Showing posts from November, 2016

TensorLight: A high-level framework for TensorFlow projects

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