Meet TorchExplorer: A New Interactive Neural Network Visualizer

TorchExplorer is a new AI tool for researchers working with unconventional neural network architectures. It automatically generates a Vega Custom Chart in wandb to visualize network architecture and allows local deployment. The user interface features an interactive module-level graph, edge representations, and column panels for detailed inspection, making it a valuable tool for understanding complex network behavior.

 Meet TorchExplorer: A New Interactive Neural Network Visualizer

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Introducing TorchExplorer: A New AI Tool for Researchers

A new AI research introduces TorchExplorer, a novel AI tool designed for researchers working with unconventional neural network architectures. It provides an interactive and insightful exploration of network layers, aiding in understanding complex neural network models.

GitHub Repository

Key Features of TorchExplorer

The left-hand panel features a module-level graph extracted from the autograd graph, allowing researchers to navigate through the network’s structure. Clicking on a module reveals its internal submodules, and a convenient expanding list helps users return to parent modules effortlessly.

Edges between nodes signify autograd traces, indicating the flow of information from parent to child modules. TorchExplorer’s column panels on the right enable users to inspect modules in detail by dragging and dropping them.

Input/output histograms showcase values passing into and out of a module’s forward method, providing a detailed view of data distribution. Additionally, input/output gradient norm histograms capture tensor gradients from backward passes, offering insights into the ℓ2-norm of gradients averaged over the batch dimension.

A noteworthy feature of TorchExplorer is its ability to handle non-standard architectures effectively, making it a valuable companion for researchers engaged in deep learning experiments.

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