Dialog state tracking is to accurately infer a compact representation of the dialog status up to the current turn, it needs to summarize all the dialog history information and user's goals. In a successful spoken dialog system, dialog state tracker is one of the most important components of the pipelines. Yet until recently, there are no general, flexible, accurate and truly end to end dialog state tracking models. In this paper, we propose a novel model named state tracking networks that can perform dialog state tracking in a natural efficient and elegant way. It uses an explicit gate to model the state updating mechanism and can be trained end to end in a deterministic manner using standard backpropagation techniques or stochastically by reinforcement learning. Our model can both deal with ASR and text input without any modification. We perform experiments on the Second Dialog State Tracking Challenge dataset(DSTC2) and get performance matching the state-of-the-art models. Furthermore, the qualitative analysis reveals that the gating mechanism learned by our model agree well with intuition.