首页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

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

时间: 2023-08-05 13:10:28 浏览: 164

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： ```matlab 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 ```

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