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Build your own Machine Learning Visualizations with the new TensorBoard API
Monday, September 11, 2017
Posted by Chi Zeng and Justine Tunney, Software Engineers, Google Brain Team
When
we open-sourced TensorFlow in 2015
, it included
TensorBoard
, a suite of visualizations for inspecting and understanding your TensorFlow models and runs. Tensorboard included a small, predetermined set of visualizations that are generic and applicable to nearly all deep learning applications such as observing how loss changes over time or
exploring clusters in high-dimensional spaces
. However, in the absence of reusable APIs, adding new visualizations to TensorBoard was prohibitively difficult for anyone outside of the TensorFlow team, leaving out a long tail of potentially creative, beautiful and useful visualizations that could be built by the research community.
To allow the creation of new and useful visualizations, we announce the release of a consistent
set of APIs
that allows developers to add custom visualization plugins to TensorBoard. We hope that developers use this API to extend TensorBoard and ensure that it covers a wider variety of use cases.
We have updated the existing dashboards (tabs) in TensorBoard to use the new API, so they serve as examples for plugin creators. For the current listing of plugins included within TensorBoard, you can explore the
tensorboard/plugins directory on GitHub
. For instance, observe the new plugin that generates
precision-recall curves
:
The plugin demonstrates the 3 parts of a standard TensorBoard plugin:
A TensorFlow summary op used to collect data for later visualization. [
GitHub
]
A Python backend that serves custom data. [
GitHub
]
A dashboard within TensorBoard built with TypeScript and polymer. [
GitHub
]
Additionally, like other plugins, the “pr_curves” plugin
provides a demo
that (1) users can look over in order to learn how to use the plugin and (2) the plugin author can use to generate example data during development. To further clarify how plugins work, we’ve also created a barebones
TensorBoard “Greeter” plugin
. This simple plugin collects greetings (simple strings preceded by “Hello, ”) during model runs and displays them. We recommend starting by exploring (or forking) the Greeter plugin as well as other
existing plugins
.
A notable example of how contributors are already using the TensorBoard API is
Beholder,
which was recently created by
Chris Anderson
while working on his master’s degree. Beholder shows a live video feed of data (e.g. gradients and convolution filters) as a model trains. You can watch the demo video
here
.
We look forward to seeing what innovations will come out of the community. If you plan to contribute a plugin to TensorBoard’s repository, you should get in touch with us first through the
issue tracker
with your idea so that we can help out and possibly guide you.
Acknowledgements
Dandelion Mané and William Chargin played crucial roles in building this API.
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