This document discusses using SigOpt to tune deep learning models. It notes that tuning deep learning systems is non-intuitive and expert-intensive using traditional random search or grid search methods. SigOpt provides a more efficient approach using Bayesian optimization to suggest optimal hyperparameters after each trial, reducing wasted expert time and computation. The document provides examples applying SigOpt to tune convolutional neural networks on CIFAR10, demonstrating a 1.6% reduction in error rate over expert tuning with no wasted trials.

1. USING SIGOPT TO TUNE DEEP LEARNING
MODELS WITH NERVANA CLOUD
Scott Clark
Co-founder and CEO of SigOpt
scott@sigopt.com @DrScottClark

2. TRIAL AND ERROR WASTES EXPERT TIME
● Deep Learning is extremely powerful
● Tuning Deep Learning systems is extremely non-intuitive

3. UNRESOLVED PROBLEM IN ML
https://www.quora.com/What-is-the-most-important-unresolved-problem-in-machine-learning-3
What is the most important unresolved problem in machine learning?
“...we still don't really know why some configurations of deep neural networks work
in some case and not others, let alone having a more or less automatic approach
to determining the architectures and the hyperparameters.”
Xavier Amatriain, VP Engineering at Quora
(former Director of Research at Netflix)

4. TUNING DEEP LEARNING MODELS
Big Data
Deep Learning
System
With tunable parameters
Expertise

5. TUNING DEEP LEARNING MODELS
Big Data
Metics
Optimally suggests
new parameters
Objective
New parameters
Expertise
Deep Learning
System
With tunable parameters

6. TUNING DEEP LEARNING MODELS
Big Data
Metics
Optimally suggests
new parameters
Objective
New parameters
Better
Results
Expertise
Deep Learning
System
With tunable parameters

7. COMMON APPROACH
Random Search for Hyper-Parameter Optimization, James Bergstra et al., 2012
1. Random search or grid search
2. Expert defined grid search near “good” points
3. Refine domain and repeat steps - “grad student descent”

8. COMMON APPROACH
● Expert intensive
● Computationally intensive
● Finds potentially local optima
● Does not fully exploit useful information
Random Search for Hyper-Parameter Optimization, James Bergstra et al., 2012
1. Random search or grid search
2. Expert defined grid search near “good” points
3. Refine domain and repeat steps - “grad student descent”

9. … the challenge of how to collect information as efficiently
as possible, primarily for settings where collecting information
is time consuming and expensive.
Prof. Warren Powell - Princeton
What is the most efficient way to collect information?
Prof. Peter Frazier - Cornell
How do we make the most money, as fast as possible?
Me - @DrScottClark
OPTIMAL LEARNING

10. ● Optimize some Overall Evaluation Criterion (OEC)
○ Loss, Accuracy, Likelihood, Revenue
● Given tunable parameters
○ Hyperparameters, feature parameters
● In an efficient way
○ Sample function as few times as possible
○ Training on big data is expensive
BAYESIAN GLOBAL OPTIMIZATION
Details at https://sigopt.com/research

11. EXAMPLE: TUNING DNN CLASSIFIERS
CIFAR10 Dataset
● Photos of objects
● 10 classes
● Metric: Accuracy
○ [0.1, 1.0]
Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.

12. ● All convolutional neural network
● Multiple convolutional and dropout layers
● Hyperparameter optimization mixture of
domain expertise and grid search (brute force)
USE CASE: ALL CONVOLUTIONAL
http://arxiv.org/pdf/1412.6806.pdf

