Machine learning with tensorflow for mobile and ubiquitous interaction
Huy Viet Le, Sven Mayer, Niels Henze · 2017
Due to the increasing amount of sensors integrated into the environment and worn by the user, a sheer amount of context-sensitive data become available. While interpreting them with traditional methods (e.g., formulas and simple heuristics) is challenging, the latest machine learning techniques require only a set of labeled data. TensorFlow is an open-source library for machine learning which implements a wide range of neural network models. With TensorFlow Mobile, researchers and developers can further deploy the trained models on low-end mobile devices for ubiquitous scenarios. This facilitates the model export and offers techniques to optimize the model for a mobile deployment. In this tutorial, we teach attendees two basic steps to a deployment of neural networks on smartphones: Firstly, we will teach how to develop neural network architectures and train them in TensorFlow. Secondly, we show the process to run the trained models on a mobile phone using TensorFlow Mobile.