Kevin Li

NeuroMapper: In-browser Visualizer for Neural Network Training

Zhiyan Zhou
Haekyu Park
Megan Dass
Austin Wright
Nilaksh Das
Duen Horng Chau
IEEE VIS: Visualization & Visual Analytics (IEEE VIS), 2022


We present our ongoing work NeuroMapper, an in-browser visualization tool that helps machine learning (ML) developers interpret the evolution of a model during training, providing a new way to monitor the training process and visually discover reasons for suboptimal training. While most existing deep neural networks (DNNs) interpretation tools are designed for already-trained model, NeuroMapper scalably visualizes the evolution of the embeddings of a model’s blocks across training epochs, enabling real-time visualization of 40,000 embedded points. To promote the embedding visualizations’ spatial coherence across epochs, NeuroMapper adapts AlignedUMAP, a recent nonlinear dimensionality reduction technique to align the embeddings. With NeuroMapper, users can explore the training dynamics of a Resnet-50 model, and adjust the embedding visualizations’ parameters in real time. NeuroMapper is open-sourced at and runs in all modern web browsers. A demo of the tool in action is available at:




  title={NeuroMapper: In-browser Visualizer for Neural Network Training},
  author={Zhiyan Zhou and Kevin Li and Haekyu Park and Megan Dass and Austin P. Wright and Nilaksh Das and Duen Horng Chau},