Initial checkin

PiperOrigin-RevId: 266043349

Author: Neural-Link Team

.bazelrc

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+# Neural Structured Learning Bazel configuration.
+#
+# See https://docs.bazel.build/versions/master/user-manual.html#config for
+# details on the various configuration options.
+
+# Build with optimization enabled.
+build --compilation_mode=opt
+
+# Processor native optimizations (depends on build host capabilities).
+build --copt=-march=native
+build --host_copt=-march=native
+build --copt=-O3

BUILD

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+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])

CONTRIBUTING.md

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+# How to Contribute
+
+We'd love to accept your patches and contributions to this project. There are
+just a few small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement. You (or your employer) retain the copyright to your contribution;
+this simply gives us permission to use and redistribute your contributions as
+part of the project. Head over to [Google's Contributor License Agreement]
+(https://cla.developers.google.com/) to see your current agreements on file or
+to sign a new one.
+
+You generally need to submit a CLA only once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.
+
+## Community Guidelines
+
+This project follows [Google's Open Source Community
+Guidelines](https://opensource.google.com/conduct/).

LICENSE

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+
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README.md

@@ -1 +1,103 @@
+# Neural Structured Learning in TensorFlow
+
+[TOC]
+
+## Overview
+
+![](g3doc/images/nsl_overview.png){width="750" style="display:block;margin:auto"}
+
+**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. Therefore, 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 adding 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 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 (low amount of supervision), NSL can also be
+generalized to unsupervised learning. Furthermore, incorporating structure
+is done only during training; there is no change to the serving/inference
+workflow. As a result, no additional cost (latency, memory consumption, etc)
+because of neural structured learning is incurred during serving. Please visit
+our tutorials for a practical introduction to NSL.
+
+## Getting started
+
+Install the prebuilt pip package using
+
+```bash
+pip install neural-structured-learning
+```
+
+## 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 repos using NSL on
+    [tensorflow.org/nsl](http://www.tensorflow.org/nsl).
+
+Please be sure to review the
+[contribution guidelines](CONTRIBUTING.md#code-style-guidelines-and-best-practices)
+for guidelines on the coding style, best practices, etc.
+
+## Issues and Questions
+
+For issues, please use [GitHub issues](https://github.com/tensorflow/nsl/issues)
+for tracking requests and bugs. For questions, please direct them to [Stack Overflow](https://stackoverflow.com) with the
+["nsl"](https://stackoverflow.com/questions/tagged/nsl)
+tag.
+
+## Reference
+
+[[1] T. Bui, S. Ravi and V. Ramavajjala. "Neural Graph Learning: Training Neural Networks Using Graphs." WSDM 2018](https://ai.google/research/pubs/pub46568.pdf)
+
+
+[[2] T. Kipf and M. Welling. "Semi-supervised classification with graph convolutional networks." ICLR 2017](https://arxiv.org/pdf/1609.02907.pdf)
+
+
+[[3] I. Goodfellow, J. Shlens and C. Szegedy. "Explaining and harnessing adversarial examples." ICLR 2015](https://arxiv.org/pdf/1412.6572.pdf)
+
+
+[[4] T. Miyato, S. Maeda, M. Koyama and S. Ishii. "Virtual Adversarial Training: a Regularization Method for Supervised and Semi-supervised Learning." ICLR 2016](https://arxiv.org/pdf/1704.03976.pdf)
+
+[[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](https://arxiv.org/abs/1902.10814)
+
+
 

WORKSPACE

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+workspace(name = "org_tensorflow_neural_structured_learning")

g3doc/_book.yaml

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+upper_tabs:
+# Tabs left of dropdown menu
+- include: /_upper_tabs_left.yaml
+- include: /api_docs/_upper_tabs_api.yaml
+# Dropdown menu
+- name: Resources
+  path: /resources
+  is_default: true
+  menu:
+  - include: /resources/_menu_toc.yaml
+  lower_tabs:
+    # Subsite tabs
+    other:
+    - name: Guide & Tutorials
+      contents:
+      - title: Framework
+        path: /neural_structured_learning/tutorials/framework
+      - title: Install
+        path: /neural_structured_learning/install
+      - heading: Neural graph learning tutorials
+      - title: Graph regularization for document classification using natural graphs
+        path: /neural_structured_learning/tutorials/graph_keras_mlp_cora
+      - title: Graph regularization for sentiment classification using synthesized graphs
+        path: /neural_structured_learning/tutorials/graph_keras_lstm_imdb
+      - heading: Adversarial learning tutorials
+      - title: Adversarial regularization for image classification
+        path: /neural_structured_learning/tutorials/adversarial_keras_cnn_mnist
+    - name: API
+      skip_translation: true
+      contents:
+      - title: All symbols
+        path: /neural_structured_learning/api_docs/python/
+      - include: /neural_structured_learning/api_docs/python/_toc.yaml
+
+- include: /_upper_tabs_right.yaml