tensorflow/neural-structured-learning

Health v2 · maintenance only

Adoption not part of the score

1010 stars · 189 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Neural Structured Learning (NSL) is a TensorFlow framework for training neural networks with structured signals, either explicit graphs or implicit adversarial perturbations. It provides Keras and Estimator APIs plus tools for building graphs, improving accuracy with limited labels and robustness against adversarial attacks.

Use cases

• train neural networks with graph-structured data
• improve model accuracy when labeled data is scarce
• make models robust against adversarial attacks
• semi-supervised learning with unlabeled samples
• add adversarial perturbation regularization to Keras models
• build graphs from training data for neural graph learning

When to choose

• you already use TensorFlow/Keras and have relational or graph structure among samples
• you need adversarial training for model robustness
• you have limited labeled data plus unlabeled samples with similarity signals

When to avoid

• your stack is PyTorch or JAX
• you need the latest TensorFlow 2.x features or active development
• your data has no relational structure and you don't need adversarial robustness

Facets

library · maturity maintenance

machine-learning deep-learning llm-training machine-learning deep-learning artificial-intelligence security python cross-platform tensorflow keras graph-learning adversarial-learning regularization semi-supervised-learning neural-graph-learning

Member repositories

For agents

markdown · JSON · MCP: product_card(name="tensorflow/neural-structured-learning")