NPTEL Deep Learning Week 8 Assignment Answers 2025
1. Which of the following is false about CNN?
a. Output should be flattened before feeding it to a fully connected layer
b. There can be only 1 fully connected layer in CNN
c. We can use as many convolutional layers in CNN
d. None of the above
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2. The input image has been converted into a matrix of size 64 X 64 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. 5×5
b. 59×59
C. 64×64
d. 60×60
Answer :-
3. Filter size of 3×3 is convolved with matrix of size 4×4 (stride=1). What will be the size of output
matrix if valid padding is applied:
a. 4×4
b. 3×3
c. 2×2
d. 1×1
Answer :-
4. Let us consider a Convolutional Neural Network having three different convolutional layers in
its architecture as:
Layer-1: Filter Size – 3 X 3, Number of Filters – 10, Stride – 1, Padding – 0
Layer-2: Filter Size – 5 X 5, Number of Filters – 20, Stride – 2, Padding – 0
Layer-3: Filter Size – 5 X5, Number of Filters – 40, Stride – 2, Padding – 0
Layer 3 of the above network is followed by a fully connected layer. If we give a 3-D image
input of dimension 39 X 39 to the network, then which of the following is the input dimension of
the fully connected layer.
a. 1960
b. 2200
c. 4563
d. 13690
Answer :-
5. Suppose you have 40 convolutional kernel of size 3 x 3 with no padding and stride 1 in the first
layer of a convolutional neural network. You pass an input of dimension 1024x1024x3 through
this layer. What are the dimensions of the data which the next layer will receive?
a. 1020x1020x40|
b. 1022x1022x40
c. 1021x1021x40
d. 1022x1022x3
Answer :-
6. Consider a CNN model which aims at classifying an image as either a rose,or a marigold, or a lily or an orchid (consider the test image can have only 1 of the classes at a time) . The last (fully-
connected) layer of the CNN outputs a vector of logits, L, that is passed through a activation that transforms the logits into probabilities, P. These probabilities are the model predictions for each of the 4 classes. Fill in the blanks with the appropriate option. Fill in the blanks with the appropriate option.
a. Leaky ReLU
b. Tanh
c. ReLU
d. Softmax
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7. Suppose your input is a 300 by 300 color (RGB) image, and you use a convolutional layer with
100 filters that are each 5×5. How many parameters does this hidden layer have (without bias)
a. 2501
b. 2600
C. 7500
d. 7600
Answer :-
8. Which of the following activation functions can lead to vanishing gradients?
a. ReLU
b. Sigmoid
c. Leaky ReLU
d. None of the above
Answer :-
9. Statement 1: Residual networks can be a solution for vanishing gradient problem
Statement 2: Residual networks provide residual connections straight to earlier layers
Statement 3: Residual networks can never be a solution for vanishing gradient problem
Which of the following option is correct?
a. Statement 2 is correct
b. Statement 3 is correct
c. Both Statement 1 and Statement 2 are correct
d. Both Statement 2 and Statement 3 are correct
Answer :-
10. Input to SoftMax activation function is [0.5,0.5,1]. What will be the output?
a. [0.28.0.28.0.44]
b. [0.022,0.956, 0.022]
c. [0.045,0.910.0.045]
d. [0.42, 0.42,0.16]
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