I've got a question regarding early stopping and I was hoping to get some insights here!
When would be best to stop training for a model that converges after a few epochs. It would make sense to stop training after training loss drops below the validation loss, but even after this point, validation loss keeps on decreasing, so would it be safe to assume the model is improving? or should the training be stopped when training loss drops below the validation loss?
I've attached two examples below, where in your opinion would be the best epoch to stop training? Also, as in the second example, the model converges quickly, does that mean the learning rate should be decreased even further (it's currently set as 0.0001, with an Adam optimizer).