Learning rate for fine-tuning in TensorFlow - Model Metrics & Evaluation

Metrics & Evaluation - Learning rate for fine-tuning

Which metric matters for learning rate fine-tuning and WHY

When adjusting the learning rate for fine-tuning a model, the key metrics to watch are training loss and validation loss. These show how well the model is learning and if it is improving on new data.

A good learning rate helps the loss go down steadily without jumping around or getting stuck. If the learning rate is too high, loss may bounce or get worse. If too low, learning is slow and may stop early.

Also, watch validation accuracy to see if the model is improving on unseen data, which means fine-tuning is effective.

Confusion matrix example during fine-tuning

      Actual \ Predicted | Positive | Negative
      -------------------|----------|---------
      Positive           |    85    |   15
      Negative           |    10    |   90

      Total samples = 200
      TP = 85, FP = 10, TN = 90, FN = 15
    

From this matrix, we can calculate precision and recall to understand model quality after fine-tuning.

Precision vs Recall tradeoff in fine-tuning

Fine-tuning with a proper learning rate balances precision and recall. For example:

• If learning rate is too high, model may overfit noise, increasing false positives (lower precision).
• If learning rate is too low, model may underfit, missing true positives (lower recall).

Choosing the right learning rate helps find a sweet spot where both precision and recall improve, making the model reliable.

Good vs Bad metric values for learning rate fine-tuning

Good: Validation loss steadily decreases, validation accuracy improves, precision and recall both above 80%.

Bad: Validation loss fluctuates or increases, accuracy plateaus or drops, precision or recall below 50%, indicating poor learning rate choice.

Common pitfalls when tuning learning rate

• Too high learning rate: Causes loss to jump or diverge, model fails to learn.
• Too low learning rate: Training is very slow, may get stuck in local minima.
• Ignoring validation metrics: Only watching training loss can hide overfitting.
• Not adjusting learning rate schedule: Fine-tuning often needs smaller learning rates than initial training.

Self-check question

Your fine-tuned model has 98% accuracy but only 12% recall on the positive class. Is it good for production? Why or why not?

Answer: No, it is not good. High accuracy can be misleading if the data is imbalanced. Low recall means the model misses most positive cases, which can be critical depending on the task (e.g., missing fraud or disease). You should adjust the learning rate or other settings to improve recall.

Key Result

Proper learning rate tuning balances training and validation loss to improve precision and recall for effective fine-tuning.