Deep Learning | Week 8

Course Name: Deep Learning

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q1. Which of the following functions can be used as an activation function in the output layer if we
wish to predict the probabilities of n classes such that the sum of p over all n equals to 1?
a. Softmax
b. RelU
c. Sigmoid
d. Tanh

Answer: a. Softmax

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Q2. The input image has been converted into a matrix of size 256 X 256 and a kernel/filter of size 5×5 with a stride of 1 and no padding. What will be the size of the convoluted matrix?
a. 252×252
b. 3×3
c 254×254
d. 256×256

Answer: a. 252×252

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q3. What will be the range of output if we apply ReLU non-linearity and then Sigmoid Nonlinearity subsequently after a convolution layer?
a. [1,1]
b. [0,1]
c. [0.5,1]
d. [1,-0.5]

Answer: c. [0.5,1]

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Q4. The figure below shows image of a face which is input to a convolutional neural net and the other three images shows different levels of features extracted from the network. Can you identify from the following options which one is correct?

a. Label 3: Low-level features, Label 2: High-level features, Label 1: Mid-level features
b. Label 1: Low-level features, Label 3: High-level features, Label 2: Mid-level features
c. Label 2: Low-level features, Label 1: High-level features, Label 3: Mid-level features
d. Label 3: Low-level features, Label 1: High-level features, Label 2: Mid-level features

Answer: b. Label 1: Low-level features, Label 3: High-level features, Label 2: Mid-level features

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q5. Suppose you have 8 convolutional kernel of size 5 x 5 with no padding and stride 1 in the first layer of a convolutional neural network. You pass an input of dimension 228 x 228 x 3 through athis layer. What are the dimensions of the data which the next layer will receive?
a. 224x224x3
b. 224x224x8
c. 226x226x8
d. 225x225x3

Answer: b. 224x224x8

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Q6. What is the mathematical form of the Leaky RelU layer?
a. f(x)=max(0,x)
b. f(x)=min(0,x)
c. f(x)=min(0, ax), where a is a small constant
d. f(x)=1(x<0)(ax)+1(x>=0)(x), where a is a small constant

Answer: d. f(x)=1(x<0)(ax)+1(x>=0)(x), where a is a small constant

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q7. The input image has been converted into a matrix of size 224 x 224 and convolved with a kernel/filter of size FxF with a stride of s and padding P to produce a feature map of dimension 222×222. Which among the following is true?
a. F=3×3,s=1,P=1
b. F=3×3,s=0, P=1
c. F=3×3,s=1,P=0
d. F=2×2,s=0, P=0

Answer: c. F=3×3,s=1,P=0

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q8. Statement 1: For a transfer learning task, lower layers are more generally transferred to another task
Statement 2: For a transfer learning task, last few layers are more generally transferred to another task
Which of the following option is correct?
a. Statement 1 is correct and Statement 2 is incorrect
b. Statement 1 is incorrect and Statement 2 is correct
c. Both Statement 1 and Statement 2 are correct
d. Both Statement 1 and Statement 2 are incorrect

Answer: a. Statement 1 is correct and Statement 2 is incorrect

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q9. Statement 1: Adding more hidden layers will solve the vanishing gradient problem for a 2-layer neural network
Statement 2: Making the network deeper will increase the chance of vanishing gradients.
a. Statement 1 is correct
b. Statement 2 is correct
c. Neither Statement 1 nor Statement 2 is correct
d. Vanishing gradient problem is independent of number of hidden layers of the neural network.

Answer: b. Statement 2 is correct

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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Q10. How many convolution layers are there in a LeNet-5 architecture?
a. 2
b. 3
c 4
d. 5

Answer: a. 2

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These are NPTEL Deep Learning Week 8 Assignment 8 Answers

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