# HarisIqbal88/PlotNeuralNet

Latex code for making neural networks diagrams

Repository: https://github.com/HarisIqbal88/PlotNeuralNet
Canonical: https://ross.abutalabs.com/products/plotneuralnet
Language: TeX
License: MIT
License Family: permissive
Topics: latex, deep-neural-networks
Last push: 2023-08-21T17:47:04+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2962, "days_push": 1108, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 24955, forks 3062 (observed 2026-08-28T04:11:37.731608+00:00)

## What it is
A LaTeX/TikZ-based library for drawing neural network architecture diagrams, with a Python interface for generating the TikZ code. It is commonly used to produce publication-quality figures for reports, papers, and presentations.

## Use cases
- draw neural network architecture diagrams for a research paper
- create CNN diagrams in LaTeX for a thesis
- generate publication-quality deep learning figures
- visualize network layers like conv and pooling for a presentation
- make TikZ neural network diagrams from Python
- diagram a U-Net or FCN architecture for a report

## When to choose
- you need polished, publication-ready neural network diagrams in LaTeX
- you want to describe architectures programmatically via Python
- you are writing academic reports or slides with TikZ figures

## When to avoid
- you need interactive or runtime visualization of actual trained models
- you want diagrams outside LaTeX/PDF workflows
- you need automatic diagram generation from model code like ONNX or PyTorch

## Facets
- artifact type: library
- maturity: maintenance
- function: data-visualization, graphics
- domain: deep-learning, machine-learning, data-visualization, documentation
- platform: windows, python, cli
- tags: latex, tikz, neural-network-diagrams, academic-writing, paper-figures, linux, macos

## Member repositories
- HarisIqbal88/PlotNeuralNet (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:37.731608+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:56:08.258512+00:00, confidence not recorded.
  - readme: https://github.com/HarisIqbal88/PlotNeuralNet (fetched 2026-08-28T04:11:37.731608+00:00, sha 74ccd268cc77)
- Data as of 2026-08-30T08:39:29.467469+00:00.