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https://issues.apache.org/jira/browse/HIVEMALL-222?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Makoto Yui updated HIVEMALL-222:
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Description:
Gradient Clipping is useful for avoiding exploding gradients
[https://github.com/scikit-learn/scikit-learn/blob/0fc7ce6bb8bb6b5b98c66ad2c0a009753def945a/sklearn/linear_model/sgd_fast.pyx#L701]
Details of exploding gradients can be found in [this
link|http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/readings/L15%20Exploding%20and%20Vanishing%20Gradients.pdf].
So, implement it for General Classifier/Regressor
was:
Gradient Clipping is useful for avoiding exploding gradients
[https://github.com/scikit-learn/scikit-learn/blob/0fc7ce6bb8bb6b5b98c66ad2c0a009753def945a/sklearn/linear_model/sgd_fast.pyx#L701]
So, implement it for General Classifier/Regressor
> Introduce Gradient Clipping to avoid exploding gradient to General
> Classifier/Regressor
> ---------------------------------------------------------------------------------------
>
> Key: HIVEMALL-222
> URL: https://issues.apache.org/jira/browse/HIVEMALL-222
> Project: Hivemall
> Issue Type: Improvement
> Affects Versions: 0.5.0
> Reporter: Makoto Yui
> Assignee: Makoto Yui
> Priority: Minor
> Fix For: 0.5.2
>
>
> Gradient Clipping is useful for avoiding exploding gradients
>
> [https://github.com/scikit-learn/scikit-learn/blob/0fc7ce6bb8bb6b5b98c66ad2c0a009753def945a/sklearn/linear_model/sgd_fast.pyx#L701]
> Details of exploding gradients can be found in [this
> link|http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/readings/L15%20Exploding%20and%20Vanishing%20Gradients.pdf].
> So, implement it for General Classifier/Regressor
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