如何在c++中加载咖啡因模型进行预测

#include <tensorflow/core/public/session.h>
#include <tensorflow/core/platform/env.h>

// 加载模型
tensorflow::Session* LoadCaffeineModel() {
  tensorflow::Session* session;
  tensorflow::Status status = tensorflow::NewSession(tensorflow::SessionOptions(), &session);
  if (!status.ok()) {
    std::cerr << "Failed to create TensorFlow session: " << status.ToString() << std::endl;
    return nullptr;
  }

  tensorflow::GraphDef graph_def;
  status = tensorflow::ReadBinaryProto(tensorflow::Env::Default(), "caffeine_model.pb", &graph_def);
  if (!status.ok()) {
    std::cerr << "Failed to load model structure: " << status.ToString() << std::endl;
    return nullptr;
  }

  status = session->Create(graph_def);
  if (!status.ok()) {
    std::cerr << "Failed to load model parameters: " << status.ToString() << std::endl;
    return nullptr;
  }

  return session;
}

// 进行预测
void PredictCaffeineLevel(tensorflow::Session* session, const std::vector<float>& input_data) {
  tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, input_data.size()}));
  std::copy(input_data.begin(), input_data.end(), input_tensor.flat<float>().data());

  std::vector<tensorflow::Tensor> outputs;
  tensorflow::Status status = session->Run({{"input", input_tensor}}, {"output"}, {}, &outputs);
  if (!status.ok()) {
    std::cerr << "Failed to run prediction: " << status.ToString() << std::endl;
    return;
  }

  // 处理预测结果
  tensorflow::Tensor output_tensor = outputs[0];
  const float* predictions = output_tensor.flat<float>().data();
  // TODO: 处理预测结果
}