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README.md

Neural Structured Learning in TensorFlow

Neural Structured Learning (NSL) is a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph [1,2,5] or implicit as induced by adversarial perturbation [3,4].

Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification.

NSL generalizes to Neural Graph Learning [1] as well as to Adversarial Learning [3]. The NSL framework in TensorFlow provides the following easy-to-use APIs and tools for developers to train models with structured signals:

  • Keras APIs to enable training with graphs (explicit structure) and adversarial pertubations (implicit structure).

  • TF ops and functions to enable training with structure when using lower-level TensorFlow APIs

  • Tools to build graphs and construct graph inputs for training

The NSL framework is designed to be flexible and can be used to train any kind of neural network. For example, feed-forward, convolution, and recurrent neural networks can all be trained using the NSL framework. In addition to supervised and semi-supervised learning (a low amount of supervision), NSL can in theory be generalized to unsupervised learning. Incorporating structured signals is done only during training, so the performance of the serving/inference workflow remains unchanged. Please check out our tutorials for a practical introduction to NSL.

Getting started

You can install the prebuilt NSL pip package by running:

pip install neural-structured-learning

For more detailed instructions on how to install NSL as a package or to build it from source in various environments, please see the installation guide

Note that NSL requires a TensorFlow version of 1.15 or higher.

Contributing to NSL

Contributions are welcome and highly appreciated - there are several ways to contribute to TF Neural Structured Learning:

  • Case studies. If you are interested in applying NSL, consider wrapping up your usage as a tutorial, a new dataset, or an example model that others could use for experiments and/or development.

  • Product excellence. If you are interested in improving NSL's product excellence and developer experience, the best way is to clone this repo, make changes directly on the implementation in your local repo, and then send us pull request to integrate your changes.

  • New algorithms. If you are interested in developing new algorithms for NSL, the best way is to study the implementations of NSL libraries, and to think of extensions to the existing implementation (or alternative approaches). If you have a proposal for a new algorithm, we recommend starting by staging your project in the research directory and including a colab notebook to showcase the new features.

    If you develop new algorithms in your own repository, we are happy to feature pointers to academic publications and/or repositories that use NSL, on tensorflow.org/neural_structured_learning.

Please be sure to review the contribution guidelines.

Issues and Questions

For issues, please use GitHub issues for tracking requests and bugs. For questions, please direct them to Stack Overflow with the "nsl" tag.

Release Notes

Please see the release notes for detailed version updates.

References

[1] T. Bui, S. Ravi and V. Ramavajjala. "Neural Graph Learning: Training Neural Networks Using Graphs." WSDM 2018

[2] T. Kipf and M. Welling. "Semi-supervised classification with graph convolutional networks." ICLR 2017

[3] I. Goodfellow, J. Shlens and C. Szegedy. "Explaining and harnessing adversarial examples." ICLR 2015

[4] T. Miyato, S. Maeda, M. Koyama and S. Ishii. "Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning." ICLR 2016

[5] D. Juan, C. Lu, Z. Li, F. Peng, A. Timofeev, Y. Chen, Y. Gao, T. Duerig, A. Tomkins and S. Ravi "Graph-RISE: Graph-Regularized Image Semantic Embedding." arXiv 2019

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