| name | pytorch-training |
| description | PyTorch model-building conventions and a neural-net training debug checklist. Use this skill whenever writing or reviewing PyTorch code that defines a model (nn.Linear, nn.Conv2d, BatchNorm) or trains one (training loop, optimizer, LR schedule), and ESPECIALLY when debugging training problems — loss not converging, loss stuck or flat, suspiciously slow learning, "hockey stick" loss curves, or a model that seems to ignore its inputs. Also consult it before starting any from-scratch training run, even if the user doesn't mention debugging. |
PyTorch Training
Model-building conventions
- Set
bias=False on any nn.Linear/nn.Conv2d immediately followed by BatchNorm — BN's mean-subtraction + learnable beta cancels the bias, making it dead params. Keep bias=True (default) on the output/classifier head, which has no BN after it.
Neural net training checklist (common mistakes if skipped)
Run through these before and during any training run — each one catches a class of silent bug that wastes full training runs:
- Overfit a single batch first. Before full training, verify the model can drive loss to ~0 on one small batch — if it can't, there's a bug in the model/loss/data pipeline; no point training on the full set.
- Verify loss @ init. Check the starting loss equals the theoretical value — softmax over
n classes should give -log(1/n) (e.g. ~2.30 for 10 classes). A mismatch means a bad head init or mislabeled targets.
- Input-independent baseline. Train once with inputs zeroed out; the model must do worse than with real inputs. If they match, the data pipeline isn't actually feeding the model (it's only learning the prior).
- Init the output-layer bias to data statistics. Regression with mean 50 → init bias 50; class imbalance 1:10 → set logit bias so p≈0.1 at init. Kills the "hockey stick" loss curve where early steps just learn the bias.
- Visualize the exact tensor right before
y_hat = model(x). Decode what actually enters the net — the only source of truth for catching preprocessing/augmentation bugs (e.g. forgetting to flip labels when flipping images).
- Don't trust LR-decay defaults; use a constant LR and tune it last. Borrowed code often decays by epoch number (e.g. ImageNet decays at epoch 30) — on a smaller dataset this silently drives the LR to ~0 before the model converges.