13. EXAMPLE: NCLOUD/NEON
● epochs: “number of epochs to run fit” - int [1,∞]
● learning rate: influence on current value of weights at each step - double (0, 1]
● momentum coefficient: “the coefficient of momentum” - double (0, 1]
● weight decay: parameter affecting how quickly weight decays - double (0, 1]
● depth: parameter affecting number of layers in net - int [1, 20(?)]
● gaussian scale: standard deviation of initialization normal dist. - double (0,∞]
● momentum step change: mul. amount to decrease momentum - double (0, 1]
● momentum step schedule start: epoch to start decreasing momentum - int [1,∞]
● momentum schedule width: epoch stride for decreasing momentum - int [1,∞]
Many tunable parameters...
...optimal values non-intuitive

14. COMPARATIVE PERFORMANCE
● Expert baseline: 0.8995
○ (using neon)
● SigOpt best: 0.9011
○ 1.6% reduction in
error rate
○ No expert time
wasted in tuning

15. USE CASE: DEEP RESIDUAL
http://arxiv.org/pdf/1512.03385v1.pdf
● Explicitly reformulate the layers as learning residual functions with
reference to the layer inputs, instead of learning unreferenced functions
● Variable depth
● Hyperparameter optimization mixture of domain expertise and grid
search (brute force)

16. COMPARATIVE PERFORMANCE
Standard Method
● Expert baseline: 0.9339
○ (from paper)
● SigOpt best: 0.9343
○ Found after 17 trials
○ 0.61% reduction in
error rate
○ No expert time
wasted in tuning

17. Questions?
scott@sigopt.com
@DrScottClark
https://sigopt.com
@SigOpt

18. TRY OUT SIGOPT FOR FREE
https://sigopt.com/get_started
● Quick example and intro to SigOpt
● No signup required
● Visual and code examples
https://sigopt.com/text-classifier
● Jupyter Notebook
● Use SigOpt to tune feature and model parameters
● Detailed walkthrough with code

19. MORE EXAMPLES
https://github.com/sigopt/sigopt-examples
Examples of using SigOpt in a variety of languages and contexts.
Tuning Machine Learning Models (with code)
A comparison of different hyperparameter optimization methods.
Using Model Tuning to Beat Vegas (with code)
Using SigOpt to tune a model for predicting basketball scores.
Learn more about the technology behind SigOpt at
https://sigopt.com/research

20. HOW DOES IT WORK?
1. User reports data
2. SigOpt builds statistical model
(Gaussian Process)
3. SigOpt finds the points of
highest Expected Improvement
4. SigOpt suggests best
parameters to test next
5. User tests those parameters
and reports results to SigOpt
6. Repeat

21. HOW DOES IT WORK?
1. User reports data
2. SigOpt builds statistical model
(Gaussian Process)
3. SigOpt finds the points of
highest Expected Improvement
4. SigOpt suggests best
parameters to test next
5. User tests those parameters
and reports results to SigOpt
6. Repeat

22. HOW DOES IT WORK?
1. User reports data
2. SigOpt builds statistical model
(Gaussian Process)
3. SigOpt finds the points of
highest Expected Improvement
4. SigOpt suggests best
parameters to test next
5. User tests those parameters
and reports results to SigOpt
6. Repeat

23. HOW DOES IT WORK?
1. User reports data
2. SigOpt builds statistical model
(Gaussian Process)
3. SigOpt finds the points of
highest Expected Improvement
4. SigOpt suggests best
parameters to test next
5. User tests those parameters
and reports results to SigOpt
6. Repeat

24. HOW DOES IT WORK?
1. User reports data
2. SigOpt builds statistical model
(Gaussian Process)
3. SigOpt finds the points of
highest Expected Improvement
4. SigOpt suggests best
parameters to test next
5. User tests those parameters
and reports results to SigOpt
6. Repeat

25. HOW DOES IT WORK?
1. User reports data
2. SigOpt builds statistical model
(Gaussian Process)
3. SigOpt finds the points of
highest Expected Improvement
4. SigOpt suggests best
parameters to test next
5. User tests those parameters
and reports results to SigOpt
6. Repeat

26. GPs: FUNCTIONAL VIEW

27. overfit good fit underfit
GPs: FITTING THE GP