ActiVis is a tool for visualizing the activation of industry-scale deep neural networks. It was designed based on participatory design sessions with Facebook researchers over 11 months to address their needs of understanding complex models and huge datasets with diverse features. ActiVis allows for both instance-level and subset-level analysis of activations in a unified view. It scales to Facebook's large models and data through user-guided instance sampling and selective precomputation of important nodes. ActiVis has been deployed on Facebook's machine learning platform and was evaluated in case studies with Facebook participants analyzing text classification models.

1. ActiVis
Visual Exploration of Industry-Scale
Deep Neural Network Models
Minsuk Kahng Pierre Andrews Aditya Kalro Polo Chau
Georgia Tech Georgia TechFacebook Facebook
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2. Deep Learning is Powerful
2Image credit:http://www.nvidia.com/object/drive-px.html, https://venturebeat.com/2016/04/14/, https://finance.yahoo.com/news/

3. Understanding Deep Learning is Challenging
3
Cat
Dog
Cat
Dog
INPUT OUTPUTMODEL
Image credit: https://www.kaggle.com/c/dogs-vs-cats/
Incorrect

4. Cat
Dog
Visualizing Instance Activation
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Which neurons are highly activated for a given input?
INPUT OUTPUTMODEL
Cat
Dog 78%
22%
[Yosinski et al., 2015; Harley, 2015; Karpathy, 2016; Liu et al., 2017]

5. tags: #mycat, #cute
date: 10/1/2017
location: 33.7, 88.4
Practical Challenges in Industry
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DIVERSE INPUT TYPESCOMPLEX MODELS LARGE DATASETS
image, text, numerical,
categorical, …
many nodes
in graph-structure
1 billion+
instances
1. 2. 3.
Develop ActiVis for Facebook-scale models and dataGOAL:
Enjoying nice weather with kiki❤️

6. Understanding ActiVis Users’ Needs
Participatory design sessions over 11 months, with
15+ Facebook researchers, engineers, & data scientists
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7. Complex Model Architectures
UNDERSTANDING ACTIVIS USERS’ NEEDS (1/3)
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Numerous deep & wide models are used.
input
output
Separate architecture from activation detailsGOAL:

8. Huge Datasets with Diverse Features
UNDERSTANDING ACTIVIS USERS’ NEEDS (2/3)
1 billion+
instances
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1,000+ multi-type features
(e.g., image, text, numerical, categorical)
tags: #mycat, #cute
date: 10/1/2017
location: 33.7, 88.4
Enjoying nice weather with kiki❤️
Use multiple approaches for scalability
GOAL:
Make use of diverse features

9. Two Key Analytics Patterns
UNDERSTANDING ACTIVIS USERS’ NEEDS (3/3)
How model responds to
individual instances?
(This instance highly activates
neurons #2, 5, 11.)
How model behaves at higher-level
categorization (e.g., by topic)?
(A subset of instances about “sports” highly
activates neurons #3, 7, 11.)
Useful for debugging Useful for large datasets
SUBSET-LEVELINSTANCE-LEVEL
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[Kahng et al., 2016; Krause et al., 2016]
Complementary
[Kulesza et al., 2015; Amershi et al., 2015]
Support both analyticsGOAL:

10. ActiVis Design Goals (Recap)
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Two analytics patterns
Complex models
Huge datasets
1.
2.
3. Unified analysis for instances & subsets
Model overview as entry point
Multiple approaches for scalability

11. ActiVis
Visualizing activation of industry-scale deep neural nets
Deployed on Facebook ML Platform
11

12. 12
demo:
“Where is Phoenix located?” → location
“What is the diameter of a golf ball?” → numeric
Exploring text classification results
ActiVis Demo

13. Demo
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14. • Unified analysis for instances & subsets
• Model architecture to activation details
• Scaling to industry-scale data & models
14
ActiVis Key Ideas (Demo Recap)

15. User-guided Instance Sampling
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SCALING TO LARGE DATA & MODELS (1/2)
e.g., “What does VAST mean?”
should be in ABBR class.
Users either want representative samples or maintain “test cases”.

16. Selective Precomputation for Important Nodes
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SCALING TO LARGE DATA & MODELS (2/2)
Often only a few nodes are helpful and model developers know them.
precomputed

17. Deployed on FBLearner
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Model developers add 3 API calls to enable ActiVis.
Facebook ML platform, used by 25% of their engineers
Click to launch
ActiVis

18. Case Studies
3 Facebook participants.
All work with text classification models.
Each session 60 minutes long.
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19. Spot1. -checking models with “test cases”
“Where is … located?” → location
Graph architecture view as entry point2.
Debugging hints from activation patterns3.
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Key Observations from Case Studies

20. • Discover interesting subsets interactively
• Support input-dependent models (e.g., RNN)
• Provide direct guidance for performance improvement
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Future Research Directions

21. ActiVis
Visual Exploration of Industry-Scale
Deep Neural Network Models
✓ Deployed on Facebook’s ML platform
✓ Support subset-level analysis
✓ Model architecture to activation details
We thank Facebook Applied Machine Learning Group, especially
Yangqing Jia, Andrew Tulloch, Liang Xiong, and Zhao Tan and
NSF Graduate Research Fellowship Program.
Pierre Andrews
Aditya Kalro
Polo Chau
Georgia Tech
Facebook
Facebook
Minsuk Kahng
Georgia Tech PhD student
http://minsuk.com