  Understanding the generalization behaviour of deep neural networks is a topic
of recent interest that has driven the production of many studies, notably the
development and evaluation of generalization "explainability" measures that
quantify model generalization ability. Generalization measures have also proven
useful in the development of powerful layer-wise model tuning and optimization
algorithms, though these algorithms require specific kinds of generalization
measures which can probe individual layers. The purpose of this paper is to
explore the neglected subtopic of probeable generalization measures; to
establish firm ground for further investigations, and to inspire and guide the
development of novel model tuning and optimization algorithms. We evaluate and
compare measures, demonstrating effectiveness and robustness across model
variations, dataset complexities, training hyperparameters, and training
stages. We also introduce a new dataset of trained models and performance
metrics, GenProb, for testing generalization measures, model tuning algorithms
and optimization algorithms.
