Debugging is isolation
When a model underperforms, the bug could be in data, features, the model, or the metric. Debugging narrows the suspect by controlled checks rather than random tweaking.
- Overfit a tiny batch: a healthy model should reach near zero loss on a handful of examples.
- Check input pipelines: visualize the exact tensors the model sees.
- Compare to a baseline to confirm the metric and split are sane.
Common silent failures
Many failures produce no error, just bad numbers. Shuffled labels, a frozen layer, or a constant output all train quietly.
- A loss that never moves hints at a dead gradient or wrong learning rate.
- A train loss far below validation hints at overfitting.
- Identical predictions for every input hint at a collapsed model.
The narrowing path
Each check rules out a layer of the system.
Key idea
Model debugging isolates faults with controlled checks like overfitting a tiny batch and inspecting the exact inputs, since most failures are silent and produce only bad numbers.