feedForwardLayer = [ fullyConnectedLayer(dff,'Name','fc1') reluLayer('Name','relu') dropoutLayer(dropoutRate,'Name','dropout') fullyConnectedLayer(dModel,'Name','fc2') dropoutLayer(dropoutRate,'Name','dropout') ];输出六类如何修改

如果你想将这个全连接网络修改为输出 6 类，那么你需要将最后一层的 fullyConnectedLayer 替换为一个输出大小为 6 的全连接层，并且修改网络的损失函数为交叉熵（cross-entropy）。具体的修改如下所示：

numClasses = 6;
feedForwardLayer = [ 
    fullyConnectedLayer(dff,'Name','fc1') 
    reluLayer('Name','relu') 
    dropoutLayer(dropoutRate,'Name','dropout') 
    fullyConnectedLayer(numClasses,'Name','fc2') % 修改为输出 6 类的全连接层
    softmaxLayer('Name','softmax') % 增加一个 softmax 层
    classificationLayer('Name','classoutput') % 增加一个分类层
];

% 修改网络的损失函数为交叉熵
options = trainingOptions('adam', ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'InitialLearnRate',initialLearnRate, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',learnRateDropFactor, ...
    'LearnRateDropPeriod',learnRateDropPeriod, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'ValidationData',{XValidation,YValidation}, ...
    'ValidationFrequency',validationFrequency, ...
    'ValidationPatience',Inf, ...
    'ExecutionEnvironment',executionEnvironment, ...
    'LossFcn', 'crossentropy'); % 修改为交叉熵损失函数