Multi-layer networks are networks on a set of entities (nodes) with multiple types of relations (edges) among them where each type of relation/interaction is represented as a network layer. As with single layer networks, community detection is an important task in multi-layer networks. A large group of popular community detection methods in networks are based on optimizing a quality function known as the modularity score, which is a measure of presence of modules or communities in networks. Hence a first step in community detection is defining a suitable modularity score that is appropriate for the network in question. Here we introduce several multi-layer network modularity measures under different null models of the network, motivated by empirical observations in networks from a diverse field of applications. In particular we define the multi-layer configuration model, the multi-layer expected degree model and their various modifications as null models for multi-layer networks to derive different modularities. The proposed modularities are grouped into two categories. The first category, which is based on degree corrected multi-layer stochastic block model, has the multi-layer expected degree model as their null model. The second category, which is based on multi-layer extensions of Newman-Girvan modularity, has the multi-layer configuration model as their null model. These measures are then optimized to detect the optimal community assignment of nodes. We compare the effectiveness of the measures in community detection in simulated networks and then apply them to four real networks.