Strategies to Reduce Overfitting
Question
You have trained a deep neural network model on Google Cloud.
The model has low loss on the training data, but is performing worse on the validation data.
You want the model to be resilient to overfitting.
Which strategy should you use when retraining the model?
Answers
Explanations
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A. B. C. D.D.
The scenario presented in this question is a common one in machine learning, where the model is performing well on the training data but not on the validation data. This is an indication that the model has overfit to the training data, and is not able to generalize well to unseen data. To address this problem, various techniques can be used.
Option A suggests applying a dropout parameter of 0.2 and decreasing the learning rate by a factor of 10. Dropout is a regularization technique that randomly drops out a fraction of the neurons during training, which reduces overfitting by preventing the network from relying too much on any particular set of features. Decreasing the learning rate can help the model converge to a better solution, as a high learning rate can cause the model to overshoot the optimal solution. This is a reasonable strategy to use, and may help the model become more resilient to overfitting.
Option B suggests applying an L2 regularization parameter of 0.4 and decreasing the learning rate by a factor of 10. L2 regularization adds a penalty term to the loss function that penalizes large weights in the network, which can help prevent overfitting. This is another reasonable strategy to use, and may help the model become more resilient to overfitting.
Option C suggests running a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters. Hyperparameter tuning is the process of searching for the best combination of hyperparameters (such as learning rate, regularization, etc.) for a given model. This is a more comprehensive approach to addressing the overfitting problem, as it can help identify the best hyperparameters for the specific model and dataset. This approach may require more computational resources and time, but can potentially yield better results than the previous options.
Option D suggests running a hyperparameter tuning job on AI Platform to optimize for the learning rate and increasing the number of neurons by a factor of 2. Increasing the number of neurons can make the model more complex, which can exacerbate overfitting. It is generally recommended to start with a simpler model and gradually increase its complexity if necessary. Optimizing for the learning rate can be helpful, but it is only one aspect of hyperparameter tuning and may not be sufficient to address the overfitting problem.
In summary, options A, B, and C are all reasonable strategies to use when retraining a model to make it more resilient to overfitting. Option D may not be the best approach as it involves increasing the model complexity, which can worsen the overfitting problem.
