NeuroMapper: In-browser Visualizer for Neural Network Training

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 alreadytrained 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 https://github.com/poloclub/NeuroMapper and runs in all modern web browsers. A demo of the tool in action is available at: https://poloclub.github.io/NeuroMapper/.