C++框架最佳实践在人工智能和机器学习中的应用

// 包含必要的库
#include <cmath>
#include <vector>
#include <iostream>

// 使用 TensorFlow 创建一个简单的线性回归模型
int main() {
  // 创建一个 TensorFlow 会话
  tensorflow::Session session;

  // 准备训练数据
  std::vector<float> x = {1, 2, 3, 4, 5};
  std::vector<float> y = {2, 4, 6, 8, 10};

  // 创建线性回归模型
  tensorflow::GraphDef graph;
  auto placeholder_x = tensorflow::Placeholder(tensorflow::DT_FLOAT, tensorflow::TensorShape({nullptr, 1}));
  auto placeholder_y = tensorflow::Placeholder(tensorflow::DT_FLOAT, tensorflow::TensorShape({nullptr, 1}));
  auto variable_w = tensorflow::Variable(tensorflow::DT_FLOAT, tensorflow::TensorShape({1}), tensorflow::Constant(0.0f));
  auto variable_b = tensorflow::Variable(tensorflow::DT_FLOAT, tensorflow::TensorShape({1}), tensorflow::Constant(0.0f));
  auto y_pred = placeholder_x * variable_w + variable_b;
  auto loss = tensorflow::reduce_mean(tensorflow::square(y_pred - placeholder_y));

  // 为模型创建训练操作
  auto optimizer = tensorflow::train::GradientDescentOptimizer(0.01f);
  auto train_op = optimizer->Minimize(loss);

  // 初始化变量
  tensorflow::Status status = session.Run(tensorflow::OperationDef({{"init", tensorflow::op::InitOp()}}), nullptr);

  // 训练模型
  for (int i = 0; i < 1000; ++i) {
    status = session.Run({{"train", train_op}, {"loss", loss}},
                        {
                            {{"x", tensorflow::Input(x)}},
                            {{"y", tensorflow::Input(y)}}});
  }

  // 打印已学习的权重和偏差
  status = session.Run({{"w", variable_w}, {"b", variable_b}}, nullptr);
  std::cout << "w: " << variable_w << ", b: " << variable_b << std::endl;
  return 0;
}