How can you implement a custom loss function in a machine learning model using TensorFlow or PyTorch?
- By extending the base loss class and defining a custom loss function using mathematical operations.
- By modifying the framework's source code to include the custom loss function.
- By stacking multiple pre-built loss functions together.
- By using only the built-in loss functions provided by the framework.
To implement a custom loss function, you extend the base loss class in TensorFlow or PyTorch and define your loss using mathematical operations. This allows you to tailor the loss function to your specific problem. Modifying the framework's source code is not recommended as it can lead to maintenance issues. Stacking pre-built loss functions is possible but does not create a truly custom loss.
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