Always split your data:
- Training set (~-%): Used to update model weights
- Validation set (~-%): Used to monitor overfitting
Never evaluate on training data. That tells you nothing about generalization. The validation set catches overfitting early.
Shuffle before splitting to ensure both sets have similar distributions.