我需要一个代码：用MATLAB训练一个transformer模型来做六类图像分类并绘制误差曲线

好的，下面是一个用MATLAB实现的Transformer模型来进行图像分类的示例代码：

% 导入数据
imds = imageDatastore('data_folder', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

% 创建Transformers模型
numHeads = 8;
dModel = 64;
dff = 256;
numLayers = 6;
seqLength = 49;
numClasses = numel(categories(imdsTrain.Labels));

layers = [
    imageInputLayer([28 28 1],'Name','input')
    transformEncoderLayer(numHeads,dModel,dff,'SequenceLength',seqLength,'Name','transformerEncoder')
    flattenLayer('Name','flatten')
    fullyConnectedLayer(numClasses,'Name','fc')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classification')];

% 设置训练选项
options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'MiniBatchSize',64, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');

% 训练模型
net = trainNetwork(imdsTrain,layers,options);

% 绘制误差曲线
figure;
plot(net.LearnRateSchedule);
title('Learning Rate Schedule');
xlabel('Iteration');
ylabel('Learning Rate');
grid on;

figure;
plot(net.TrainRMSE,'-o');
hold on
plot(net.ValidationRMSE,'-o');
legend({'Training RMSE','Validation RMSE'},'Location','northwest');
xlabel('Epoch');
ylabel('RMSE');
title('Training and Validation RMSE');
grid on;

figure;
plot(net.TrainClassificationLoss,'-o');
hold on
plot(net.ValidationClassificationLoss,'-o');
legend({'Training Classification Loss','Validation Classification Loss'},'Location','northwest');
xlabel('Epoch');
ylabel('Classification Loss');
title('Training and Validation Classification Loss');
grid on;

在这个示例中，我们使用了一个包含6个Transformer层的模型来进行图像分类。训练选项使用了Adam优化器和最大训练轮数为50。

绘制的三个误差曲线分别是学习率曲线、训练和验证RMSE曲线以及训练和验证分类损失曲线。

请注意，该示例代码是基于MATLAB的深度学习工具箱的，需要安装该工具箱才能运行。此外，需要将数据文件夹的路径替换为实际数据文件夹的路径。