tensorflow / tensorflow/recommenders
loss becomes nan
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Description
58/108 [===============>..............] - ETA: 1:05:49 - root_mean_squared_error: nan - factorized_top_k/top_1_categorical_accuracy: 0.0045 - factorized_top_k/top_5_categorical_accuracy: 0.0089 - factorized_top_k/top_10_categorical_accuracy: 0.0130 - factorized_top_k/top_50_categorical_accuracy: 0.0295 - factorized_top_k/top_100_categorical_accuracy: 0.0380 - loss: nan - regularization_loss: nan - total_loss: nan
why do we have the loss becoming nan? Any tips appreciated.
Thanks,
Robert
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Research direction
No source files, tests, or entry points are identified; the report only includes training output showing NaN values. Start by obtaining the model, data, and training configuration that produced this run, then reproduce it and trace when the loss first becomes NaN. Done means identifying a reproducible cause and documenting or testing the corresponding fix.
Written by the indexing model from the issue text.
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
- 25/100