MLS-C01 Question 191
Select 3You are training a deep learning model using TensorFlow on Amazon SageMaker for a binary classification task. During training, you observe that the model performs well on the training set but fails to generalize to the validation set, indicating overfitting. To mitigate this issue, you decide to use dropout. Which of the following correctly describes how dropout will help improve your model's generalization?
- A
Dropout randomly sets a fraction of input neurons to zero during each training step, forcing the model to rely on multiple independent features.
- B
Dropout decreases the number of layers in the model during training, reducing model complexity and preventing overfitting.
- C
Dropout introduces noise during training, which helps the model generalize better to unseen data.
- D
Dropout ensures that all neurons are equally active during training, improving data representation.
- E
Dropout is automatically disabled during inference, allowing the full network to be used for predictions.
Show answer and explanation
Correct answers: A, C, E
Explanation
Dropout is a regularization technique used to prevent overfitting in deep learning models. During training, it randomly deactivates a fraction of neurons, reducing reliance on specific neurons and encouraging the model to learn more generalized patterns. This stochastic behavior helps improve generalization to unseen data. During inference, dropout is disabled so the full network is used for predictions, ensuring optimal performance.
- A. Correct.
Correct: Dropout randomly sets a fraction of input neurons to zero during training, which helps prevent the model from over-relying on specific features and encourages it to learn more robust patterns.
- B. Incorrect.
Incorrect: Dropout does not decrease the number of layers in the model; it only affects individual neurons within layers during training, not the model architecture.
- C. Correct.
Correct: Dropout introduces stochastic behavior (noise) during training, which helps the model generalize better by being less sensitive to specific data points.
- D. Incorrect.
Incorrect: Dropout does not ensure equal activity across all neurons. Instead, it temporarily deactivates some neurons during training.
- E. Correct.
Correct: During inference, dropout is disabled so that the entire trained network is used to make predictions, ensuring maximum predictive power.