Author: Zen and the Art of Computer Programming
1 Introduction
Generally speaking, deep learning models need more iterations to converge to a good enough state. That is to say, when the model training obtains a relatively high accuracy rate, the training will generally continue until the model completely converges or overfitting occurs. However, if the training process is inadvertently terminated, the model may enter a local optimum, eventually leading to underfitting and even poor generalization. In order to avoid this situation, <|im_sep|> early stopping method is a strategy to decide whether to continue training by detecting whether the performance on the validation set has improved, or to discard the previous model parameters and start training from scratch.
The key to early stopping is the measurement of performance on the validation set. There are two main ways to detect how well the validation set performs with early stopping:
1) Monitoring Metrics: In the early stopping method, the loss function or performance indicators on the verification set are usually used as indicators to judge the quality of the model. Such an approach enables fast and precise assessment of model performance. However, it should be noted that due to the difference in the quality of the validation set data, the performance metrics of different models or tasks may vary greatly. Therefore, it is very important to choose an appropriate performance index.
2) Tuning Hyperparameters: You can also select the optimal model by adjusting hyperparameters (such as learning rate, weight decay, etc.). Hyperparameter tuning itself is a complex process, but the cost of manual tuning can be greatly reduced through automated methods. For example, Bayesian optimization algorithms can automatically choose new hyperparameter values based on historical model performance.
In addition to metrics, early stopping can be used to control the training process in other ways. For example, limit the maximum training time, set stricter metric thresholds to terminate training, etc.
2. Explanation of basic concepts and terms
2.1 Definition
early stop method (