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Systematic Analysis and Automated Search of Hyper-parameters for Cell Classifier Training
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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 hyper-parameters with state of the art search techniques. We further propose an approach for automated, successive search space reduction that yields well performing sets of hyper-parameters in a time-efficient way.
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 hyper-parameters with state of the art search techniques. We further propose an approach for automated, successive search space reduction that yields well performing sets of hyper-parameters in a time-efficient way.
