Keras Hyper tuning - Final model state
The high value for epoch will only lead into high accuracy and lowest loss for training dataset, but the important thing that you should watch during training on data is the val_loss and val_metric;
In most cases if the model continue training on the data will cause overfitting on the validation data (the validation data are not seen by the model and just evaluatted by model after an epoch) so the high value for epochs wont lead into better model
so the most important thing to notice is the val_loss and discontinue or break model training if you notice continuos increasing in val_loss; so you could implement a callback (EarlyStopping) to stop model training whenever the increasing in val_loss is watched.
I am new to Deep learning and would like to understand on the below points. Can you please help.

If I give number of epochs as 100 to train and try to evaluate the model, does it take the best epoch model or the final model after 100 epochs.

In history, I am seeing loss and val loss. Do the model try to minimize only the training loss and just show the val_loss for our reference, similar to the metrics it shows.

If I use Keras Tuner (RandomSearch), there is an objective function. I am confused whether the model try to reduce the loss provided during compile or the loss provided in the tuner objective.


Can you please clarify on the above points.