c++训练CNNmnist手写数字识别的函数

以下是使用C++训练CNN进行MNIST手写数字识别的函数：

void train() {
    // 加载MNIST数据集
    std::vector<std::vector<double>> train_images, test_images;
    std::vector<int> train_labels, test_labels;
    load_mnist(train_images, train_labels, test_images, test_labels);

    // 初始化CNN模型
    int input_size = 28;
    int output_size = 10;
    int num_filters = 16;
    double learning_rate = 0.1;
    int num_epochs = 10;
    int batch_size = 10;
    ConvolutionalNeuralNetwork cnn(input_size, output_size, num_filters, learning_rate);

    // 训练CNN模型
    for (int epoch = 0; epoch < num_epochs; epoch++) {
        double loss = 0.0;
        std::vector<int> indices(train_images.size());
        std::iota(indices.begin(), indices.end(), 0);
        std::shuffle(indices.begin(), indices.end(), rng);

        for (int batch_start = 0; batch_start < indices.size(); batch_start += batch_size) {
            int batch_end = std::min(batch_start + batch_size, (int)indices.size());
            std::vector<std::vector<double>> batch_images(batch_end - batch_start, std::vector<double>(input_size * input_size));
            std::vector<int> batch_labels(batch_end - batch_start);

            for (int i = batch_start; i < batch_end; i++) {
                batch_images[i - batch_start] = train_images[indices[i]];
                batch_labels[i - batch_start] = train_labels[indices[i]];
            }

            loss += cnn.train(batch_images, batch_labels);
        }

        double accuracy = cnn.test(test_images, test_labels);

        std::cout << "Epoch " << epoch + 1 << " loss: " << loss / train_images.size() << " accuracy: " << accuracy << std::endl;
    }
}

该函数使用了一个名为 ConvolutionalNeuralNetwork 的自定义 CNN 类，使用了MNIST数据集进行训练，并在每个 epoch 结束时输出损失和准确率。你需要自己实现该类并确保其能够正确地训练和测试 MNIST 数据集。