CosineAnnealingLR in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - CosineAnnealingLR

Which metric matters for CosineAnnealingLR and WHY

CosineAnnealingLR is a learning rate scheduler. It changes the learning rate during training to help the model learn better. The key metric to watch is the training loss and validation loss. These show if the model is learning well and not stuck or jumping around.

Why loss? Because the scheduler affects how fast or slow the model updates its knowledge. A good scheduler helps the loss go down smoothly and reach a low value.

Confusion matrix or equivalent visualization

CosineAnnealingLR does not directly affect classification results or confusion matrices. Instead, we look at loss curves over epochs.

Epoch | Training Loss | Validation Loss | Learning Rate
------------------------------------------------------
  1   |     0.85     |      0.90      |    0.1
  2   |     0.70     |      0.75      |    0.095
  3   |     0.60     |      0.65      |    0.09
 ...  |     ...      |      ...       |    ...
 30   |     0.15     |      0.20      |    0.01
    

This table shows how the learning rate decreases following a cosine curve, helping the loss reduce steadily.

Precision vs Recall tradeoff (or equivalent)

CosineAnnealingLR affects training speed and stability, not precision or recall directly. But indirectly, a good learning rate schedule can help the model find a better balance between underfitting and overfitting.

If the learning rate is too high, the model jumps around and may not learn well (high loss, unstable training). If too low, training is slow and may get stuck (slow loss decrease).

CosineAnnealingLR smoothly lowers the learning rate, allowing the model to explore early and fine-tune later, improving final accuracy and generalization.

What "good" vs "bad" metric values look like for CosineAnnealingLR

Good:

• Training and validation loss decrease smoothly over epochs.
• Learning rate follows a cosine curve, starting higher and gradually lowering.
• Validation loss does not increase sharply (no overfitting).
• Final accuracy or other task metrics improve compared to constant learning rate.

Bad:

• Loss curves are noisy or jump up and down.
• Validation loss increases early, showing overfitting.
• Learning rate does not change or changes abruptly.
• Model accuracy is worse than with a fixed learning rate.

Metrics pitfalls

• Ignoring loss curves: Only looking at final accuracy can hide unstable training caused by poor learning rate scheduling.
• Overfitting signs: Validation loss rising while training loss falls means the model memorizes training data, not generalizing well.
• Data leakage: If validation data leaks into training, loss and accuracy look too good, hiding scheduler issues.
• Overfitting to scheduler: Using too short or too long cosine cycles can cause poor convergence.

Self-check question

Your model uses CosineAnnealingLR. Training loss decreases smoothly, but validation loss stays high and does not improve. Is the scheduler working well? Why or why not?

Answer: The scheduler helps training loss go down, but high validation loss means the model is overfitting or data issues exist. The scheduler alone is not enough; you may need regularization or better data.

Key Result

CosineAnnealingLR helps reduce training loss smoothly by adjusting learning rate, improving model convergence and generalization.