Model visualizer

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-ba

SHAP is an explainable AI toolkit that provides a game theoretic framework for interpreting machine learning model predictions. It functions as a feature attribution engine, decomposing model outputs into the sum of individual feature effects to clarify how specific input variables influence a final decision. By assigning importance values to these inputs, the library enables users to understand the logic behind complex predictive models. The project distinguishes itself through its versatility and specialized calculation methods. It operates as a model-agnostic diagnostic library, capable of

Transformers-interpret is a diagnostic library designed for the interpretability of transformer-based machine learning models. It functions as an attribution framework that quantifies the contribution of individual input tokens to a model's final predictions, allowing users to audit decision patterns and debug natural language processing tasks. The library utilizes gradient-based analysis and hook-based introspection to trace how specific input features influence model outputs. By mapping abstract numerical attribution scores back to human-readable linguistic units, it provides a clear view o

Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users

This project is a browser-based machine learning education tool and neural network sandbox. It provides an interactive environment for experimenting with network architectures and hyperparameters to understand deep learning concepts. The tool functions as a visualizer for TensorFlow neural networks, allowing users to see how models learn and classify data in real time. It enables the prototyping of model architectures to observe how different hidden layers and neurons affect a network's ability to solve specific data patterns. The system covers neural network architecture and operation visua

TensorBoard is a visualization toolkit for tracking and analyzing machine learning model training progress and performance using TensorFlow event logs. It provides a monitoring dashboard for plotting scalar metrics, tensor distributions, and training curves, and includes specialized tools for visualizing neural network computational graphs and projecting high-dimensional embeddings. The project enables side-by-side comparison of multiple training runs to analyze the impact of hyperparameters on model outcomes. It also features a high-dimensional embedding projector and a graph visualizer for

Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in

Debugging, monitoring and visualization for Python Machine Learning and Data Science

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