How do I utilize Keras Callbacks to implement early stopping during model training

Question

With the help of python programming can you tell me How do I utilize Keras Callbacks to implement early stopping during model training?

Answer 1

Use Keras EarlyStopping callback to monitor validation loss (val_loss) or accuracy (val_accuracy), stopping training when no improvement is observed for a set number of epochs (patience), and optionally restoring the best model weights.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Stops Training to Prevent Overfitting:

  • Monitors validation loss (val_loss) or accuracy (val_accuracy) and stops training when performance plateaus.

• patience=3 Ensures Stability:

  • Waits for 3 consecutive epochs before stopping to avoid stopping too early due to minor fluctuations.

• restore_best_weights=True Ensures Best Model is Used:

  • Restores the model’s weights from the epoch where it performed best, preventing degraded performance.

• Speeds Up Training by Eliminating Redundant Epochs:

  • Saves computation time by stopping training when further learning gains are negligible.

Hence, using Keras EarlyStopping callback prevents overfitting, speeds up training, and ensures the best model weights are used for optimal generalization.