How to choose the appropriate loss function in PyTorch?

Choosing the right loss function in PyTorch depends on your task type and model architecture. Here are some common loss functions and their applicable scenarios:

1. CrossEntropyLoss is a loss function that is suitable for multi-class classification problems, especially when the labels are discrete.
2. Mean squared error loss function (MSELoss): suitable for regression problems, i.e. predicting continuous values.
3. BCEWithLogitsLoss is suitable for binary classification problems and combines the Sigmoid activation function with binary cross-entropy loss.
4. NLLLoss: Suitable for multi-classification problems, it combines the LogSoftmax activation function and negative log likelihood loss function.

In addition to the common loss functions mentioned above, PyTorch also offers many other types of loss functions. You can choose the appropriate loss function based on the specific task requirements. Alternatively, you can also customize a loss function to meet specific needs.