DeepVisual: a visual programming tool for deep learning systems

As deep learning (DL) opens the way to many technological innovations in a wild range of fields, more and more researchers and developers from diverse domains start to take advantage of DLs. In many circumstances, a developer leverages a DL framework and programs the training software in the form of source code (e.g., Python, Java). However, not all of the developers across domains are skilled at programming. It is highly desirable to provide a way so that a developer could focus on how to design and optimize their DL systems instead of spending too much time on programming. To simplify the programming process towards saving time and effort especially for beginners, we propose and implement DeepVisual, a visual programming tool for the design and development of DL systems. DeepVisual represents each layer of a neural network as a component. A user can drag-and-drop components to design and build a DL model, after which the training code is automatically generated. Moreover, DeepVisual supports to extract the neural network architecture on the given source code as input. We implement DeepVisual as a PyCharm plugin and demonstrate its usefulness on two typical use cases.