帮我写一个MATLAB多输入多输出的CNN回归预测模型代码

当然可以！以下是一个简单的多输入多输出的CNN回归预测模型代码示例：

% 导入数据
X1 = load('input1.mat');
X2 = load('input2.mat');
Y = load('output.mat');

% 划分训练集和测试集
train_ratio = 0.8;
train_size = round(size(X1,1)*train_ratio);

X1_train = X1(1:train_size,:);
X1_test = X1(train_size+1:end,:);
X2_train = X2(1:train_size,:);
X2_test = X2(train_size+1:end,:);
Y_train = Y(1:train_size,:);
Y_test = Y(train_size+1:end,:);

% 构建模型
input1 = inputLayer(size(X1_train,2),'Name','input1');
input2 = inputLayer(size(X2_train,2),'Name','input2');
conv1 = convolution2dLayer(3,16,'Padding','same','Name','conv1');
relu1 = reluLayer('Name','relu1');
pool1 = maxPooling2dLayer(2,'Stride',2,'Name','pool1');
conv2 = convolution2dLayer(3,32,'Padding','same','Name','conv2');
relu2 = reluLayer('Name','relu2');
pool2 = maxPooling2dLayer(2,'Stride',2,'Name','pool2');
concat = concatenationLayer(3,2,'Name','concat');
fc1 = fullyConnectedLayer(128,'Name','fc1');
relu3 = reluLayer('Name','relu3');
fc2 = fullyConnectedLayer(size(Y_train,2),'Name','fc2');
regression = regressionLayer('Name','regression');

net = [
    imageInputLayer([size(X1_train,2) 1 1],'Name','input1')
    imageInputLayer([size(X2_train,2) 1 1],'Name','input2')
    convolution2dLayer(3,16,'Padding','same','Name','conv1')
    reluLayer('Name','relu1')
    maxPooling2dLayer(2,'Stride',2,'Name','pool1')
    convolution2dLayer(3,32,'Padding','same','Name','conv2')
    reluLayer('Name','relu2')
    maxPooling2dLayer(2,'Stride',2,'Name','pool2')
    concatenationLayer(3,2,'Name','concat')
    fullyConnectedLayer(128,'Name','fc1')
    reluLayer('Name','relu3')
    fullyConnectedLayer(size(Y_train,2),'Name','fc2')
    regressionLayer('Name','regression')
];

% 设置训练参数
options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'MiniBatchSize',64, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{cat(3,X1_test,X2_test),Y_test}, ...
    'ValidationFrequency',10, ...
    'Plots','training-progress');

% 训练模型
trainedNet = trainNetwork({X1_train,X2_train},Y_train,net,options);

这个模型包括两个输入层 `input1` 和 `input2`，分别输入两个输入数据 `X1` 和 `X2`。两个输入数据经过卷积层和池化层后，进行拼接，再经过全连接层和回归层最终输出预测结果。根据具体数据情况，可以调整模型结构和训练参数。