Prior work on deep learning models-based mobile applications in a cloud-edge computing environment focuses on performing lightweight data pre-processing tasks on edge servers for cloud-hosted cognitive servers. Those approaches have two major drawbacks. First, it is uneasy for the mobile applications to assure satisfactory user experience in terms of network communication delay, because the intermediary edge servers are used only to pre-process data (e.g., images and videos) and the cloud servers must be used to complete the required data processing tasks. Second, those approaches assume the pre-trained deep learning models deployed on cloud servers do not change, and do not attempt to automatically upgrade in a context-aware manner via the collected data. In this paper, we propose a cloud-edge collaboration framework that facilitates delivering mobile applications with long duration, fast response, and high accuracy properties. In the proposed framework, the edge server can provide cognitive service with long duration and fast response properties via a shallow convolutional neural network model, called EdgeCNN. Our experimental results show that EdgeCNN can reduce the average response time of cognitive services and improve accuracy.