fixed reg loss not being normalized by batch_split#61
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Pull request overview
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Adjusts latent-code regularization scaling so it remains consistent when training batches are split into multiple chunks (batch_split).
Changes:
- Updates
reg_lossnormalization to use samples-per-split (num_sdf_samples / batch_split) instead of the fullnum_sdf_samples. - Reformats the
reg_lossexpression to span multiple lines for readability.
Comment on lines
+603
to
+605
| reg_loss = (code_reg_lambda * min(1, epoch / 100) * l2_size_loss) / ( | ||
| num_sdf_samples / batch_split | ||
| ) |
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Overall Grade |
Security Reliability Complexity Hygiene |
Code Review Summary
| Analyzer | Status | Updated (UTC) | Details |
|---|---|---|---|
| Python |  |
May 20, 2026 7:01a.m. | Review ↗ |
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