Implementation of loss function implementation.
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- Python
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Description
I am reading the pretraining and finetuning loss function and am trying to understand the implementation of this loss:
I think I have a general idea of implementation, but would like to confirm in case I am not missing any details.
The idea I have is as follows:
For each variable calculate the MAE loss
Multiply each variable loss by specific variable weight.
Sum loss of variables grouping by Surface variables and Atmospheric Variables separately
Multiply Surface and Atmospheric losses by alpha and beta weights respectively
Finally sum losses together and multiply by (dataset weight/(tot # variables in dataset)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue does not name a file, test, or entry point. Read the pretraining and finetuning loss implementation and compare it with the attached formula, checking the variable, surface/atmospheric, alpha/beta, and dataset weighting. Done means confirming or correcting the interpretation in the issue.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100