  Next-generation 21cm observations will enable imaging of reionization on very
large scales. These images will contain more astrophysical and cosmological
information than the power spectrum, and hence providing an alternative way to
constrain the contribution of different reionizing sources populations to
cosmic reionization. Using Convolutional Neural Networks, we present a simple
network architecture that is sufficient to discriminate between
Galaxy-dominated versus AGN-dominated models, even in the presence of simulated
noise from different experiments such as the HERA and SKA.
