improving deep neural networks hyperparameter tuning regularization and optimization week 1 quiz answers
Quiz - Practical aspects of Deep Learning
1. If you have 10,000 examples, how would you split the train/dev/test set? Choose the best option.
- 33% train. 33% dev. 33% test
- 98% train. 1% dev. 1% test
- 60% train. 20% dev. 20% test
2. The dev and test set should:
- Be identical to each other (same (x,y) pairs)
- Come from the same distribution
- Have the same number of examples
- Come from different distributions
3. If your Neural Network model seems to have high variance, what of the following would be promising things to try?
- Get more training data
- Make the Neural Network deeper
- Get more test data
- Increase the number of units in each hidden layer
- Add regularization
4. Working on a model to classify bananas and oranges your classifier gets a training set error of 0.1% and a dev set error of 11%. Which of the following two are true?
- The model is overfitting the dev set.
- The model is overfitting the train set.
- The model has a high variance.
- The model has a very high bias.
5. What is weight decay?
- Gradual corruption of the weights in the neural network if it is trained on noisy data.
- A regularization technique (such as L2 regularization) that results in gradient descent shrinking the weights on every iteration.
- The process of gradually decreasing the learning rate during training.
- A technique to avoid vanishing gradient by imposing a celling on the values or the weights.
6. The regularization hyperparameter must be set to zero during testing to avoid getting random results. True/False?
- True
- False
7. Which of the following are true about dropout?
- In practice, it eliminates units of each layer with a probability of 1 – keep_prob.
- In practice, it eliminates units of each layer with a probability of keep_prob.
- It helps to reduce the bias of a model.
- It helps to reduce overfitting.
8. Decreasing the parameter keep_prob from (say) 0.6 to 0.4 will likely cause the following:
- Reducing the regularization effect.
- Increasing the regularization effect.
- Causing the neural network to have a higher variance.
9. Which of the following actions increase the regularization of a model? (Check all that apply)
- Increase the value of keep_prob in dropout.
- Decrease the value of the hyperparameter lambda.
- Normalizing the data.
- Increase the value of the hyperparameter lambda.
- Make use of data augmentation.
