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Tensorspace is a WebGL-based 3D visualization framework and renderer designed to map deep learning model architectures and tensor data into interactive three-dimensional spaces. It serves as a neural network architecture visualizer and model inspector, allowing users to render model topologies and analyze data flow within a web browser. The project distinguishes itself through its ability to convert pre-trained Keras and TensorFlow models into spatial representations. It integrates with TensorFlow.js to execute inference in the browser, enabling the real-time visualization of intermediate act
cnn-explainer is an interactive web application and educational sandbox designed for visualizing the internal operations and layers of convolutional neural networks. It functions as a tool for understanding how these networks process image data through real-time graphics and interactive visualizations. The project includes a browser-based environment for training small convolutional neural networks on specific image classes. It also provides a model converter that transforms trained neural network files from backend framework formats into web-compatible versions for browser loading. The appl
Publication-ready NN-architecture schematics.
VisualDL is a deep learning visualization toolkit and experiment tracking dashboard. It provides a web-based interface for monitoring training metrics, analyzing high-dimensional data, and rendering model architectures through static and dynamic graphs. The toolkit serves as a performance profiler to identify execution bottlenecks and optimize resource usage. It also functions as a data analyzer that uses projection algorithms to identify relationships between points in complex datasets. Capabilities include tracking training metrics via scalars and histograms, comparing multiple experiments
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 main features of redaops/ann-visualizer are: Neural Network Architecture Visualizers, Architecture Visualizers, Sequential Model Builders, Neural Network Diagram Generators, Neural Network Visualizations, Graphviz DOT Integrations, Deep Learning Model Renderers, Image File Persistence.
Projects with overlapping indexed features include: tensorspace-team/tensorspace — Tensorspace is a WebGL-based 3D visualization framework and renderer designed to map deep learning model architectures… poloclub/cnn-explainer — cnn-explainer is an interactive web application and educational sandbox designed for visualizing the internal… alexlenail/nn-svg — Publication-ready NN-architecture schematics. sksq96/pytorch-summary — pytorch-summary is a collection of utilities for PyTorch neural networks designed to generate model summaries,… paddlepaddle/visualdl — VisualDL is a deep learning visualization toolkit and experiment tracking dashboard. It provides a web-based interface… harisiqbal88/plotneuralnet — PlotNeuralNet is a programmatic tool designed to generate high-quality visual representations of neural network…