MLA-C01 Question 232
Select 3You are training a deep learning model using Amazon SageMaker and notice that the model's validation loss decreases initially but then starts to oscillate and does not converge. Which of the following actions should you take to address the convergence issue?
- A
Reduce the learning rate used by the optimizer.
- B
Increase the batch size during training.
- C
Switch to a different optimizer, such as Adam or RMSprop.
- D
Add regularization techniques such as dropouts or L2 regularization.
- E
Increase the complexity of the model by adding more layers.
Show answer and explanation
Correct answers: A, C, D
Explanation
When a model's validation loss oscillates and fails to converge, it often indicates issues with the learning rate, optimizer, or overfitting. Reducing the learning rate can provide more stable updates to the model weights. Switching to an optimizer like Adam or RMSprop can dynamically adjust the learning rate and improve stability. Adding regularization techniques helps manage overfitting and can stabilize training. Increasing the batch size or model complexity is unlikely to directly address the root cause of the convergence issue and might instead introduce new challenges.
- A. Correct.
Reducing the learning rate can help prevent oscillations and allow for a smoother convergence, especially if the current learning rate is too high.
- B. Incorrect.
Increasing the batch size may not directly address convergence issues and can sometimes lead to slower convergence due to less frequent updates to model weights.
- C. Correct.
Switching to an optimizer like Adam or RMSprop, which adapt the learning rate dynamically, can help stabilize training and improve convergence.
- D. Correct.
Regularization techniques such as dropout or L2 regularization can reduce overfitting and stabilize training, which can indirectly help with convergence issues.
- E. Incorrect.
Increasing the complexity of the model may exacerbate convergence issues, as the model might become harder to optimize and require more training data to avoid overfitting.