How to Train Network on Image and Feature Data for regression

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zhushaolong 2022-3-12

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1670064-how-to-train-network-on-image-and-feature-data-for-regression

评论： yanqi liu 2022-3-14

dsX1Train = arrayDatastore(X1Train,IterationDimension=4);
dsX2Train = arrayDatastore(X2Train);
dsTTrain = arrayDatastore(TTrain);
dsTrain = combine(dsX1Train,dsX2Train,dsTTrain);
%% 
lgraph = layerGraph();
tempLayers = [
    imageInputLayer([224 224 3],"Name","imageinput_1")
    convolution2dLayer([3 3],8,"Name","conv_1","Padding","same")
    batchNormalizationLayer("Name","batchnorm_1")
    reluLayer("Name","relu_1")
    averagePooling2dLayer([2 2],"Name","avgpool2d_1","Stride",[2 2])
    convolution2dLayer([3 3],16,"Name","conv_2","Padding","same")
    batchNormalizationLayer("Name","batchnorm_2")
    reluLayer("Name","relu_2")
    averagePooling2dLayer([2 2],"Name","avgpool2d_2","Stride",[2 2])
    convolution2dLayer([3 3],32,"Name","conv_3","Padding","same")
    batchNormalizationLayer("Name","batchnorm_3")
    reluLayer("Name","relu_3")
    convolution2dLayer([3 3],32,"Name","conv_4","Padding","same")
    batchNormalizationLayer("Name","batchnorm_4")
    reluLayer("Name","relu_4")
    dropoutLayer(0.2,"Name","dropout")
    fullyConnectedLayer(1,"Name","fc_1")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    imageInputLayer([1 46 1],"Name","imageinput_2")
    fullyConnectedLayer(1,"Name","fc_2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
    concatenationLayer(2,2,"Name","concat")
    fullyConnectedLayer(1,"Name","fc_3")
    regressionLayer("Name","regressionoutput")];
lgraph = addLayers(lgraph,tempLayers);
clear tempLayers;
lgraph = connectLayers(lgraph,"fc_2","concat/in1");
lgraph = connectLayers(lgraph,"fc_1","concat/in2");
%% 
options = trainingOptions("sgdm", ...
    MaxEpochs=15, ...
    InitialLearnRate=0.001, ...
    Plots="training-progress", ...
    Verbose=0);
net = trainNetwork(dsTrain,lgraph,options);