Google Professional Machine Learning Engineer Question 224
Select 4Google Cloud PlatformYou are training a machine learning model on Google Cloud using TensorFlow, and you notice that the model's validation loss is fluctuating significantly while the training loss decreases steadily. What steps can you take to address this issue?
- A
Add regularization techniques such as L2 regularization or dropout to your model.
- B
Increase the model's complexity by adding more layers or neurons to capture the training data better.
- C
Enable early stopping based on validation loss to prevent overfitting.
- D
Reduce the learning rate to ensure smoother convergence during training.
- E
Increase the size of your training dataset by augmenting the data or collecting more samples.
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Fluctuating validation loss combined with decreasing training loss is often a sign of overfitting or instability during training. To address this, you can apply regularization techniques (e.g., L2 regularization, dropout), enable early stopping to halt training when validation performance degrades, reduce the learning rate to stabilize convergence, and increase the size of the training dataset to improve generalization. Avoid increasing model complexity, as it can exacerbate overfitting.
- A. Correct.
Adding regularization techniques such as L2 regularization or dropout can help prevent overfitting, which is a likely cause of fluctuating validation loss.
- B. Incorrect.
Increasing the model's complexity is likely to exacerbate overfitting, worsening the issue rather than addressing it.
- C. Correct.
Enabling early stopping based on validation loss helps prevent overfitting by stopping training when the validation loss begins to increase, indicating that the model's performance on unseen data is no longer improving.
- D. Correct.
Reducing the learning rate can lead to smoother convergence, helping the model better generalize and potentially reducing fluctuations in validation loss.
- E. Correct.
Increasing the size of your training dataset can improve generalization by providing the model with more diverse examples, thereby reducing the risk of overfitting.