how to interpret training state plot

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salah mahdi
salah mahdi il 18 Gen 2016
Modificato: TED MOSBY il 18 Nov 2024
Dear friends,
can anyone help me to interpret the training state plot

Risposte (1)

TED MOSBY
TED MOSBY il 15 Nov 2024
Modificato: TED MOSBY il 18 Nov 2024
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!

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