latest

User Guide

Installation
Logic Explained Network (LENs) tutorial
- Entropy-based LENs
- \(\psi\) LENs
Concept Embeddings tutorial
- Limits of Concept Bottleneck Models
- Concept Embeddings
Deep Concept Reasoning tutorial
- Limits of Concept Embeddings
- Deep Concept Reasoning
Contributing to Pytorch Explain
- How Can I Contribute?
- Coding Style
Running tests

API Reference

Logic explanations for entropy-based LENs
- explain_class()
- explain_classes()
Logic explanations for \(\psi\) LENs
- explain_class()
Utils for extracting logic explanations
Logic explanation metrics
Utils
- get_predictions()
- replace_names()
Logic layers
- EntropyLinear
  - EntropyLinear.extra_repr()
  - EntropyLinear.forward()
Loss functions to regularize the neural model
- entropy_logic_loss()
- l1_loss()
Prune functions to simplify the neural model
- prune_equal_fanin()
APIs for concepts
- ConceptEmbedding
  - ConceptEmbedding.forward()
- ConceptReasoningLayer
  - ConceptReasoningLayer.forward()
Logic T-Norms

Copyright

Authors
Apache License
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

pytorch_explain

Utils for extracting logic explanations
Edit on GitHub

Utils for extracting logic explanations

torch_explain.logic.nn.utils

Previous Next

© Copyright 2023, Pietro Barbiero. Revision 4986e0b7.

Built with Sphinx using a theme provided by Read the Docs.

Read the Docs v: latest

Versions: latest; stable

Downloads

On Read the Docs: Project Home; Builds