MLA-C01 Question 163
Select 3You are training a deep learning model on Amazon SageMaker using a large dataset. During the training process, you notice that the model's loss decreases significantly in the first few epochs, but plateaus afterward. Which of the following adjustments to the training parameters could help improve the model's performance?
- A
Increase the number of epochs to allow the model more time to learn.
- B
Reduce the batch size to allow for more granular updates to the model weights.
- C
Increase the learning rate to help the model converge faster.
- D
Decrease the learning rate to allow the model to refine its optimization in smaller steps.
Show answer and explanation
Correct answers: A, B, D
Explanation
When the model's loss plateaus after a few epochs, it may indicate that the model has reached a local minimum or that the learning rate is too large for further fine-tuning. Increasing the number of epochs allows the model more time to train. Reducing the batch size enables more frequent updates to the model weights, which can improve convergence. Decreasing the learning rate allows for finer adjustments during optimization, improving the chances of breaking through a plateau. However, increasing the learning rate at this point could destabilize the training process.
- A. Correct.
Increasing the number of epochs gives the model additional time to continue learning, which can be beneficial if the model hasn't fully converged yet.
- B. Correct.
Reducing the batch size allows for more frequent weight updates, which can help the model escape local minima or plateaus in the loss function.
- C. Incorrect.
Increasing the learning rate may cause the model to overshoot the optimal solution, especially if the loss has already plateaued.
- D. Correct.
Decreasing the learning rate can help the model refine its optimization by taking smaller steps, which is especially useful when the loss has plateaued.