Backend PyTorch: Add L1 and L1+L2 regularizers #1905
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We are unifying the regularization for tensorflow and paddle, see #1894 . Do you think we can also unify pytorch regularization in a more unified code? |
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Unfortunately, this can be a bit difficult in pytorch. The implementation options seem unnecessarily complicated to me. |
lululxvi
reviewed
Dec 18, 2024
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| def train_step(inputs, targets, auxiliary_vars): | ||
| def closure(): | ||
| losses = outputs_losses_train(inputs, targets, auxiliary_vars)[1] | ||
| total_loss = torch.sum(losses) | ||
| if l1_factor: |
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For l1+l2 regularization, this might not be the correct way. weight_decay in the optimizers is implemented not as the L2 loss function. We should only consider either L1 or L2, not L1 + L2 now.
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I didn't notice earlier that you implemented the NysNewtonCG optimizer. Should I add L1 regularization in |
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Continuation of the PR #1884