C++ で機械学習モデルを構築し、大規模なデータを処理するにはどうすればよいですか?

// 导入 TensorFlow 库
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/public/graph_def_builder.h"
#include "tensorflow/core/public/tensor.h"

// 定义模型架构
GraphDefBuilder builder;
auto input = builder.AddPlaceholder(DataType::DT_FLOAT, TensorShape({1, 32, 32, 3}));
auto conv1 = builder.Conv2D(input, 32, {3, 3}, {1, 1}, "SAME");
auto conv2 = builder.Conv2D(conv1, 64, {3, 3}, {1, 1}, "SAME");
auto pool = builder.MaxPool(conv2, {2, 2}, {2, 2}, "SAME");
auto flattened = builder.Flatten(pool);
auto dense1 = builder.FullyConnected(flattened, 128, "relu");
auto dense2 = builder.FullyConnected(dense1, 10, "softmax");

// 将计算图构建成 TensorFlow 会话
Session session(Env::Default(), GraphDef(builder.Build()));

// 从 CIFAR-10 数据集加载数据
auto dataset = Dataset::FromTensorSlices(data).Batch(16);

// 创建 TensorProtos 以保存图像和标签数据
Tensor image_tensor(DataType::DT_FLOAT, TensorShape({16, 32, 32, 3}));
Tensor label_tensor(DataType::DT_INT32, TensorShape({16}));

// 训练模型
for (int i = 0; i < num_epochs; i++) {
  dataset->GetNext(&image_tensor, &label_tensor);
  session.Run({{{"input", image_tensor}, {"label", label_tensor}}}, nullptr);
}

Tensor accuracy_tensor(DataType::DT_FLOAT, TensorShape({}));
session.Run({}, {{"accuracy", &accuracy_tensor}});
cout << "Model accuracy: " << accuracy_tensor.scalar<float>() << endl;