Founded in 1957, Magna has established itself as the largest automotive supplier in North America. With over 60 years of experience supplying components and systems to manufacturers worldwide, Magna is a visionary leader, driving the evolution of the automotive industry.

In the ever-evolving world of artificial intelligence, understanding how neural networks learn has been a complex challenge for engineers. Monitoring model performance, diagnosing issues, and optimizing architectures can be a daunting task, especially when working with large-scale networks.

Our Visualizing Neural Network Gradients software solves this problem for Magna, offering an innovative two-part solution: a logger, and a visualization tool. The logger seamlessly integrates into engineers’ existing machine learning pipelines, collecting data in real time. The visualization tool offers an interactive interface that provides an intuitive representation of the model’s learning process, revolutionizing how engineers analyze their networks, placing an emphasis on convenience and actionable insight.

Our platform provides detailed 2D and 3D visualizations of the neural network structure, with each layer and gradient dynamically visualized to reflect performance metrics like gradient flow and magnitude. Engineers can monitor performance in real time and identify potential issues with the network, such as vanishing or exploding gradients, enabling more efficient troubleshooting and model optimization.

Built using Electron, our platform leverages Three.js for 3D visualization, creating a fully immersive environment. The Python-based logger supports two widely used machine learning frameworks, PyTorch and TensorFlow, recording gradient and network data in HDF5 files that smoothly integrate with the visualization tool for real-time analysis.