Google Professional Machine Learning Engineer: Verify Low Loss on Small Dataset to Debug Deep Learning Models

Learn how to debug your deep learning model by verifying it can obtain a low loss on a small subset of the dataset. This essential step helps identify issues before scaling up training.

Question

You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?

A. Verify that your model can obtain a low loss on a small subset of the dataset
B. Add handcrafted features to inject your domain knowledge into the model
C. Use the Vertex AI hyperparameter tuning service to identify a better learning rate
D. Use hardware accelerators and train your model for more epochs

Answer

A. Verify that your model can obtain a low loss on a small subset of the dataset

Explanation

When debugging a deep learning model that shows little change in training and validation losses during the initial few epochs on a large dataset, the first step should be to verify the model’s ability to achieve low loss on a small subset of the data.

This approach helps isolate potential issues:

1. Data quality: Ensure the small subset is representative and properly preprocessed.
2. Model architecture: Confirm the model can learn the underlying patterns in the data.
3. Optimization process: Verify the loss function, optimizer, and learning rate are appropriate.

By training on a small subset, you can quickly identify and address problems before investing resources in large-scale training. If the model struggles with the subset, investigate data quality, model design, and optimization settings.

Once the model achieves low loss on the subset, gradually increase the dataset size and monitor performance. This iterative process helps build confidence in the model’s ability to scale up and learn from the full dataset efficiently.

Google Professional Machine Learning Engineer certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Google Professional Machine Learning Engineer exam and earn Google Professional Machine Learning Engineer certification.