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https://issues.apache.org/jira/browse/FLINK-2162?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Till Rohrmann updated FLINK-2162:
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Description:
At the moment, the SGD implementation uses a simple adaptive learning rate
strategy, {{adaptedLearningRate = initialLearningRate/sqrt(iterationNumber)}},
which makes the optimization algorithm sensitive to the setting of the
{{initialLearningRate}}. If this value is chosen wrongly, then the SGD might
become instable.
There are better ways to calculate the learning rate [1] such as Adagrad [3],
Adadelta [4], SGD with momentum [5] others [2]. They promise to result in more
stable optimization algorithms which don't require so much hyperparameter
tweaking. It might be worthwhile to investigate these approaches.
It might also be interesting to look at the implementation of vowpal wabbit [6].
Resources:
[1] [http://imgur.com/a/Hqolp]
[2] [http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html]
[3] [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf]
[4] [http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf]
[5] [http://www.willamette.edu/~gorr/classes/cs449/momrate.html]
[6] [https://github.com/JohnLangford/vowpal_wabbit]
was:
At the moment, the SGD implementation uses a simple adaptive learning rate
strategy, {{adaptedLearningRate = initialLearningRate/sqrt(iterationNumber)}},
which makes the optimization algorithm sensitive to the setting of the
{{initialLearningRate}}. If this value is chosen wrongly, then the SGD might
become instable.
There are better ways to calculate the learning rate [1] such as Adagrad [3],
Adadelta [4], SGD with momentum [5] others [2]. They promise to result in more
stable optimization algorithms which don't require so much hyperparameter
tweaking. It might be worthwhile to investigate these approaches.
Resources:
[1] [http://imgur.com/a/Hqolp]
[2] [http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html]
[3] [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf]
[4] [http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf]
[5] [http://www.willamette.edu/~gorr/classes/cs449/momrate.html]
> Implement adaptive learning rate strategies for SGD
> ---------------------------------------------------
>
> Key: FLINK-2162
> URL: https://issues.apache.org/jira/browse/FLINK-2162
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Priority: Minor
> Labels: ML
>
> At the moment, the SGD implementation uses a simple adaptive learning rate
> strategy, {{adaptedLearningRate =
> initialLearningRate/sqrt(iterationNumber)}}, which makes the optimization
> algorithm sensitive to the setting of the {{initialLearningRate}}. If this
> value is chosen wrongly, then the SGD might become instable.
> There are better ways to calculate the learning rate [1] such as Adagrad [3],
> Adadelta [4], SGD with momentum [5] others [2]. They promise to result in
> more stable optimization algorithms which don't require so much
> hyperparameter tweaking. It might be worthwhile to investigate these
> approaches.
> It might also be interesting to look at the implementation of vowpal wabbit
> [6].
> Resources:
> [1] [http://imgur.com/a/Hqolp]
> [2] [http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html]
> [3] [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf]
> [4] [http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf]
> [5] [http://www.willamette.edu/~gorr/classes/cs449/momrate.html]
> [6] [https://github.com/JohnLangford/vowpal_wabbit]
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