From the course: Deep Learning with Python: Optimizing Deep Learning Models

Early stopping and checkpointing - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Early stopping and checkpointing

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- [Instructor] Early stopping is a regularization technique designed to optimize the training process by halting it when a model's performance on a validation dataset stops improving. This technique operates by continuously evaluating the model's performance on the validation metric, such as validation loss or accuracy at the end of each epoch. Early stopping monitors these metrics and halts training process if no improvement is observed for a specified number of consecutive epochs defined by the patience parameter. One of the key advantages of early stopping is its ability to prevent overfitting by ceasing training before the model begins to lose its ability to generalize the unseen data. Additionally, it conserves computational resources, saving time and processing power by stopping unnecessary training. Early stopping also minimizes the need for manual intervention, as it automates the decision of when to end training. Early stopping is not without its challenges. For one, early stopping is sensitive to noise in validation metrics, which can lead to premature stopping. Furthermore, the effectiveness of early stopping relies heavily on setting an appropriate patience parameter, which may require experimentation to optimize for a specific task or dataset. Despite these limitations, early stopping remains a valuable tool for improving model performance and training efficiency. Another approach to improving model performance is checkpointing. Checkpointing involves saving the model's parameters periodically during training, typically, when performance on the validation set improves. This ensures that the best version of the model with the lowest validation loss or highest validation accuracy is preserved. The process is straightforward. At the end of each epoch, the performance of the current version of the model is compared to that of the best one saved so far. If the validation metric shows improvement, the new version is saved to disc, replacing the previously saved version. This ensures that the best version of the model is always recoverable, regardless of how training progresses or if unforeseen interruptions occur. Checkpointing offers several advantages. First, it guarantees that the best version of the model is preserved, even if continued training leads to overfitting. Second, it provides fault tolerance, acting as a safety net in the event of system crashes or power failures. Third, it allows flexibility, enabling you to experiment with different stopping criteria, without risking the loss of the best performing model. Checkpointing though does have some limitations. Saving model parameters requires additional storage, which can become a concern for large models. Moreover, frequent checkpointing such as saving after every epoch can introduce delays due to increased input output operations. Despite these limitations, checkpointing is a valuable tool for preserving model quality and ensuring training resilience.

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