How to Train Network on Image and Feature Data for regression

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);

I referenced this example：https://www.mathworks.com/help/deeplearning/ug/train-network-on-image-and-feature-data.html?s_tid=srchtitle_Train%20Network%20on%20Image%20and%20Feature_1

Warning: Training stops at iteration 3 because the training loss is NaN. Predictions using the output network may contain NaN values.

[yanqi liu]

yes，sir，may be check the data to find NaN value，if possible，may be upload your data to analysis