# Evidence: Debug unstable learning

Copy this template into your own portfolio and fill it after running your modified exercise.

## Evidence required

Keep the analytic error recurrence, per-step weight/loss/gradient norm for rates 0.1 and 0.3, observed 3.24 loss multiplier, changed-scale comparison, clipping bound, and one repaired nonfinite-input case.

- Command: `python3 phases/05-networks/05-debug-unstable-learning/code/main.py`
- Working directory: repository root
- Python / JAX / NumPy versions: 
- Hardware and device: 
- My prediction before running: 
- My change to the exercise: 
- Exit code and meaningful output: 
- Why the result supports or contradicts my prediction: 
- Checkpoint result: 
- Artifact link or saved filename: 
- Portfolio status: saved in local workspace