c++神经网络算法实现-Toy模板网

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#include <iostream>
#include <vector>
#include <cmath>
#include <cstdlib>
#include <ctime>
#include <algorithm>
#include <Eigen/Dense>

using namespace std;
using namespace Eigen;

// 定义常量
const int INPUT_NUM = 784; // 输入层节点数
const int HIDDEN_NUM = 100; // 隐藏层节点数
const int OUTPUT_NUM = 10; // 输出层节点数
const double LEARNING_RATE = 0.1; // 学习率
const int EPOCH_NUM = 20; // 迭代次数
const int BATCH_SIZE = 100; // 批量大小

// 定义神经网络类
class NeuralNetwork {
public:
NeuralNetwork(int inputNum, int hiddenNum, int outputNum) {
inputNum_ = inputNum;
hiddenNum_ = hiddenNum;
outputNum_ = outputNum;

// 初始化权重和偏置
srand(time(NULL));
for (int i = 0; i < inputNum_; i++) {
for (int j = 0; j < hiddenNum_; j++) {
w1_(i, j) = (rand() / double(RAND_MAX)) * 2 - 1;
}
}
for (int i = 0; i < hiddenNum_; i++) {
for (int j = 0; j < outputNum_; j++) {
w2_(i, j) = (rand() / double(RAND_MAX)) * 2 - 1;
}
}
for (int i = 0; i < hiddenNum_; i++) {
b1_[i] = (rand() / double(RAND_MAX)) * 2 - 1;
}
for (int i = 0; i < outputNum_; i++) {
b2_[i] = (rand() / double(RAND_MAX)) * 2 - 1;
}
}

// 前向传播
void forwardPropagation(MatrixXd& x) {
for (int i = 0; i < hiddenNum_; i++) {
double z = 0;
for (int j = 0; j < inputNum_; j++) {
z += x(j, 0) * w1_(j, i);
}
z += b1_[i];
h_(i, 0) = sigmoid(z);
}
for (int i = 0; i < outputNum_; i++) {
double z = 0;
for (int j = 0; j < hiddenNum_; j++) {
z += h_(j, 0) * w2_(j, i);
}
z += b2_[i];
y_(i, 0) = softmax(z);
}
}

// 反向传播
void backwardPropagation(MatrixXd& x, int t) {
MatrixXd delta2 = y_ - oneHot(t);
MatrixXd delta1 = (w2_ * delta2).cwiseProduct(h_.array() * (1 - h_.array())).matrix();
w2_ -= LEARNING_RATE * h_ * delta2.transpose();
w1_ -= LEARNING_RATE * x * delta1.transpose();
b2_ -= LEARNING_RATE * delta2;
b1_ -= LEARNING_RATE * delta1.col(0);
}

// 训练
void train(vector<MatrixXd>& xTrain, vector<int>& tTrain) {
for (int epoch = 0; epoch < EPOCH_NUM; epoch++) {
shuffle(xTrain, tTrain); // 打乱训练集
for (int i = 0; i < xTrain.size(); i += BATCH_SIZE) {
int batchSize = min(BATCH_SIZE, int(xTrain.size() - i));
for (int j = 0; j < batchSize; j++) {
forwardPropagation(xTrain[i + j]);
backwardPropagation(xTrain[i + j], tTrain[i + j]);
}
}
double accuracy = test(xTrain, tTrain); // 计算准确率
cout << "Epoch: " << epoch + 1 << ", Accuracy: " << accuracy << endl;
}
}

// 预测
int predict(MatrixXd& x) {
forwardPropagation(x);
int maxIndex = 0;
double maxValue = y_(0, 0);
for (int i = 1; i < outputNum_; i++) {
if (y_(i, 0) > maxValue) {
maxIndex = i;
maxValue = y_(i, 0);
}
}
return maxIndex;
}

private:
int inputNum_, hiddenNum_, outputNum_;
MatrixXd w1_ = MatrixXd::Zero(INPUT_NUM, HIDDEN_NUM), w2_ = MatrixXd::Zero(HIDDEN_NUM, OUTPUT_NUM);
VectorXd b1_ = VectorXd::Zero(HIDDEN_NUM), b2_ = VectorXd::Zero(OUTPUT_NUM);
MatrixXd h_ = MatrixXd::Zero(HIDDEN_NUM, 1), y_ = MatrixXd::Zero(OUTPUT_NUM, 1);

// 激活函数 sigmoid
double sigmoid(double x) {
return 1 / (1 + exp(-x));
}

// 激活函数 softmax
double softmax(double x) {
return exp(x) / exp(1);
}

// 打乱训练集
void shuffle(vector<MatrixXd>& xTrain, vector<int>& tTrain) {
for (int i = 0; i < xTrain.size(); i++) {
int j = rand() % xTrain.size();
swap(xTrain[i], xTrain[j]);
swap(tTrain[i], tTrain[j]);
}
}

// 测试
double test(vector<MatrixXd>& xTest, vector<int>& tTest) {
int correctCount = 0;
for (int i = 0; i < xTest.size(); i++) {
if (predict(xTest[i]) == tTest[i]) {
correctCount++;
}
}
return double(correctCount) / xTest.size();
}

// one-hot编码
MatrixXd oneHot(int t) {
MatrixXd oneHotVec = MatrixXd::Zero(outputNum_, 1);
oneHotVec(t, 0) = 1;
return oneHotVec;
}
};

// 测试函数
void test() {
vector<MatrixXd> xTrain, xTest;
vector<int> tTrain, tTest;
int row, col;
cin >> row >> col;
MatrixXd input(row, col);
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++)

{
 cin >> input(i, j);
 } 
} 
xTrain.push_back(input.col(col - 1));
 // 将最后一列作为训练数据
 tTrain.push_back(0); 
// 标签为0
 NeuralNetwork nn(INPUT_NUM, HIDDEN_NUM, OUTPUT_NUM); nn.train(xTrain, tTrain); int prediction = nn.predict(xTrain[0]); cout << "Prediction: " << prediction << endl; }

int main() {

test();

return 0;

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