How can I handle errors in C++ for machine learning applications?

How can I handle errors in C++ for machine learning applications?

In C++, you can handle errors using exceptions. You can define try-catch blocks to catch exceptions that may occur during execution.

Can you give me an example of using try-catch in a machine learning context?

Sure! For instance, if you're loading a model, you can wrap the loading code in a try block and catch any exceptions related to file I/O.

What types of exceptions should I be aware of?

Common exceptions include std::runtime_error for general errors, std::invalid_argument for invalid inputs, and std::out_of_range for accessing elements outside of a container.

Is there a way to log these errors for debugging?

Yes, you can use logging libraries like spdlog or Boost.Log to log errors, which can help you debug issues in your machine learning application.