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harisiqbal88/plotneuralnet

★ 24,752TeXAudience · researcherComplexity · 2/5DormantLicenseSetup · moderate

TLDR

Create publication-quality neural network architecture diagrams by writing Python code instead of drawing manually. Outputs polished LaTeX graphics for research papers.

Mindmap

mindmap
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    What it does
      Generates network diagrams
      Converts code to visuals
      Academic-ready output
    How it works
      Python describes layers
      Converts to LaTeX TikZ
      Renders polished graphics
    Use cases
      Research papers
      Thesis illustrations
      Conference presentations
    Tech stack
      Python
      LaTeX
      TikZ graphics
    Key features
      Reproducible diagrams
      Easy to update
      Professional styling

Things people build with this

USE CASE 1

Generate clean neural network architecture diagrams for machine learning research papers and theses.

USE CASE 2

Create reproducible network visualizations that update automatically when you modify the architecture description.

USE CASE 3

Produce publication-ready diagrams with consistent academic styling for conference submissions and presentations.

Tech stack

PythonLaTeXTikZTeX

Getting it running

Difficulty · moderate Time to first run · 30min

Requires LaTeX/TikZ installation on system to render diagrams; Python dependencies are straightforward.

Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

PlotNeuralNet is a tool for creating publication-quality diagrams of neural network architectures, designed for use in research papers and presentations. Instead of drawing these diagrams manually in a graphics program, you describe the network structure in code and the tool generates the visual output automatically. It works by combining Python and LaTeX (a document preparation system widely used in academia). You write a short Python script that describes your network's layers, convolutional layers, pooling layers, softmax layers, and the connections between them, and the tool converts that into TikZ code (a LaTeX graphics library) and renders a polished architectural diagram. This means your diagram is reproducible, easy to update when the architecture changes, and matches the style expected in academic publications. You would use PlotNeuralNet if you are writing a machine learning paper or thesis and need a clean, professional diagram showing how your neural network is structured. It runs on Ubuntu and Windows, requires a LaTeX installation (such as texlive or MikTeX), and is written primarily in TeX with a Python interface.

Copy-paste prompts

Prompt 1
Show me how to write a PlotNeuralNet Python script that describes a simple convolutional neural network with 3 conv layers and 2 fully connected layers.
Prompt 2
I have a neural network architecture diagram in PlotNeuralNet. How do I modify the Python code to add a new pooling layer between the second and third convolutional layers?
Prompt 3
Help me set up PlotNeuralNet on my Ubuntu machine and generate my first network diagram from a Python script.
Prompt 4
Convert this neural network description into a PlotNeuralNet Python script: input layer, 2 conv layers, 1 pooling layer, 1 softmax output layer.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.