  We train deep generative models on datasets of reflexive polytopes. This
enables us to compare how well the models have picked up on various global
properties of generated samples. Our datasets are complete in the sense that
every single example, up to changes of coordinate, is included in the dataset.
Using this property we also perform tests checking to what extent the models
are merely memorizing the data. We also train models on the same dataset
represented in two different ways, enabling us to measure which form is easiest
to learn from. We use these experiments to show that deep generative models can
learn to generate geometric objects with non-trivial global properties, and
that the models learn some underlying properties of the objects rather than
simply memorizing the data.
