How should dropout parameters typically be set?

When using dropout, several factors can typically be considered to set the parameters correctly:

1. Network complexity: The higher the complexity of the network, the dropout parameters can be set slightly larger to reduce the risk of overfitting.
2. Dataset size: If the dataset is small, you can decrease the dropout parameter appropriately to fully utilize the limited training samples.
3. Overfitting and underfitting situations: If the model is overfitting, you can increase the dropout parameter to reduce the dependency between neurons; if the model is underfitting, you can decrease the dropout parameter.
4. Other regularization methods: If other regularization methods such as L1 or L2 regularization are used in the model, it may be appropriate to decrease the dropout parameter.

Generally speaking, the dropout parameter can be set between 0.2 and 0.5. In practice, the best dropout parameter can typically be selected through cross-validation.