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optimizer.step()
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```
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Please visit our [website](https://libauc.org/) or [github](https://github.com/Optimization-AI/LibAUC) for more examples.
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Tips
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---
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Please check the following list before running experiments:
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- Compute the `imbalance_ratio` from your train set and pass it to `AUCMLoss(imratio=xxx)`
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- Choose the proper `initial learning rate` and use `optimizer.update_regularizer(decay_factor=10)` to decay learning rate
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- Use activation function e.g., `torch.sigmoid()` before passing model outputs to loss function
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- Reshape both `preds` and `targets` to `(N, 1)` before constructing loss
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Please visit our [website](https://libauc.org/) or [github](https://github.com/Optimization-AI/LibAUC) for more examples.
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Citation
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