由void readDataset(string imagesFile, string labelsFile, vector<Mat>& images, vector<int>& labels) void convolve(Mat& input, Mat filter, Mat& output) void pool(Mat& input, Mat& output) void fullyConnected(Mat& input, Mat& weight, Mat& bias, Mat& output) void softmax(Mat& input, Mat& output) void cnnModel(Mat& input, Mat& output)组成的C++手写数字识别main函数实现

好的，以下是一个简单的手写数字识别的C++程序实现：

#include <iostream>
#include <fstream>
#include <vector>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

// 读取MNIST数据集
void readDataset(string imagesFile, string labelsFile, vector<Mat>& images, vector<int>& labels)
{
    ifstream ifsImages(imagesFile, ios::binary);
    ifstream ifsLabels(labelsFile, ios::binary);

    if (!ifsImages.is_open() || !ifsLabels.is_open()) {
        cout << "Failed to open file." << endl;
        return;
    }

    int magicImages, magicLabels, numImages, numLabels;
    ifsImages.read((char*)&magicImages, sizeof(magicImages));
    ifsLabels.read((char*)&magicLabels, sizeof(magicLabels));
    ifsImages.read((char*)&numImages, sizeof(numImages));
    ifsLabels.read((char*)&numLabels, sizeof(numLabels));
    magicImages = reverseInt(magicImages);
    magicLabels = reverseInt(magicLabels);
    numImages = reverseInt(numImages);
    numLabels = reverseInt(numLabels);

    for (int i = 0; i < numImages; ++i) {
        Mat image = Mat::zeros(28, 28, CV_8UC1);
        for (int r = 0; r < 28; ++r) {
            for (int c = 0; c < 28; ++c) {
                unsigned char pixel;
                ifsImages.read((char*)&pixel, sizeof(pixel));
                image.at<uchar>(r, c) = pixel;
            }
        }
        images.push_back(image);
    }

    for (int i = 0; i < numLabels; ++i) {
        unsigned char label;
        ifsLabels.read((char*)&label, sizeof(label));
        labels.push_back((int)label);
    }
}

// 卷积操作
void convolve(Mat& input, Mat filter, Mat& output)
{
    int filterSize = filter.rows;
    int pad = filterSize / 2;
    Mat paddedInput;
    copyMakeBorder(input, paddedInput, pad, pad, pad, pad, BORDER_CONSTANT, Scalar(0));
    output.create(input.rows, input.cols, input.type());

    for (int r = pad; r < paddedInput.rows - pad; ++r) {
        for (int c = pad; c < paddedInput.cols - pad; ++c) {
            double sum = 0.0;
            for (int fr = 0; fr < filterSize; ++fr) {
                for (int fc = 0; fc < filterSize; ++fc) {
                    int ir = r - pad + fr;
                    int ic = c - pad + fc;
                    sum += paddedInput.at<double>(ir, ic) * filter.at<double>(fr, fc);
                }
            }
            output.at<double>(r - pad, c - pad) = sum;
        }
    }
}

// 池化操作
void pool(Mat& input, Mat& output)
{
    output.create(input.rows / 2, input.cols / 2, input.type());

    for (int r = 0; r < input.rows; r += 2) {
        for (int c = 0; c < input.cols; c += 2) {
            double maxVal = 0.0;
            for (int pr = 0; pr < 2; ++pr) {
                for (int pc = 0; pc < 2; ++pc) {
                    double val = input.at<double>(r + pr, c + pc);
                    if (val > maxVal) {
                        maxVal = val;
                    }
                }
            }
            output.at<double>(r / 2, c / 2) = maxVal;
        }
    }
}

// 全连接层操作
void fullyConnected(Mat& input, Mat& weight, Mat& bias, Mat& output)
{
    output.create(1, weight.cols, CV_64FC1);

    for (int c = 0; c < weight.cols; ++c) {
        double sum = 0.0;
        for (int r = 0; r < weight.rows; ++r) {
            sum += input.at<double>(r) * weight.at<double>(r, c);
        }
        sum += bias.at<double>(0, c);
        output.at<double>(0, c) = sum;
    }
}

// Softmax操作
void softmax(Mat& input, Mat& output)
{
    output.create(1, input.cols, CV_64FC1);
    double sum = 0.0;

    for (int c = 0; c < input.cols; ++c) {
        double expVal = exp(input.at<double>(0, c));
        output.at<double>(0, c) = expVal;
        sum += expVal;
    }

    for (int c = 0; c < input.cols; ++c) {
        output.at<double>(0, c) /= sum;
    }
}

// CNN模型
void cnnModel(Mat& input, Mat& output)
{
    // 卷积层1
    Mat filter1 = (Mat_<double>(5, 5) << -1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,24,24,24,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1);
    Mat conv1;
    convolve(input, filter1, conv1);

    // 激活函数ReLU
    Mat relu1;
    cv::max(0, conv1, relu1);

    // 池化层1
    Mat pool1;
    pool(relu1, pool1);

    // 卷积层2
    Mat filter2 = (Mat_<double>(5, 5) << -1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,24,24,24,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1);
    Mat conv2;
    convolve(pool1, filter2, conv2);

    // 激活函数ReLU
    Mat relu2;
    cv::max(0, conv2, relu2);

    // 池化层2
    Mat pool2;
    pool(relu2, pool2);

    // 全连接层1
    Mat fcInput = pool2.reshape(1, 1);
    Mat weight1 = (Mat_<double>(fcInput.cols, 128) << ...);
    Mat bias1 = (Mat_<double>(1, 128) << ...);
    Mat fc1;
    fullyConnected(fcInput, weight1, bias1, fc1);

    // 激活函数ReLU
    Mat relu3;
    cv::max(0, fc1, relu3);

    // 全连接层2
    Mat weight2 = (Mat_<double>(relu3.cols, 10) << ...);
    Mat bias2 = (Mat_<double>(1, 10) << ...);
    Mat fc2;
    fullyConnected(relu3, weight2, bias2, fc2);

    // Softmax
    softmax(fc2, output);
}

int main()
{
    // 读取MNIST数据集
    vector<Mat> images;
    vector<int> labels;
    readDataset("train-images-idx3-ubyte", "train-labels-idx1-ubyte", images, labels);

    // 训练CNN模型
    for (int i = 0; i < images.size(); ++i) {
        Mat input = images[i] / 255.0;
        Mat output;
        cnnModel(input, output);

        // 计算损失函数
        double loss = 0.0;
        for (int c = 0; c < output.cols; ++c) {
            int label = (c == labels[i]) ? 1 : 0;
            loss += -label * log(output.at<double>(0, c));
        }

        // 反向传播更新权重和偏置
        // ...
    }

    return 0;
}

需要注意的是，这只是一个简单的手写数字识别程序实现，具体的实现细节还需要根据实际需求进行调整和完善。