Topic 1 Question 157
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?
Verify that your model can obtain a low loss on a small subset of the dataset
Add handcrafted features to inject your domain knowledge into the model
Use the Vertex AI hyperparameter tuning service to identify a better learning rate
Use hardware accelerators and train your model for more epochs
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コメント(3)
- 正解だと思う選択肢: A
the first step to quickly debug the deep learning model is to verify that it can obtain a low loss on a small subset of the dataset (Option A). If the model fails to achieve good results on the smaller subset, further investigation is required to identify and address potential issues with the model.
👍 1PST212023/07/20 - 正解だと思う選択肢: A
I choose A
👍 1Mdso2023/07/31 - 正解だと思う選択肢: A
Verifying that your model can obtain a low loss on a small subset of the dataset is a good first step for debugging because it helps you determine if your model is capable of fitting the data and learning from it. If your model cannot fit a small subset of the data, it may indicate issues with the model architecture, initialization, or optimization algorithm. By starting with a small subset, you can identify and fix these issues more quickly, before moving on to larger-scale training and more complex debugging tasks.
👍 1tavva_prudhvi2023/11/15
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