how to interpret training state plot

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Dear friends,

can anyone help me to interpret the training state plot

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Answer 1

TED MOSBY 2024-11-15

编辑：TED MOSBY 2024-11-18

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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!