MLS-C01 Question 189
Select 3You are training a deep learning model using Amazon SageMaker. During training, you observe that the model is overfitting on the training dataset, leading to poor generalization on the validation dataset. You decide to use dropout as a regularization technique. Which of the following statements about dropout are correct?
- A
Dropout randomly sets a fraction of the input units to zero during training, reducing the risk of overfitting.
- B
Dropout increases the model complexity, making it more prone to overfitting.
- C
The dropout rate represents the fraction of neurons to be dropped and is typically set between 0.2 and 0.5.
- D
Dropout is only applied during training and not during inference.
- E
Dropout ensures all neurons in the network are dropped at least once during training for equitable contribution.
Show answer and explanation
Correct answers: A, C, D
Explanation
Dropout is a widely used regularization technique in deep learning to prevent overfitting by randomly dropping neurons during training. This forces the network to learn more generalized features, as it cannot rely on specific neurons always being present. The dropout rate determines the fraction of neurons to drop, and dropout is only active during training, not inference.
- A. Correct.
Correct. Dropout randomly disables a fraction of neurons during training, forcing the model to learn more robust features and reducing overfitting.
- B. Incorrect.
Incorrect. Dropout reduces model complexity by preventing co-adaptation of neurons, thus lowering the risk of overfitting.
- C. Correct.
Correct. The dropout rate determines the fraction of neurons to drop. Values between 0.2 and 0.5 are common and effective in practice.
- D. Correct.
Correct. Dropout is only applied during training to introduce randomness. During inference, all neurons are used, and their outputs are scaled appropriately.
- E. Incorrect.
Incorrect. Dropout does not guarantee that all neurons will be dropped at least once during training. It randomly selects neurons to drop based on the defined dropout rate.