NCA-GENM Question 162
Select 4You are training a multimodal generative AI model for a video generation task. The model is underperforming due to slow convergence during training and suboptimal results in generated outputs. To optimize the model's performance, which actions should you take?
- A
Adjust the learning rate to find a balance between convergence speed and stability.
- B
Increase the batch size to improve gradient estimates and reduce noise during training.
- C
Increase the number of layers in the model to ensure better feature extraction.
- D
Use a learning rate scheduler to dynamically adjust the learning rate during training.
- E
Perform a grid search to optimize key hyperparameters such as dropout rate and optimizer type.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Optimizing the performance of AI models often involves tuning hyperparameters like learning rate, batch size, dropout rate, and optimizer type. Actions such as adjusting the learning rate, using a learning rate scheduler, increasing batch size, and performing a grid search are effective and targeted approaches. Adding more layers, on the other hand, is a model architecture change rather than a hyperparameter optimization technique.
- A. Correct.
Adjusting the learning rate is a fundamental step in optimizing model performance. A lower learning rate can improve stability, while a higher rate may speed up convergence. This is a critical hyperparameter to tune.
- B. Correct.
Increasing the batch size can help reduce noise in gradient estimates, leading to more stable and effective training. However, this may require sufficient computational resources.
- C. Incorrect.
While adding more layers might improve feature extraction in some cases, it can also overcomplicate the model and lead to overfitting or vanishing gradients. This is not a direct hyperparameter tuning action.
- D. Correct.
A learning rate scheduler adjusts the learning rate dynamically during training, often leading to improved convergence and better final performance. This is a widely used optimization technique.
- E. Correct.
Grid search systematically explores combinations of hyperparameters, like dropout rates and optimizer types, to find the best configuration for the model. This is a direct hyperparameter tuning method.