IEEE Trans Vis Comput Graph. 2018 Jan;24(1):1-12. doi: 10.1109/TVCG.2017.2744878. Epub 2017 Aug 29.
We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.
我们介绍了 TensorFlow 图可视化工具的设计研究，这是 TensorFlow 机器智能平台的一部分。这个工具通过可视化底层数据流图，帮助用户理解复杂的机器学习架构。该工具通过应用一系列图变换来实现，这些变换可应用标准布局技术来生成清晰易读的交互式图表。为了使图表更简洁，我们将非关键节点与布局分离。为了提供概览，我们使用源代码中注释的层次结构构建了一个聚类图。为了支持按需探索嵌套结构，我们进行边缘捆绑以实现稳定且响应迅速的聚类扩展。最后，我们检测并突出显示重复结构，以强调模型的模块化组成。为了展示可视化工具的实用性，我们描述了一些示例使用场景并报告了用户反馈。总的来说，用户发现这个可视化工具对于理解、调试和共享模型结构非常有用。
