save_best_only: if save_best_only=True, it only saves when the model is considered the "best" and the latest best model according to the quantity monitored will not be overwritten. If filepath doesn't contain formatting options like {epoch} then filepath will be overwritten by each new better model.
restore_best_weights: Whether to restore model weights from the epoch with the best value of the monitored quantity. If False, the model weights obtained at the last step of training are used. An epoch will be restored regardless of the performance relative to the baseline. If no epoch improves on baseline, training will run for patience epochs and restore weights from the best epoch in that set.
If I train my model and save the best model and restore the weights of the best epoch... - am I not doing the same thing twice? Would it not just produce two model files, one for the epoch and one for the final model but both actually being the same?
Then if this is correct which would be the preferred method to use? (As I understand, models are sometimes held in memory EarlyStopping for but not sure about model_checkpoint ModelCheckpoint)
The former saves the weights of the model at the epoch where it performed the best on the validation set, while the latter restores those saved weights into the model and use it for predictions.
When you save the weights of a model using the ModelCheckpoint callback during training, the weights are saved to disk (e.g., to a .h5 file) at specified checkpoints (e.g., after every epoch). The purpose of saving the weights is to be able to restore them later for predictions, in case you need to stop the training for some reason, or if you want to use the weights for inference on a different dataset.
Once the training is complete, you can restore the weights of the best performing model by loading them back into the model architecture, and then use the model for predictions.
The difference between early stopping and saving the weights using ModelCheckpoint is that early stopping saves the weights automatically based on a criterion (the performance on the validation set), while ModelCheckpoint saves the weights at specified intervals (e.g., after every epoch).
So, in the case of early stopping, you don't have to specify when to save the weights, because the algorithm stops training automatically and saves the weights when the performance on the validation set stops improving. On the other hand, with ModelCheckpoint, you have more control over when to save the weights, but you have to manually stop the training when the performance is no longer improving.
In summary, saving the weights during training allows you to persist the state of the model, so that you can continue training or use the model for predictions later.
In terms of preferred method, it depends on your use case. If you have limited memory, you may only keep the best model's weights in memory, and use the ModelCheckpoint to periodically save the best weights to disk. If memory is not a concern, you could keep all intermediate models in memory and use the EarlyStopping to stop training once the performance on the validation set stops improving.