Warning: file_put_contents(aitw/63908.html): Failed to open stream: Permission denied in /www/wwwroot/zansou.com.cn/aitwxq.php on line 205
c++ai代码,利用ai制作短视频,扣子ai教程,零基础学ai,ai画图app

用户提问: c++ai代码

Ai回答: Creating AI-related code in C++ can cover a wide range of applications, from basic machine learning algorithms to more complex neural networks、For simplicity, let's focus on a basic example of a neural network implemented in C++、This example will be quite rudimentary and is intended to introduce you to the concept、

This example will create a simple neural network with one input layer, one hidden layer, and one output layer、It will be trained on a simple dataset for a binary classification problem.

Dependencies

For this example, we'll keep dependencies minimal、However, for more complex AI projects, you might want to consider using libraries like TensorFlow, OpenCV, or Caffe、Here, we'll stick with the Standard Template Library (STL) and basic C++ for simplicity.

Simple Neural Network Example

cpp
#include <iostream>
#include <vector>
#include <cmath>
#include <random>

// Sigmoid function and its derivative
double sigmoid(double x) {
return 1 / (1 + exp(-x));
}

double sigmoidDerivative(double x) {
return x * (1 x);
}

class NeuralNetwork {
public:
int inputs, hidden, outputs;
std::vector<std::vector<double>> weightsIH, weightsHO;
std::vector<double> hiddenLayer, outputLayer;

NeuralNetwork(int i, int h, int o) : inputs(i), hidden(h), outputs(o) {
// Initialize weights randomly
std::random_device rd;
std::mt19937 gen(rd());
std::normal_distribution<double> dis(0.0, 1.0);

weightsIH.resize(inputs, std::vector<double>(hidden, 0.0));
weightsHO.resize(hidden, std::vector<double>(outputs, 0.0));

for (int i = 0; i < inputs; ++i) {
for (int j = 0; j < hidden; ++j) {
weightsIH[i][j] = dis(gen);
}
}

for (int i = 0; i < hidden; ++i) {
for (int j = 0; j < outputs; ++j) {
weightsHO[i][j] = dis(gen);
}
}

hiddenLayer.resize(hidden, 0.0);
outputLayer.resize(outputs, 0.0);
}

void feedForward(const std::vector<double>& input) {
for (int i = 0; i < hidden; ++i) {
double sum = 0.0;
for (int j = 0; j < inputs; ++j) {
sum += input[j] * weightsIH[j][i];
}
hiddenLayer[i] = sigmoid(sum);
}

for (int i = 0; i < outputs; ++i) {
double sum = 0.0;
for (int j = 0; j < hidden; ++j) {
sum += hiddenLayer[j] * weightsHO[j][i];
}
outputLayer[i] = sigmoid(sum);
}
}

void train(const std::vector<double>& input, const std::vector<double>& target, double learningRate) {
// Calculate output layer errors
std::vector<double> outputErrors(outputs, 0.0);
for (int i = 0; i < outputs; ++i) {
double error = target[i] outputLayer[i];
outputErrors[i] = error * sigmoidDerivative(outputLayer[i]);
}

// Adjust hidden->output weights
for (int i = 0; i < hidden; ++i) {
for (int j = 0; j < outputs; ++j) {
weightsHO[i][j] += learningRate * hiddenLayer[i] * outputErrors[j];
}
}

// Calculate hidden layer errors
std::vector<double> hiddenErrors(hidden, 0.0);
for (int i = 0; i < outputs; ++i) {
for (int j = 0; j < hidden; ++j) {
hiddenErrors[j] += outputErrors[i] * weightsHO[j][i];
}
}

// Adjust input->hidden weights
for (int i = 0; i < inputs; ++i) {
for (int j = 0; j < hidden; ++j) {
weightsIH[i][j] += learningRate * input[i] * hiddenErrors[j] * sigmoidDerivative(hiddenLayer[j]);
}
}
}

void printOutput() {
for (double val : outputLayer) {
std::cout << val << ;
}
std::cout << std::endl;
}
};

int main() {
// Example usage
NeuralNetwork nn(2, 2, 1); // 2 inputs, 2 hidden units, 1 output

// Example training data (XOR function)
std::vector<std::vector<double>> inputs = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
std::vector<std::vector<double>> targets = {{0}, {1}, {1}, {0}};

for (int epoch = 0; epoch < 10000; ++epoch) {
for (int i = 0; i < inputs.size(); ++i) {
nn.feedForward(inputs[i]);
nn.train(inputs[i], targets[i], 1.0); // Learning rate of 1.0 for simplicity
}
}

// Test
for (const auto& input : inputs) {
nn.feedForward(input);
nn.printOutput();
}

return 0;
}

Explanation

NeuralNetwork Class: This class represents a simple neural network with one hidden layer.
feedForward: This method performs a forward pass through the network, calculating the output based on the current weights and inputs.
train: This method adjusts the weights based on the error and learning rate, performing a simple backpropagation.
sigmoid & sigmoidDerivative: Activation function and its derivative used for calculating outputs and gradients.

Note

This is a very basic implementation、Real-world applications would likely use more sophisticated libraries (like TensorFlow or PyTorch, which have C++ APIs) for efficiency and practicality.
The example uses the XOR function as a training dataset for simplicity、The XOR function is a basic example often used in neural network explanations because it's not linearly separable and thus requires a hidden layer.


0

IP地址: 239.246.6.56

搜索次数: 23

提问时间: 2025-04-20 20:26:21

热门提问
坤恒顺维
国内ai公司排名
如何制造ai
机器人性能
ai品牌排名

友情链接
搜索引擎平台  검색 엔진 소스 코드 

关于我们:
域名 工具 日记 价格 加盟 联系

加入群聊
群
上一篇639076390863909下一篇