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| Layer | Frequency | Function | Update Rate | Scope | |-------|-----------|----------|--------------|-------| | f1 | Low | Global trend detection | Slow (e.g., every 100 iterations) | Whole system | | f3 | Medium | Regional adjustment | Medium (e.g., every 20 iterations) | Subsystem | | f5 | High | Local correction | Fast (e.g., every iteration) | Individual unit |
The objective is to maximize the final validation metric $M$ (e.g., F1-score). The transition involves applying the selected loss to update the main network weights $W$ via gradient descent.
| Layer | Frequency | Function | Update Rate | Scope | |-------|-----------|----------|--------------|-------| | f1 | Low | Global trend detection | Slow (e.g., every 100 iterations) | Whole system | | f3 | Medium | Regional adjustment | Medium (e.g., every 20 iterations) | Subsystem | | f5 | High | Local correction | Fast (e.g., every iteration) | Individual unit |
The objective is to maximize the final validation metric $M$ (e.g., F1-score). The transition involves applying the selected loss to update the main network weights $W$ via gradient descent.
