Machine Learning Graph Visualization With Hierarchical Node Aggregation
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Solution Overview
Problem
Visualizing large machine learning models with hundreds of thousands or millions of operations is challenging due to limited display space, making it difficult to effectively present and navigate their hierarchical structure.
Innovation Solution
A visualization generator that identifies related nodes in a machine learning model and aggregates them into hierarchical layers, allowing for a visual layout that presents the model's architecture at different levels of granularity, with customizable user interaction for real-time modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individual nodes are displayed in the computational graph, then detailed information about each operation is provided, but the visualization becomes unreadable and unusable when the model contains hundreds of thousands or millions of operations
Solution Approach 1:
The patent merges multiple individual nodes into aggregated node groups that represent functional units or layers. Each aggregated node visually combines multiple operations (e.g., convolution layers, fully connected layers) into a single visual element, allowing the graph to remain readable while preserving access to detailed operation information through expansion or tooltip features.
Solution Approach 2:
The computational graph is segmented into hierarchical levels where aggregated nodes represent higher-level functional units. Users can navigate between different levels of granularity, viewing the graph as a whole with aggregated nodes, then expanding specific nodes to see individual operations when detailed inspection is needed.
2Loss of information
If all individual nodes are displayed, then complete model architecture is visualized, but the display space is exceeded making navigation difficult
Solution Approach 1:
Multiple nodes representing operations within the same functional unit are merged into single aggregated visual elements. This consolidation dramatically reduces the total number of visual elements displayed, fitting large models within limited screen real estate while maintaining complete architectural information in an compressed format.
Solution Approach 2:
The patent introduces a hierarchical dimension to the visualization, organizing nodes into multiple aggregation levels. This allows the model architecture to be represented in a compact 2D layout by adding the aggregation level as an additional organizational dimension, enabling complete architecture visualization without exceeding display space.
3Ease of operation
If nodes are aggregated into functional units, then readability and navigation are improved, but detailed information about individual operations becomes harder to access
Solution Approach 1:
The visualization implements a nested structure where aggregated nodes contain individual operation nodes within them. Users can click or expand an aggregated node to reveal the individual operations it contains, creating a nested view that transitions from high-level functional units to detailed operations, thus maintaining both navigation ease and information accessibility.
Solution Approach 2:
The visualization is made dynamic, allowing users to interactively expand and collapse aggregated nodes. This dynamic behavior enables the graph to adapt its level of detail based on user needs, providing a high-level overview for navigation and detailed operation views for inspection, with smooth transitions between states.
Data Source
AI summary
Systems, methods, devices, and related techniques for visualizing machine learning models with hierarchical structure are described in this document. In some implementations, a visualization generator receives a computational graph for a machine learning model and outputs a visual layout of the computational graph. The visual layout includes visual elements organized to present the hierarchical structure of the computational graph The visual layout can include respective visual elements for each node aggregation layer defined by the hierarchical structure. The respective visual elements for each node aggregation layer are provided in lieu of respective visual elements for individual nodes of the multiple nodes assigned to the one or more node aggregation layers.