Leaving aside the training loss, the optimizer (ADAM), the number of U-NET blocks (tuned to a meaningful target receptive field for the problem) and the number of filters per layer (set to the maximum fitting in gpu memory for batch size = 1), what would be the most critical hyperparameters, and some sensible guidelines to tune them to achieve the best prediction accuracy without having to perform a multi-gpu extensive/expensive search for each problem?
Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Sign up to join this community
Anybody can ask a question
Anybody can answer
The best answers are voted up and rise to the top
