tensorflow - What is the last state of the model after training?

Question

After fitting the model with model.fit(...), you can use .evaluate() or .predict() methods with the model.

The problem arises when I use Checkpoint during training.
(Let's say 30 checkpoints, with checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath, save_weights_only=True))

Then I can't quite figure out what do I have left, the last state of this model.

Is it the best one? or the latest one?

If the former is the case, one of 30 checkpoints should be same with the model I have left.
If the latter is the case, the latest checkpoint should be same with the model I have left.

Of course, I checked both the cases and neither one is right.

Answer 1

If you set save_best_only=True the checkpoint saves the model weights for the epoch that had the "best" performance. For example if your were monitoring 'val_loss' then it will save the model for the epoch with the lowest validation loss. If save_best_only=False then the model is saved at the end of each epoch regardless of the value of the metric being monitored. Of course if you do not use special formatting for the model save path then the save weights will be over written at the end of each epoch.