% 分割数据集为训练集和测试集 cv = cvpartition(size(features, 1),'Holdout',0.2); Xtrain = features(training(cv),:); Ytrain = categorical(labels(training(cv))); Xtest = features(test(cv),:); Ytest = categorical(labels(test(cv))); % 定义 CNN 架构 layers = [ imageInputLayer([1 3 1],'Name','input') convolution2dLayer([1 3],32,'Padding','same','Name','conv1') batchNormalizationLayer('Name','BN1') reluLayer('Name','relu1') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1') convolution2dLayer([1 3],64,'Padding','same','Name','conv2') batchNormalizationLayer('Name','BN2') reluLayer('Name','relu2') maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2') fullyConnectedLayer(128,'Name','fc') batchNormalizationLayer('Name','BN3') reluLayer('Name','relu3') fullyConnectedLayer(4,'Name','fc2') softmaxLayer('Name','softmax') classificationLayer('Name','classoutput')]; % 定义训练选项 options = trainingOptions('sgdm', ... 'MaxEpochs',20, ... 'ValidationData',{Xtest,Ytest}, ... 'ValidationFrequency',30, ... 'Verbose',false, ... 'Plots','training-progress'); % 训练 CNN net = trainNetwork(Xtrain,Ytrain,layers,options); % 对测试集进行预测 YPred = classify(net,Xtest); % 计算分类准确率 accuracy = sum(YPred == Ytest)/numel(Ytest); disp(['Classification accuracy: ' num2str(accuracy)]);代码中出现 层 'pool2': 输入大小不匹配。此层的输入大小与预期的输入大小不同。 此层的输入: 来自 层 'relu2' (大小 1(S) × 1(S) × 64(C) × 1(B))错误要怎么用padding来解决？给代码

在定义第二个池化层 `maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2')` 时，它的输入大小应该是与上一层 `relu2` 的输出大小相同的。因此，可能需要在第二个卷积层中添加 padding，使得其输出大小与上一层相同。以下是修改后的代码：

% 分割数据集为训练集和测试集
cv = cvpartition(size(features, 1),'Holdout',0.2);
Xtrain = features(training(cv),:);
Ytrain = categorical(labels(training(cv)));
Xtest = features(test(cv),:);
Ytest = categorical(labels(test(cv)));

% 定义 CNN 架构
layers = [
    imageInputLayer([1 3 1],'Name','input')
    convolution2dLayer([1 3],32,'Padding','same','Name','conv1')
    batchNormalizationLayer('Name','BN1')
    reluLayer('Name','relu1')
    maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool1')
    convolution2dLayer([1 3],64,'Padding','same','Name','conv2')
    batchNormalizationLayer('Name','BN2')
    reluLayer('Name','relu2')
    maxPooling2dLayer([1 2],'Stride',[1 2],'Name','pool2','Padding','same') % 添加 padding
    fullyConnectedLayer(128,'Name','fc')
    batchNormalizationLayer('Name','BN3')
    reluLayer('Name','relu3')
    fullyConnectedLayer(4,'Name','fc2')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classoutput')
];

% 定义训练选项
options = trainingOptions('sgdm', ...
    'MaxEpochs',20, ...
    'ValidationData',{Xtest,Ytest}, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');

% 训练 CNN
net = trainNetwork(Xtrain,Ytrain,layers,options);

% 对测试集进行预测
YPred = classify(net,Xtest);

% 计算分类准确率
accuracy = sum(YPred == Ytest)/numel(Ytest);
disp(['Classification accuracy: ' num2str(accuracy)]);