Model is not computing metrics after recompiling in efficient serving tutorial

Open
#661 1 comment 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
30/100
Issue type
Bug
Clarity
Needs clarification
Activity status
Stale
Tech stack
python, tensorflow
Domain
machine-learning

Research direction

Start by running the efficient_serving tutorial and reproduce the first evaluation, then add the Brute force layer or scann layer, recompile, and evaluate again. Compare the returned metrics and losses with the screenshots; done means metrics are present after recompiling and evaluating the model.

Written by the indexing model from the issue text.

Description

I am running the code in efficient_serving tutorial. On evaluating for first time, model is computing metrics and losses. But after adding Brute force layer or scann layer, recompiling and evaluating the model, the model is computing losses only.

Screenshot 2023-04-11 at 1 45 20 PM Screenshot 2023-04-11 at 1 45 32 PM Screenshot 2023-04-11 at 1 34 09 PM

As shown in the screenshot, metrics are not found in the result.

Dominant language
Python
Stars
2k
Forks
300
No merged PRs in 30d

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from tensorflow/recommenders

All issues in tensorflow/recommenders

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.