I am using TPE sampler from optuna to optimize hyperparameters for Deep Learning vision models. I was wondering if optuna adapt search depending of the number of trials.
If I train for 1000 trials and stop at 500, I can see that many parameters were not tried by the algorithm. If I reduce n_trials, does TPE explore faster (and less precisely) ? In other terms, is interupting optuna at 500 with n_step=1000 the same as using n_trials = 500 and waiting until the end.
I only have basic understanding of how TPE works.
Thanks.