PlotNeuralNet
LaTeX code and Python interface for creating publication-quality neural network architecture diagrams.
What is PlotNeuralNet?
PlotNeuralNet is a LaTeX-based tool for creating neural network architecture diagrams. It provides TikZ code and a Python interface to programmatically generate clean, publication-ready visualizations of deep learning models. The project solves the problem of manually drawing complex network architectures for academic papers and presentations.
Researchers, academics, and students writing papers about neural networks who need professional diagrams. Also useful for developers creating documentation for deep learning frameworks.
It produces vector-based diagrams that scale perfectly for publications, offers programmatic generation through Python, and ensures consistent styling across all network visualizations—saving time compared to manual drawing tools.
Overview
Latex code for making neural networks diagrams
Use Cases
Best For
- Creating neural network diagrams for academic papers and theses
- Generating consistent architecture visualizations for research presentations
- Documenting deep learning model architectures in publications
- Teaching neural network concepts with clear visual aids
- Automating diagram generation for multiple model variants
- Producing publication-ready figures that meet journal formatting requirements
Not Ideal For
- Projects requiring interactive or web-based neural network visualizations
- Teams without LaTeX expertise needing quick, drag-and-drop diagram tools
- Environments where diagram customization beyond predefined layers (e.g., truncated pyramids) is essential
- Use cases prioritizing real-time collaboration or cloud-based diagram editing
Pros & Cons
Pros
Uses LaTeX's TikZ package to generate scalable vector graphics, ensuring diagrams remain crisp in publications, as shown in the high-resolution examples provided.
Offers a Python API that allows automated diagram creation, enabling batch processing and consistency across multiple models, demonstrated in the pyexamples directory.
Includes predefined layers like Conv and Pool with uniform aesthetics, reducing manual styling effort and ensuring professional-looking outputs for academic papers.
Handles node positioning and connections between layers programmatically, simplifying the creation of complex architectures without manual alignment.
Directly generates high-quality PDFs with proper typography and scaling, tailored for journal submissions and presentations, as evidenced by the Overleaf links.
Cons
Missing key functionalities like easy legend support and more layer shapes (e.g., TruncatedPyramid), as admitted in the TODO list, limiting out-of-the-box usability.
Requires LaTeX installation with specific packages (e.g., texlive-latex-extra on Ubuntu or MikTeX on Windows), which can be non-trivial and error-prone for beginners.
Predefined layers may not cover all modern architectures (e.g., RNN examples are pending), forcing users to manually extend or customize code for niche cases.
Tightly coupled to LaTeX, making it unsuitable for workflows requiring other formats (e.g., web embeds) and adding a learning curve for those unfamiliar with TikZ.
Frequently Asked Questions
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