Skip to content

GitLab

  • Menu
Projects Groups Snippets
    • Loading...
  • Help
    • Help
    • Support
    • Community forum
    • Submit feedback
    • Contribute to GitLab
  • Sign in / Register
  • ketos ketos
  • Project information
    • Project information
    • Activity
    • Labels
    • Planning hierarchy
    • Members
  • Repository
    • Repository
    • Files
    • Commits
    • Branches
    • Tags
    • Contributors
    • Graph
    • Compare
  • Issues 27
    • Issues 27
    • List
    • Boards
    • Service Desk
    • Milestones
  • Merge requests 0
    • Merge requests 0
  • CI/CD
    • CI/CD
    • Pipelines
    • Jobs
    • Schedules
  • Deployments
    • Deployments
    • Environments
    • Releases
  • Monitor
    • Monitor
    • Incidents
  • Analytics
    • Analytics
    • Value stream
    • CI/CD
    • Repository
  • Wiki
    • Wiki
  • Snippets
    • Snippets
  • Activity
  • Graph
  • Create a new issue
  • Jobs
  • Commits
  • Issue Boards
Collapse sidebar
  • public_projects
  • ketosketos
  • Issues
  • #169

Closed
Open
Created Apr 02, 2022 by Oliver Kirsebom@kirsebomOwner

Remove dropout from ResNet? (And other ketos architectures)

In my own experience, training the ketos ResNet architecture with dropout > 0 results in (far) worse performance than dropout = 0. This appears to be consistent with observations made by others. See for example: https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html . I'm wondering if we should simply remove the dropout argument from the definition of the ResNetBlock and ResNetArch in ketos: https://gitlab.meridian.cs.dal.ca/public_projects/ketos/-/blob/master/ketos/neural_networks/resnet.py#L100 ?

@bpadovese , @fsfrazao , your thoughts/experiences?

I'm not sure if the dropout has similar negative impact on the other ketos architectures (cnn, densenet, inception) ...

To upload designs, you'll need to enable LFS and have an admin enable hashed storage. More information
Assignee
Assign to
Time tracking