ML0042 Early Stopping

Early Stopping is a regularization technique used to halt training when a model’s performance on a validation set stops improving, thus avoiding overfitting. It monitors metrics like validation loss or validation accuracy and stops after a defined number of stagnant epochs (patience). This ensures efficient training and better generalization.

Implementation:
Split data into training and validation sets.
After each epoch, evaluate on the validation set.
If performance improves, save the model and reset the patience counter.
If no improvement, counter add one; if the counter reaches the patience epochs, stop training.
Restore best weights after stopping, load the model weights from the epoch that yielded the best validation performance.

Below is one example loss plot when using early stop.