sicara/tf-explain

Health v2 · maintenance only

Adoption not part of the score

1037 stars · 110 forks observed · 2026-08-28

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

tf-explain is a Python library implementing interpretability methods (Grad CAM, occlusion sensitivity, vanilla gradients, etc.) for tf.keras models as TensorFlow 2.x callbacks. It lets you explain neural network predictions either on trained models or during training with results logged to TensorBoard.

Use cases

• explain why a keras image classifier made a prediction
• visualize grad cam heatmaps for a tensorflow model
• interpret neural network predictions during training
• apply occlusion sensitivity to a tf.keras model
• understand which image regions activate a CNN
• log interpretability visualizations to tensorboard

When to choose

• you use TensorFlow 2.x / Keras and need explainability methods like Grad CAM or occlusion sensitivity
• you want interpretability visualizations both post-training and as training callbacks
• you prefer a lightweight pip-installable library over building explainability code yourself

When to avoid

• you work with PyTorch or other frameworks - it is TensorFlow 2.x only
• you need explainability for NLP or tabular models - methods are image-focused
• you need actively developed features - the project is in maintenance mode with infrequent releases

Facets

library · maturity maintenance

machine-learning data-visualization deep-learning deep-learning machine-learning computer-vision data-visualization python cross-platform interpretability explainable-ai tensorflow keras grad-cam neural-network-visualization callbacks gpu

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