写一个CNN数字识别的c++程序

好的，以下是一个简单的CNN数字识别的C++程序：

#include <iostream>
#include <fstream>
#include <vector>
#include <cmath>

using namespace std;

// 定义卷积核，大小为3*3
const int kernelSize = 3;
const int kernel[kernelSize][kernelSize] = {
    {-1, -1, -1},
    {-1,  8, -1},
    {-1, -1, -1}
};

// 定义图像大小
const int imageSize = 28;

// 定义卷积后的图像大小
const int convSize = imageSize - kernelSize + 1;

// 定义池化操作的大小
const int poolSize = 2;

// 定义池化后的图像大小
const int poolConvSize = convSize / poolSize;

// 定义全连接层的大小
const int fcSize = 10;

// 定义输入图像
int image[imageSize][imageSize];

// 定义卷积后的图像
int convImage[convSize][convSize];

// 定义池化后的图像
int poolImage[poolConvSize][poolConvSize];

// 定义全连接层的权重
int fcWeights[poolConvSize * poolConvSize][fcSize];

// 定义全连接层的偏置
int fcBiases[fcSize];

// 定义softmax函数
void softmax(float *output)
{
    float sum = 0;
    for (int i = 0; i < fcSize; i++)
    {
        output[i] = exp(output[i]);
        sum += output[i];
    }
    for (int i = 0; i < fcSize; i++)
    {
        output[i] /= sum;
    }
}

// 读取图像文件
void readImage(string filename)
{
    ifstream file(filename.c_str(), ios::binary);
    if (!file.is_open())
    {
        cout << "Error: cannot open file " << filename << endl;
        exit(1);
    }
    char buffer[16];
    file.read(buffer, 16);
    for (int i = 0; i < imageSize; i++)
    {
        for (int j = 0; j < imageSize; j++)
        {
            unsigned char pixel;
            file.read((char*)&pixel, sizeof(pixel));
            image[i][j] = pixel;
        }
    }
    file.close();
}

// 卷积操作
void convolve()
{
    for (int i = 0; i < convSize; i++)
    {
        for (int j = 0; j < convSize; j++)
        {
            int sum = 0;
            for (int k = 0; k < kernelSize; k++)
            {
                for (int l = 0; l < kernelSize; l++)
                {
                    sum += kernel[k][l] * image[i + k][j + l];
                }
            }
            convImage[i][j] = sum;
        }
    }
}

// 池化操作
void pool()
{
    for (int i = 0; i < poolConvSize; i++)
    {
        for (int j = 0; j < poolConvSize; j++)
        {
            int maxVal = 0;
            for (int k = 0; k < poolSize; k++)
            {
                for (int l = 0; l < poolSize; l++)
                {
                    maxVal = max(maxVal, convImage[i * poolSize + k][j * poolSize + l]);
                }
            }
            poolImage[i][j] = maxVal;
        }
    }
}

// 加载全连接层的权重和偏置
void loadWeightsAndBiases()
{
    ifstream file("weights.bin", ios::binary);
    if (!file.is_open())
    {
        cout << "Error: cannot open file weights.bin" << endl;
        exit(1);
    }
    for (int i = 0; i < poolConvSize * poolConvSize; i++)
    {
        for (int j = 0; j < fcSize; j++)
        {
            file.read((char*)&fcWeights[i][j], sizeof(fcWeights[i][j]));
        }
    }
    file.close();
    file.open("biases.bin", ios::binary);
    if (!file.is_open())
    {
        cout << "Error: cannot open file biases.bin" << endl;
        exit(1);
    }
    for (int i = 0; i < fcSize; i++)
    {
        file.read((char*)&fcBiases[i], sizeof(fcBiases[i]));
    }
    file.close();
}

// 前向传播
void forward()
{
    convolve();
    pool();
    float output[fcSize] = {0};
    for (int i = 0; i < poolConvSize; i++)
    {
        for (int j = 0; j < poolConvSize; j++)
        {
            for (int k = 0; k < fcSize; k++)
            {
                output[k] += poolImage[i][j] * fcWeights[i * poolConvSize + j][k];
            }
        }
    }
    for (int i = 0; i < fcSize; i++)
    {
        output[i] += fcBiases[i];
    }
    softmax(output);
    for (int i = 0; i < fcSize; i++)
    {
        cout << "Probability of " << i << ": " << output[i] << endl;
    }
}

int main(int argc, char **argv)
{
    if (argc < 2)
    {
        cout << "Usage: " << argv[0] << " image_file" << endl;
        return 0;
    }
    readImage(argv[1]);
    loadWeightsAndBiases();
    forward();
    return 0;
}

这个程序实现了一个简单的CNN数字识别器，它读取一个28*28的数字图像文件，使用一个3*3的卷积核进行卷积操作，然后使用一个2*2的池化操作，最后通过一个全连接层进行分类，使用softmax函数输出每个数字的概率。程序中的权重和偏置是预先训练好的。你可以将图像文件作为参数传递给程序来进行测试。