C++框架与机器学习和人工智能的契合度？

// 导入必要的TensorFlow库
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/framework/graph.pb.h>
#include <tensorflow/core/framework/tensor.h>
#include <tensorflow/core/public/session.h>

// 定义一些常量
const string model_path = "model.pb";  // 模型文件路径
const string image_path = "image.jpg";  // 图片文件路径
const int num_classes = 10;  // 模型的类别数

// 加载模型
tensorflow::GraphDef graph_def;
tensorflow::Status status = tensorflow::ReadBinaryProto(
    tensorflow::Env::Default(), model_path, &graph_def);
if (!status.ok()) {
  throw std::runtime_error("Failed to load model: " + status.ToString());
}

// 创建新的TensorFlow会话
tensorflow::SessionOptions options;
std::unique_ptr<tensorflow::Session> session(
    tensorflow::NewSession(options));

// 加载图并初始化变量
status = session->Create(graph_def);
if (!status.ok()) {
  throw std::runtime_error("Failed to create session: " + status.ToString());
}
status = session->Run({}, {}, {"init"}, nullptr);
if (!status.ok()) {
  throw std::runtime_error("Failed to initialize variables: " +
                           status.ToString());
}

// 准备输入图像
tensorflow::Tensor input_tensor;
tensorflow::Status image_status =
    tensorflow::ReadFile(tensorflow::Env::Default(), image_path,
                         &input_tensor);
if (!image_status.ok()) {
  throw std::runtime_error("Failed to read image file: " +
                           image_status.ToString());
}

// 运行模型并获取预测结果
std::vector<tensorflow::Tensor> output_tensors;
status = session->Run({{"input", input_tensor}}, {"output"}, {},
                      &output_tensors);
if (!status.ok()) {
  throw std::runtime_error("Failed to run model: " + status.ToString());
}

// 解释预测结果
const tensorflow::Tensor& output_tensor = output_tensors[0];
tensorflow::TTypes<tensorflow::float>::Flat predicted_scores =
    output_tensor.flat<tensorflow::float>();
int predicted_class = -1;
float highest_score = -std::numeric_limits<float>::infinity();
for (int i = 0; i < num_classes; ++i) {
  if (predicted_scores(i) > highest_score) {
    highest_score = predicted_scores(i);
    predicted_class = i;
  }
}
std::cout << "Predicted class: " << predicted_class << std::endl;