帮我写一个c++利用CNN实现数字识别的代码
时间: 2023-08-28 21:21:55 浏览: 39
好的,以下是一个简单的基于CNN的数字识别代码示例,仅供参考:
```c++
#include <iostream>
#include <fstream>
#include <vector>
#include <cmath>
using namespace std;
// 定义卷积核大小和步长
const int kernel_size = 5;
const int stride = 1;
// 定义卷积层和池化层的大小
const int conv_size = 28;
const int pool_size = 14;
// 定义网络结构
const int input_size = 28 * 28; // 输入层大小
const int hidden_size = 100; // 隐藏层大小
const int output_size = 10; // 输出层大小
// 定义激活函数
double sigmoid(double x) {
return 1.0 / (1.0 + exp(-x));
}
// 定义卷积函数
void convolve(vector<vector<double>>& input, vector<vector<double>>& kernel, vector<vector<double>>& output) {
int kernel_half = kernel_size / 2;
for (int i = kernel_half; i < conv_size - kernel_half; i += stride) {
for (int j = kernel_half; j < conv_size - kernel_half; j += stride) {
double sum = 0.0;
for (int k = -kernel_half; k <= kernel_half; ++k) {
for (int l = -kernel_half; l <= kernel_half; ++l) {
sum += input[i + k][j + l] * kernel[kernel_half + k][kernel_half + l];
}
}
output[(i - kernel_half) / stride][(j - kernel_half) / stride] = sigmoid(sum);
}
}
}
// 定义池化函数
void max_pool(vector<vector<double>>& input, vector<vector<double>>& output) {
for (int i = 0; i < pool_size; ++i) {
for (int j = 0; j < pool_size; ++j) {
double max_val = -1.0;
for (int k = 0; k < kernel_size; ++k) {
for (int l = 0; l < kernel_size; ++l) {
max_val = max(max_val, input[i * stride + k][j * stride + l]);
}
}
output[i][j] = max_val;
}
}
}
// 定义前向传播函数
void forward(vector<vector<double>>& input, vector<vector<double>>& W1, vector<vector<double>>& W2, vector<double>& b1, vector<double>& b2,
vector<vector<double>>& conv1_output, vector<vector<double>>& pool1_output, vector<double>& hidden_output, vector<double>& output) {
// 卷积层
convolve(input, W1, conv1_output);
// 池化层
max_pool(conv1_output, pool1_output);
// 隐藏层
for (int i = 0; i < hidden_size; ++i) {
double sum = 0.0;
for (int j = 0; j < pool_size; ++j) {
for (int k = 0; k < pool_size; ++k) {
sum += pool1_output[j][k] * W2[j * pool_size + k][i];
}
}
hidden_output[i] = sigmoid(sum + b1[i]);
}
// 输出层
for (int i = 0; i < output_size; ++i) {
double sum = 0.0;
for (int j = 0; j < hidden_size; ++j) {
sum += hidden_output[j] * W2[j][i];
}
output[i] = sigmoid(sum + b2[i]);
}
}
int main() {
// 读取权重和偏置
vector<vector<double>> W1(kernel_size, vector<double>(kernel_size));
vector<vector<double>> W2(pool_size * pool_size, vector<double>(hidden_size));
vector<double> b1(hidden_size);
vector<double> b2(output_size);
ifstream fin1("W1.txt");
ifstream fin2("W2.txt");
ifstream fin3("b1.txt");
ifstream fin4("b2.txt");
for (int i = 0; i < kernel_size; ++i) {
for (int j = 0; j < kernel_size; ++j) {
fin1 >> W1[i][j];
}
}
for (int i = 0; i < pool_size * pool_size; ++i) {
for (int j = 0; j < hidden_size; ++j) {
fin2 >> W2[i][j];
}
}
for (int i = 0; i < hidden_size; ++i) {
fin3 >> b1[i];
}
for (int i = 0; i < output_size; ++i) {
fin4 >> b2[i];
}
// 读取测试数据并进行预测
ifstream fin5("test.txt");
int correct = 0, total = 0;
while (!fin5.eof()) {
++total;
vector<vector<double>> input(conv_size, vector<double>(conv_size));
for (int i = 0; i < conv_size; ++i) {
for (int j = 0; j < conv_size; ++j) {
fin5 >> input[i][j];
}
}
vector<vector<double>> conv1_output(pool_size, vector<double>(pool_size));
vector<vector<double>> pool1_output(pool_size, vector<double>(pool_size));
vector<double> hidden_output(hidden_size);
vector<double> output(output_size);
forward(input, W1, W2, b1, b2, conv1_output, pool1_output, hidden_output, output);
int predict = 0;
double max_val = -1.0;
for (int i = 0; i < output_size; ++i) {
if (output[i] > max_val) {
max_val = output[i];
predict = i;
}
}
int label;
fin5 >> label;
if (predict == label) {
++correct;
}
}
// 输出准确率
cout << "Accuracy: " << (double)correct / total << endl;
return 0;
}
```
需要注意的是,这只是一个简单的示例,实际上需要进行更多的调参和优化才能得到更好的性能。同时,还需要准备训练数据和标签,并使用反向传播算法进行训练。