Systematic Analysis And Automated Search Of Hyper-Parameters For Cell Classifier Training

Performance and robustness of neural networks depend on a suitable choice of hyper-parameters, which is important in research as well as for the final deployment of deep learning algorithms. While a manual systematical analysis can be too time consuming, a fully automatic search is very dependent on the kind of hyper-parameters. For a cell classification network, we assess the individual effects of a large number of hyper-parameters and compare the resulting choice of hyperparameters with state of the art search techniques. We further propose an approach for automated, successive search space reduction that yields well performing sets of hyperparameters in a time-efficient way.