Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have shown to achieve superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, the existing methods for network embedding are unable to generate representation vectors for unseen vertices; besides, these methods only utilize topological information from the network ignoring a rich set of nodal attributes, which is abundant in all real-life networks. In this paper, we present a novel network embedding approach called Neural-Brane, which overcomes both of the above limitations. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Additionally, Neural-Brane is an inductive embedding approach, which enables generating embedding vectors for unseen future vertices of the attributed network. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification task on four real-world graph datasets. Experimental results demonstrate the superiority of Neural-Brane over the state-of-the-art existing methods.