帮我修改正确
时间: 2023-08-11 15:19:43 浏览: 51
如果你的数据集通道数为1,那么你可以通过在'ConvLayer1'之前添加一个通道数为1的卷积层来解决这个问题。修改后的代码可能如下所示:
```matlab
function net = train_EEGNet(X_train, Y_train, Fs, T, EEGNet_Params)
% Define EEGNet architecture
inputLayer = imageInputLayer([size(X_train,1) size(X_train,2) 1]);
convLayer1 = convolution2dLayer([1 EEGNet_Params.F1], EEGNet_Params.F1, 'padding', 'same');
convLayer2 = convolution2dLayer([EEGNet_Params.Chans EEGNet_Params.F2], EEGNet_Params.F2, 'padding', 'same');
depthLayer = depthConcatenationLayer(2);
poolLayer = maxPooling2dLayer([1 EEGNet_Params.P], 'stride', [1 EEGNet_Params.Q]);
dropoutLayer = dropoutLayer(EEGNet_Params.DropProb);
flattenLayer = flattenLayer();
denseLayer = fullyConnectedLayer(2, 'name', 'fc');
softmaxLayer = softmaxLayer();
classificationLayer = classificationLayer();
layers = [
inputLayer
convLayer1
convolution2dLayer([EEGNet_Params.Chans EEGNet_Params.F1], EEGNet_Params.F1, 'padding', 'same') % 新增卷积层
convLayer2
depthLayer
poolLayer
dropoutLayer
flattenLayer
denseLayer
softmaxLayer
classificationLayer];
% Set training options
options = trainingOptions('adam', ...
'InitialLearnRate', EEGNet_Params.LR, ...
'MaxEpochs', EEGNet_Params.NumEpochs, ...
'MiniBatchSize', EEGNet_Params.MiniBatchSize, ...
'Shuffle', 'every-epoch', ...
'Plots', 'training-progress', ...
'Verbose', false);
% Train network
net = trainNetwork(X_train, categorical(Y_train), layers, options);
end
```
这里我们新增了一个卷积层,它的输入通道数为1,输出通道数为16,用于将输入数据的通道数转换为网络期望的通道数。