# Evidence: Losses and value_and_grad

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

## Evidence required

Keep handwritten loss/gradient arithmetic, checks at three weights, small/large-rate update results, auxiliary residuals, and mean-versus-sum batch-replication results. Explain the update-scale change.

- Command: `python3 phases/02-transforms/02-losses-and-value-and-grad/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