DevDiary

Since it has proven stability and reliability in two successful Windows 10 project for more than a year, we thought about to open source our service-driven framework. Even when it has not reached version 1.0 yet, you can nevertheless use it for your next project right now. Check out the UWPCore Framework on Github . I developed this framework in course of the last year together with my friend Patrick Mutter. More information about the framework is written down on the landing page of the repository. It even includes a short description of how to get started. In case you use our framework, and consider any kind of problem or bug, feel free to either open an issue on Github, or via a pull request.

In the course of the development of my Master's Thesis "Deep Learning Approaches to Predict Future Frames in Videos" at TUM, I realized that the high flexibility of TensorFlow has its price: boilerpate code. Many things that are needed in almost every neural network training or evaluation script have to be implemented over and over again. To that end, I started to implement a high-level API for Google's machine intelligence library, called TensorLight . TensorLight comes with four guiding principles: Simplicity:  Straight-forward to use for anybody who has already worked with TensorFlow. Especially, no further learning is required regarding how to define a model's graph definition. Compactness:  Reduce boilerplate code, while keeping the transparency and flexibility of TensorFlow. Standardization:  Provide a standard way in respect to the implementation of models and datasets in order to save time. Further, it automates the whole training and validatio...