  The competition "Predicting Generalization in Deep Learning (PGDL)" aims to
provide a platform for rigorous study of generalization of deep learning models
and offer insight into the progress of understanding and explaining these
models. This report presents the solution that was submitted by the user
\emph{smeznar} which achieved the eight place in the competition. In the
proposed approach, we create simple metrics and find their best combination
with automatic testing on the provided dataset, exploring how combinations of
various properties of the input neural network architectures can be used for
the prediction of their generalization.
