how to interpret training state plot

Dear friends,

can anyone help me to interpret the training state plot

Answer 1

[TED MOSBY]

Hi @salah mahdi,

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!