Deep Learning · Quiz 2 · Jan 2024

If the subgoals in the goal description are swapped places then does the Goal Stack Planning algorithm find an optimal plan?

What are the possible outcomes if we don’t do bias correction in ADAM?

Consider the assertion and the corresponding reason, Assertion (A): The RMSProp Algorithm starts oscillating around the minima. Reason (R): RMSProp is sensitive to initial learning rate value.

Suppose you have a deep neural network trained on a dataset with a small amount of labeled examples. After applying L2 regularization during training with a fixed value of regularization parameter, the following observations are made: • The empirical error decreases significantly. • The true error remains high. • The variance of the error on different subsets of the training data is relatively low. • When tested on a diverse set of unseen data, the model’s performance is inconsistent. Which of the following statements provides a plausible explanation for this scenario?

What are the maximum and minimum possible values of the derivative of sigmoid function?

Consider a feed-forward neural network containing three inputs and one output. It contains 50 hidden layers, each containing two neurons. The activation function used in all the hidden layers is ReLU, the output layer uses the Sigmoid activation function for binary classification. The network is trained with a binary cross-entropy loss function. Assume all weights are initialized to 1 and all biases to 0. For an input of [-10, 5, -20], what will be the output of the neural network?

Suppose we have a neural network with two hidden layers, each containing two neurons. The network contains two inputs and one output. Weights: wij = 1 for all weights Biases: bi = 0 for all biases Input: x = [10, -1]. We consider the following two scenarios: **Scenario 1:** We use Maxout activation for the hidden layers with k = 2 linear functions. **Scenario 2:** We use ReLU activation with dropout with a dropout probability of 0.5 Which of the following is true concerning the outputs in each scenario?

Consider a neural network with one hidden layer containing two neurons. The input layer contains 2 neurons and the output layer contains one neuron. ReLU activation is used for the hidden layers and sigmoid is used for the output layer. Considering weights wij = 0.5 for all weights and biases bi = 0 for all biases. Assuming a dropout probability of 0.5, if an input vector x = [1, -2] is fed to this network, which of the following seems to be the expected output?

Which of the following statements are true w.r.t. AdaDelta Algorithm?

After training a neural network using vanilla gradient descent, you notice that it performs exceptionally well on the training set (99% accuracy) but poorly on the test set (50% accuracy). Which of the following techniques could be employed to address this overfitting issue and improve generalization performance?

Which of the following optimization algorithms encounter problem of oscillating around the minima?

Consider a network where sigmoid activation is used at all hidden layers. Which of the following will be true?

Consider an Input matrix X of shape 4×4 and the kernel (filter) of shape 3×3 with a bias b = 1. Assume, stride equals 1 and no padding.

Note: In all your calculations, **truncate** the result to 2 decimal points, that is, if the intermediate result is 2.34434, then take it as 2.34 for the next step. Based on the above data answer the given subquestions.

The feature map A is then passed through a max pooling layer with the kernel size of 2 × 2. The result is then passed as an input to the sigmoid (logistic) function.

What is the output value y? Enter your answer to two decimal places.

Consider an Input matrix X of shape 4×4 and the kernel (filter) of shape 3×3 with a bias b = 1. Assume, stride equals 1 and no padding.

Note: In all your calculations, **truncate** the result to 2 decimal points, that is, if the intermediate result is 2.34434, then take it as 2.34 for the next step. Based on the above data answer the given subquestions.

An MRI image of size 500 × 500 × 10 is feed as an input to a convolutional layer. Suppose that we want to produce an output of size 241 × 241 × 5 using a set of kernels. Suppose the kernel size is 20 × 20 × 10. Answer the given subquestions

What is the value of stride s?. Assume zero padding (p = 0)

An MRI image of size 500 × 500 × 10 is feed as an input to a convolutional layer. Suppose that we want to produce an output of size 241 × 241 × 5 using a set of kernels. Suppose the kernel size is 20 × 20 × 10. Answer the given subquestions

How many parameters (excluding bias) are there in the convolutional layer?