PlotNeuralNet is a programmatic tool designed to generate high-quality visual representations of neural network architectures. It functions as a declarative visualization framework that converts structural definitions into professional-grade graphical output, specifically tailored for technical documentation and academic research papers.
The project distinguishes itself by utilizing a layer-centric procedural modeling approach, which applies standardized geometric templates to network components to ensure consistent visual styling. By leveraging a domain-specific macro language and a LaTeX-based engine, it translates high-level architectural descriptions into precise vector-based diagrams. This allows users to define complex network structures through a programming interface, automating the creation of schematics that accurately reflect model configurations.
Beyond basic generation, the tool supports the prototyping of deep learning models by visualizing layer connections and data flow. It employs coordinate-based layout calculations and modular component templating to maintain alignment and spacing across diagrams, ensuring that visual records remain consistent as model designs evolve.
