C++中實現Adam和RMSProp優化算法
以下是一些關于C++中實現Adam和RMSProp優化算法的實用示例和資源，涵蓋不同場景和應用。由于篇幅限制，完整代碼，但提供關鍵實現片段、庫使用方法和學習資源。
基礎Adam優化器實現
Adam優化器實現
#include <vector>
#include <cmath>class AdamOptimizer {
public:AdamOptimizer(float lr = 0.001, float beta1 = 0.9, float beta2 = 0.999, float eps = 1e-8): learning_rate(lr), beta1(beta1), beta2(beta2), epsilon(eps), t(0) {}void update(std::vector<float>& params, const std::vector<float>& grads) {t++;if (m.empty()) {m.resize(params.size(), 0.0f);v.resize(params.size(), 0.0f);}for (size_t i = 0; i < params.size(); ++i) {m[i] = beta1 * m[i] + (1 - beta1) * grads[i];v[i] = beta2 * v[i] + (1 - beta2) * grads[i] * grads[i];float m_hat = m[i] / (1 - std::pow(beta1, t));float v_hat = v[i] / (1 - std::pow(beta2, t));params[i] -= learning_rate * m_hat / (std::sqrt(v_hat) + epsilon);}}private:float learning_rate, beta1, beta2, epsilon;std::vector<float> m, v;int t;
};
基礎RMSProp實現
class RMSPropOptimizer {
public:RMSPropOptimizer(float lr = 0.001, float decay = 0.9, float eps = 1e-8): learning_rate(lr), decay_rate(decay), epsilon(eps) {}void update(std::vector<float>& params, const std::vector<float>& grads) {if (cache.empty()) {cache.resize(params.size(), 0.0f);}for (size_t i = 0; i < params.size(); ++i) {cache[i] = decay_rate * cache[i] + (1 - decay_rate) * grads[i] * grads[i];params[i] -= learning_rate * grads[i] / (std::sqrt(cache[i]) + epsilon);}}private:float learning_rate, decay_rate, epsilon;std::vector<float> cache;
};
使用Eigen庫實現矩陣優化
#include <Eigen/Dense>void AdamWithEigen(Eigen::MatrixXf& params, const Eigen::MatrixXf& grads) {static Eigen::MatrixXf m = Eigen::MatrixXf::Zero(params.rows(), params.cols());static Eigen::MatrixXf v = Eigen::MatrixXf::Zero(params.rows(), params.cols());static int t = 0;const float lr = 0.001, beta1 = 0.9, beta2 = 0.999, eps = 1e-8;t++;m = beta1 * m + (1 - beta1) * grads;v = beta2 * v + (1 - beta2) * grads.array().square().matrix();Eigen::MatrixXf m_hat = m / (1 - std::pow(beta1, t));Eigen::MatrixXf v_hat = v / (1 - std::pow(beta2, t));params -= lr * m_hat.array() / (v_hat.array().sqrt() + eps);
}
PyTorch C++ API示例
#include <torch/torch.h>torch::Tensor adam_update(torch::Tensor& param, const torch::Tensor& grad) {static torch::Tensor m = torch::zeros_like(param);static torch::Tensor v = torch::zeros_like(param);static int64_t t = 0;t++;m = 0.9 * m + (1 - 0.9) * grad;v = 0.999 * v + (1 - 0.999) * grad.pow(2);auto m_hat = m / (1 - std::pow(0.9, t));auto v_hat = v / (1 - std::pow
)
