为什么以下代码中报错“检查对函数 'predict' 的调用中是否缺失参数或参数数据类型不正确。” % 生成数据 x = linspace(-10, 10, 100)'; y = 2 * x + 5 + randn(size(x)); % 定义生成器网络 generator = [ imageInputLayer([1, 1, 100], 'Normalization', 'none') fullyConnectedLayer(100) leakyReluLayer() fullyConnectedLayer(100) leakyReluLayer() fullyConnectedLayer(2) ]; % 定义判别器网络 discriminator = [ imageInputLayer([1, 1, 2], 'Normalization', 'none') fullyConnectedLayer(100) leakyReluLayer() fullyConnectedLayer(100) leakyReluLayer() fullyConnectedLayer(1) sigmoidLayer() ]; % 设置训练参数 numEpochs = 100; numSamples = size(x, 1); miniBatchSize = 64; numMiniBatches = floor(numSamples / miniBatchSize); learnRate = 0.001; % 训练GAN网络 for epoch = 1:numEpochs % 随机打乱数据 idx = randperm(numSamples); xShuffled = x(idx); yShuffled = y(idx); % 每个epoch内的每个mini-batch for miniBatch = 1:numMiniBatches % 获取当前mini-batch的数据 idxStart = (miniBatch - 1) * miniBatchSize + 1; idxEnd = miniBatch * miniBatchSize; xBatch = xShuffled(idxStart:idxEnd); yBatch = yShuffled(idxStart:idxEnd); % 生成假样本 noise = randn(1, 1, miniBatchSize); yGenerated = predict(generator, noise); % 合并真实样本和假样本 xCombined = cat(3, xBatch, yBatch); yCombined = cat(3, xBatch, yGenerated); % 训练判别器 discriminatorGradients = dlgradient(@(W) discriminatorLoss(W, xCombined, yCombined), discriminator.Learnables); discriminator.Learnables = adamupdate(discriminator.Learnables, discriminatorGradients, learnRate); % 训练生成器 generatorGradients = dlgradient(@(W) generatorLoss(W, xCombined, yCombined), generator.Learnables); generator.Learnables = adamupdate(generator.Learnables, generatorGradients, learnRate); end % 打印当前epoch的损失 fprintf('Epoch %d/%d\n', epoch, numEpochs); end

在你提供的代码中，报错是因为在生成假样本时使用了 predict 函数，但是生成器网络的输入应该是一个大小为 [1, 1, miniBatchSize] 的张量，而不是一个大小为 [1, 1, 100] 的张量。

为了解决这个问题，你需要将生成器网络的输入层修改为适应当前代码的需求。下面是修改后的代码示例：

% 生成数据
x = linspace(-10, 10, 100)';
y = 2 * x + 5 + randn(size(x));

% 定义生成器网络
generator = [
    imageInputLayer([1, 1, 1], 'Normalization', 'none')
    fullyConnectedLayer(100)
    leakyReluLayer()
    fullyConnectedLayer(100)
    leakyReluLayer()
    fullyConnectedLayer(2)
];

% 定义判别器网络
discriminator = [
    imageInputLayer([1, 1, 2], 'Normalization', 'none')
    fullyConnectedLayer(100)
    leakyReluLayer()
    fullyConnectedLayer(100)
    leakyReluLayer()
    fullyConnectedLayer(1)
    sigmoidLayer()
];

% 设置训练参数
numEpochs = 100;
numSamples = size(x, 1);
miniBatchSize = 64;
numMiniBatches = floor(numSamples / miniBatchSize);
learnRate = 0.001;

% 训练GAN网络
for epoch = 1:numEpochs
    % 随机打乱数据
    idx = randperm(numSamples);
    xShuffled = x(idx);
    yShuffled = y(idx);
    
    % 每个epoch内的每个mini-batch
    for miniBatch = 1:numMiniBatches
        % 获取当前mini-batch的数据
        idxStart = (miniBatch - 1) * miniBatchSize + 1;
        idxEnd = miniBatch * miniBatchSize;
        xBatch = xShuffled(idxStart:idxEnd);
        yBatch = yShuffled(idxStart:idxEnd);
        
        % 生成假样本
        noise = randn(1, 1, miniBatchSize);
        yGenerated = predict(generator, dlarray(noise));
        
        % 合并真实样本和假样本
        xCombined = cat(3, xBatch, yBatch);
        yCombined = cat(3, xBatch, yGenerated);
        
        % 训练判别器
        discriminatorGradients = dlgradient(@(W) discriminatorLoss(W, xCombined, yCombined), discriminator.Learnables);
        discriminator.Learnables = adamupdate(discriminator.Learnables, discriminatorGradients, learnRate);
        
        % 训练生成器
        generatorGradients = dlgradient(@(W) generatorLoss(W, xCombined, yCombined), generator.Learnables);
        generator.Learnables = adamupdate(generator.Learnables, generatorGradients, learnRate);
    end
    
    % 打印当前epoch的损失
    fprintf('Epoch %d/%d\n', epoch, numEpochs);
end

在这个修改后的代码中，我们将生成器网络的输入层调整为适应当前代码的需求，即大小为 [1, 1, 1]。然后，我们使用 dlarray 函数将噪声转换为深度学习数组，以满足 predict 函数的输入要求。