tensorflow / tensorflow/recommenders
Ranking model predicting same value for all
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- Dominant language
- Python
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Description
Hi,
I am training a Ranking model with some basic numeric features and the target is either 0 or some value between 0-1.
So the model is training and just in 1 epoch it comes to a minimum loss and doesnot decrease more.
The main issue is observed when i predict with test data , i get all the predictions as 0. tried some changes in Layers but all leads to almost 99% values as 0
Where as same architecture when i trained using sequential model . its learning and getting atleast some values in prediction .
Not sure why with tfrs Ranking its some issue .
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Research direction
No files, tests, or entry points are named. Start by reproducing the Ranking model with the described numeric features and binary-or-continuous target, then compare its training and prediction behavior with the working Sequential model. Done means identifying why predictions collapse to zero and documenting or fixing the difference.
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Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100