Deep Learning has made huge advancements in automation, but current neural networks are hard to understand. A new method trains deep hypernetworks to create explainable linear models that are just as accurate as black-box deep networks. These explainable models are easy to interpret and use the same resources as regular deep models. Experimental results show that they perform as well as the best classifiers on data and are just as interpretable as other explanation techniques.