RedaOps/ann-visualizerArchived
Ann Visualizer
Ann-visualizer is a Python library and diagram generator that creates graphical representations of artificial neural network architectures from sequential machine learning models. It translates layer dimensions, connectivity, and network topology into high-resolution image files that can be exported directly to disk for documentation, presentations, and design verification before training.
The tool processes sequential model definitions by traversing layer structures and utilizing layout commands to render clear diagrams. These visualizations assist in inspecting network designs and serve as an educational aid for understanding complex deep learning structures.
Features
- Neural Network Architecture Visualizers - Generates graphical architecture diagrams from sequential deep learning models and exports them to disk.
- Architecture Visualizers - Renders layer-by-layer structures of deep learning models for documentation and presentations.
- Sequential Model Builders - Traverses linear layer stacks in sequential machine learning models to map network topology.
- Neural Network Diagram Generators - Converts code-based sequential machine learning models into visual network diagram image files.
- Neural Network Visualizations - Draws graphical diagrams of artificial neural network architectures from sequential model definitions.
- Graphviz DOT Integrations - Bridges neural network metadata with Graphviz DOT layout tools to render structural diagrams.
- Deep Learning Model Renderers - Visually inspects sequential model definitions and layer connectivity to verify neural network designs before training.
- Image File Persistence - Exports rendered neural network diagrams directly to local image files for offline documentation and presentation.
