neural-structured-learning
Neural Structured Learning (NSL) in TensorFlow enhances neural network accuracy by using structured signals in training, benefiting particularly from limited labeled data. It provides flexible Keras APIs and TensorFlow operations for integrating graphs and adversarial perturbations. NSL is compatible with various network types like feed-forward, convolutional, and recurrent, and supports supervised and semi-supervised learning. It is easy to install via pip and works with TensorFlow 1.15+, excluding version 2.1. Explore available tutorials and research for more effective implementation.
