Introduction to Neural Structured Learning in TensorFlow
Neural Structured Learning (NSL) is an innovative framework in the realm of artificial intelligence that enhances the training of neural networks by utilizing structured signals, in addition to regular feature inputs. This framework, part of TensorFlow, leverages the relationships and similarities among data samples, which can be explicit, like a graph structure, or implicit, such as those produced by adversarial interference.
Key Features
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Structured Signals: These are employed to reveal relationships or similarities between samples, which might be unlabeled. By integrating these signals during training, NSL boosts neural networks' performance. This is particularly beneficial when there is a scarcity of labeled data, enhancing the model's accuracy and robustness against adversarial attacks, which aim to confuse model predictions.
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Versatility: The NSL framework caters to various neural network architectures, whether they are feed-forward, convolutional, or recurrent models. It's applicable in different learning scenarios, including supervised, semi-supervised, and potentially unsupervised learning.
Tools and APIs
NSL includes several developer-friendly tools to facilitate the integration of structured signals into model training:
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Keras APIs: Make it straightforward to include graphs and adversarial perturbations during training.
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TensorFlow Operations and Functions: Provide foundational capabilities for incorporating structure when using lower-level TensorFlow APIs.
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Graph Tools: Assist in building and using graphs as training inputs.
Implementation and Getting Started
To start using NSL, it can be easily installed via pip:
pip install neural-structured-learning
This package requires TensorFlow 1.15 or higher and is compatible with TensorFlow 2.x versions, excluding 2.1 due to an incompatibility issue.
Learning and Tutorials
NSL offers a rich set of resources to help users quickly learn and implement its capabilities. A series of YouTube videos provide comprehensive overviews, while interactive Colab tutorials offer hands-on practice. Tutorials cover various aspects such as:
- Training with natural graphs
- Training with synthesized graphs
- Adversarial learning techniques
These resources, along with examples, can be found on the NSL GitHub page under the examples directory.
Contributions and Research
NSL thrives on community contributions, welcoming new case studies, product improvements, and algorithm innovations. Developers can participate in various ways, such as refining product excellence, creating tutorials, or developing new learning algorithms.
The research directory of NSL highlights ongoing projects like hyperbolic knowledge graph embeddings and graph agreement models, fostering a collaborative environment for innovation.
Community Engagement
For issues, questions, or feedback, users are encouraged to engage via GitHub, Stack Overflow, or dedicated feedback forms. The community is integral to NSL's evolution and effectiveness.
Summary
Neural Structured Learning in TensorFlow is a versatile, robust framework enhancing neural network training by leveraging structured signals. With user-friendly APIs, extensive documentation, and a supportive community, NSL stands as a formidable tool for developers looking to improve model accuracy and resilience against adversarial challenges.