epoch-dependent dropout
时间: 2023-09-19 14:10:43 浏览: 49
Epoch-dependent dropout is a type of dropout regularization technique used in deep learning. It is a modification of the standard dropout technique, where the probability of keeping a unit during training is varied based on the current epoch number. The idea behind epoch-dependent dropout is that as the training progresses, the network becomes more robust and overfitting becomes less likely. Therefore, the dropout rate can be gradually reduced as the training epochs increase, allowing the network to learn more complex representations.
In epoch-dependent dropout, the dropout rate is typically set to a high value at the beginning of training and gradually decreased over time. This can be done in a linear or exponential manner, depending on the specific implementation. By reducing the dropout rate over time, epoch-dependent dropout allows the network to gradually learn more complex representations while still preventing overfitting.
Overall, epoch-dependent dropout is a useful technique for preventing overfitting in deep learning models, and it can be particularly effective when used in conjunction with other regularization techniques such as weight decay and early stopping.