C++框架在未来技术中的应用场景有哪些？（c++的

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

#include <tensorflow/core/public/tensor.h> 内容来自zvvq

using namespace tensorflow; 本文来自zvvq

int main() { 内容来自samhan666

// 加载 TensorFlow 模型 内容来自samhan

Session* session = NewSession(SessionOptions()); zvvq.cn

const string model_path = "path/to/model.pb";

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Status status = session->LoadGraphDef(ReadBinaryProto(Env::Default(), model_path), {});

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if (!status.ok()) {

throw runtime_error("Error loading model: " + status.ToString());

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} 本文来自zvvq

// 加载图像数据

string image_path = "path/to/image.jpg"; zvvq.cn

Image img = LoadImage(image_path);

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// 预处理图像

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Tensor input_tensor = PreprocessImage(img); 内容来自zvvq

// 推断 内容来自zvvq，别采集哟

vector<Tensor> output_tensors; zvvq.cn

status = session->Run({{input_tensor}}, {"output"}, {}, &output_tensors);

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if (!status.ok()) { 内容来自samhan

throw runtime_error("Error running inference: " + status.ToString());

} 内容来自zvvq，别采集哟

// 获取结果

const Tensor& predictions = output_tensors[0];

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auto predictions_as_vector = predictions.flat<float>();

// 输出类标签 本文来自zvvq

int best_class = -1; 内容来自zvvq

float best_probability = -1;

for (int i = 0; i < predictions_as_vector.size(); ++i) { zvvq

if (predictions_as_vector(i) > best_probability) {

best_class = i; 内容来自samhan

best_probability = predictions_as_vector(i); zvvq

}

}

cout << "Predicted class: " << best_class << endl; copyright zvvq

cout << "Predicted probability: " << best_probability << endl;

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// 回收内存

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session->Close();

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return 0; 内容来自zvvq

}