As @ptrblck suggests, share a code example 
If I understand correctly, your problem is of multi-task learning 1. You can create separate sub-tasks (i.e. separate classifiers) for each target, aggregate the losses (sum/avg) from each classifier, and backpropagate it. The prediction error from each of the tasks will update the network weights.