帮我写一段matlab的CNN-biGRU代码

以下是一个简单的MATLAB实现CNN-biGRU模型的代码示例：

% 加载数据集并进行预处理
load mnist.mat
XTrain = reshape(XTrain, [28, 28, 1, size(XTrain, 4)]);
XTest = reshape(XTest, [28, 28, 1, size(XTest, 4)]);
YTrain = categorical(YTrain);
YTest = categorical(YTest);

% 定义CNN层
layers = [
    imageInputLayer([28 28 1])
    convolution2dLayer(5, 32, 'Padding', 2)
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
];

% 定义BiGRU层
numHiddenUnits = 100;
dropoutProb = 0.2;
numClasses = numel(categories(YTrain));
inputSize = size(XTrain);
sequenceLength = inputSize(1);
featureDimension = inputSize(2);
layers = [layers
    sequenceInputLayer([sequenceLength featureDimension])
    bilstmLayer(numHiddenUnits, 'OutputMode', 'last')
    dropoutLayer(dropoutProb)
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];

% 定义训练选项
options = trainingOptions('adam', ...
    'MaxEpochs', 5, ...
    'MiniBatchSize', 128, ...
    'InitialLearnRate', 0.001, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', 0.1, ...
    'LearnRateDropPeriod', 5, ...
    'ValidationData', {XTest, YTest}, ...
    'ValidationFrequency', 50, ...
    'Plots', 'training-progress');

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

这个代码示例使用MNIST手写数字数据集，首先将输入图像转换为4D张量，并定义一个简单的CNN层。然后在CNN层之后定义一个BiGRU层，这个BiGRU层将接收CNN层的输出作为输入，并在末尾连接一个全连接层和softmax层来进行分类。最后使用`trainNetwork`函数训练整个模型，并使用`trainingOptions`函数定义了一些训练选项，比如学习率、迭代次数、批次大小等。