Gneural Network - Tasks: task #14202, Implement Dropout regularization...
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task #14202: Implement Dropout regularization for nnet.
Submitter: | Ray Dillinger <rayd> | ||
Submitted: | Sun 30 Oct 2016 05:05:19 PM UTC | ||
Should Start On: | Sun 30 Oct 2016 07:00:00 AM UTC | Should be Finished on: | Fri 30 Dec 2016 08:00:00 AM UTC |
Category: | None | Priority: | 5 - Normal |
Status: | None | Privacy: | Public |
Assigned to: | None | Percent Complete: | 0% |
Open/Closed: | Open | Effort: | 0.00 |
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Date | Changed by | Updated Field | Previous Value | => | Replaced by |
---|---|---|---|---|---|
2016-10-30 | rayd | Should be Finished on | 2016-10-30 | 2016-12-30 |
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Under dropout regularization, during training some randomly-selected fraction of the nodes simply don't activate, and the signal from all other nodes is multiplied by the complement of that fraction.
This is a very effective way to prevent overfitting, even when the subsequent layer has more weights than the previous.