VisualKeras is a Python package that turns Keras and TensorFlow neural network architectures into clear, publication-ready diagrams. Instead of the dense default model plots, it generates styled visualizations that are easy to read in papers, theses and slide decks. The project is released under the MIT license, has about 661 stars on GitHub, and was updated in March 2026.
VisualKeras works with both standalone Keras and the Keras bundled inside TensorFlow. Its layered style renders networks as stacked layer blocks, which works especially well for convolutional neural networks where the flow from input to classifier matters. For general models, including plain feed-forward networks, a graph style shows nodes and connections instead. Styling is deliberately flexible: colors, spacing, layer dimensions and text can be adjusted so the diagram matches the look of a paper or presentation rather than a debugging screenshot. The package is installed from PyPI, documented on Read the Docs, and its repository highlights an extensive automated test suite, which makes it a dependable little utility in research workflows.
The project is maintained on GitHub with its examples and issue tracker in the open, and its documentation explains both rendering styles with sample outputs. Because it works directly from the model definition, it fits naturally at the end of an experiment or just before writing up results, when an accurate architecture figure is needed.
VisualKeras is free and open source under the MIT license. There is no paid version or hosted service; you install the package and render diagrams locally.
VisualKeras is for machine learning researchers, students and engineers who need clean neural network architecture diagrams for publications, documentation, teaching material or internal design reviews.