Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Correct testing loss in the quickstart_pytorch example #1672

Conversation

charlesbvll
Copy link
Member

Reference Issues/PRs

#1604

What does this implement/fix? Explain your changes.

Makes the total testing loss in the quickstart_pytorch example more consistent by changing the reduction method of the criterion from mean to sum.

Copy link
Member

@danieljanes danieljanes left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Lgtm!

@danieljanes danieljanes merged commit d2cc601 into main Feb 20, 2023
@danieljanes danieljanes deleted the 1604-incorrect-calculation-of-running-loss-in-pytorch-example-code branch February 20, 2023 13:45
tanertopal added a commit that referenced this pull request Feb 20, 2023
* improve_state:
  Apply autoformat
  Apply more review changes
  Update src/py/flwr/server/state/state_test.py
  Add review changes
  Fix quickstart Scikit Learn example (#1683)
  Fix PyTorch MNIST example doc (#1684)
  Update src/py/flwr/server/state/in_memory_state.py
  Update src/py/flwr/server/state/state_test.py
  Update src/py/flwr/server/state/state_test.py
  Update src/py/flwr/server/state/state_test.py
  Update src/py/flwr/server/state/in_memory_state.py
  Add baselines to good first contributions doc (#1679)
  Add FedProx to README (#1681)
  Add FedProx and FedAvg to baselines doc (#1680)
  Fix diagrams in docs (#1677)
  Correct testing loss in the quickstart_pytorch example (#1672)
  Improve testing in state
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Incorrect calculation of running loss in PyTorch example code
2 participants