name","softmax") classificationLayer("Name","classification")]; % Set the hyper parameters for unet training options = trainingOptions('adam', ... 'InitialLearnRate',1e-4, ... 'Plots','training-progress',... 'MaxEpochs',40, ... 'MiniBatchSize',128,... 'VerboseFrequency',1,... 'ExecutionEnvironment', 'auto',... 'Shuffle','every-epoch',... 'ValidationData',valDataSet,... 'ValidationFrequency',1,... 'WorkerLoad',4,... 'CheckPointPath',rootDir); % start training net = trainNetwork(Xtrain,Ytrain, netLayers, options); save('bilstm.mat','net'); bilstm = importdata('bilstm.mat'); % Accuracy assessment pred = classify(bilstm, Xval); [confusionMatrix,order] = confusionmat(categorical(valY),pred); cm = confusionchart(confusionMatrix); % caculate user accuracy and producer accuracy confusionMatrix = [confusionMatrix, zeros(size(confusionMatrix,1),1)]; confusionMatrix = [confusionMatrix; zeros(1,size(confusionMatrix,2))]; confusionMatrix(1:end-1,end) = confusionMatrix(sub2ind(size(confusionMatrix),1:numClasses,1:numClasses))... ./sum(confusionMatrix(1:end-1,1:end-1),2)'; confusionMatrix(end,1:end-1) = confusionMatrix(sub2ind(size(confusionMatrix),1:numClasses,1:numClasses))... ./sum(confusionMatrix(1:end-1,1:end-1),1); confusionMatrix(end,end) = sum(confusionMatrix(sub2ind(size(confusionMatrix),1:numClasses,1:numClasses)))... ./sum(sum(confusionMatrix(1:end-1,1:end-1))); producerAccuracy = confusionMatrix(end,1:end-1); userAccuracy = confusionMatrix(1:end-1,end); overallAccuracy = confusionMatrix(end,end);