Use Callbacks

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Callbacks can help improve your training process, monitor metrics, and avoid overfitting.
Common callbacks include:
- EarlyStopping: Stop training when a monitored metric has stopped improving.
- ModelCheckpoint: Save the best model during training.
- ReduceLROnPlateau: Reduce the learning rate when a metric has stopped improving.
pythonCopy codefrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau callbacks = [ EarlyStopping(monitor='val_loss', patience=3), ModelCheckpoint('best_model.h5', save_best_only=True), ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=2) ] model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, callbacks=callb

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