1. Mu (μ) Graph
- Frequent oscillations in ‘μ’ could suggest that the optimization is struggling to find a stable path, possibly due to a complex loss landscape.
- A consistently high ‘μ’ might indicate that the model is having trouble converging and may require adjustments, such as a different initialization or learning rate.
2. Gradient Graph
- A steadily decreasing gradient magnitude is a good sign of convergence.
- Persistent large gradients or oscillations may require learning rate adjustments or gradient clipping to stabilize training.
3. Validation Checks Graph
- Decrease in Validation Loss: Indicates that the model is generalizing well to unseen data.
- Increase in Validation Loss: Could suggest overfitting, where the model performs well on training data but poorly on validation data.
- Plateau in Validation Metrics: May indicate that the model has reached its capacity with the current architecture and data.
Hope this helps!
