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DLP · Convolution & pooling arithmetic

15 questions · 75 marks · 3 papers

Answer key

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Deep Learning Practice

Convolution & pooling arithmetic

Official Paper

Examination Instructions & Guidelines

Solve on notebook/paper first before checking solutions.
MCQ: Exactly 1 option is correct. Negative marking applies.
MSQ: One or more options correct. Select all to score.
NAT: Numerical response. No negative marking.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

End Term · 31 Aug 2025 (FN)End Term

Q1MCQ5 Marks·Choose one option
Asked 2 times Key ↓
Consider the task of applying a convolutional layer with a stride of 1 to an image of size 227 × 227 (Grayscale), while ensuring that the output feature map retains the same spatial dimensions. Which of the following code snippets correctly implements this using PyTorch?
1. A
2. B
3. C
4. D
Q2MCQ5 Marks·Choose one option
Asked 2 times Key ↓
1. AOutput size: 8 × 8; Effective receptive field: 5 × 5
2. BOutput size: 10 × 10; Effective receptive field: 5 × 5
3. COutput size: 10 × 10; Effective receptive field: 3 × 3
4. DOutput size: 12 × 12; Effective receptive field: 7 × 7
Q3MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid advantages of using Convolutional Neural Networks (CNNs) instead of fully connected Multilayer Perceptrons (MLPs) for image-based tasks?
1. ACNNs require significantly fewer parameters than MLPs for high-dimensional image inputs.
2. BCNNs are better at capturing spatial hierarchies due to weight sharing and local connectivity.
3. CCNNs can approximate any function with fewer layers than MLPs due to the universal approximation theorem.
4. DCNNs are translation invariant by design, making them robust to object position shifts in images.
5. ECNNs do not require any activation functions since convolutions are linear operators.
6. FCNNs are computationally faster than MLPs on image data due to sparse connections and weight sharing.
Q4NAT5 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q5NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:

End Term · 31 Aug 2025 (AN)End Term

Q6MCQ5 Marks·Choose one option
Asked 2 times Key ↓
Consider the task of applying a convolutional layer with a stride of 1 to an image of size 111 × 111 (Grayscale), while ensuring that the output feature map retains the same spatial dimensions. Which of the following code snippets correctly implements this using PyTorch?
1. A
2. B
3. C
4. D
Q7MCQ5 Marks·Choose one option
Asked 2 times Key ↓
1. AOutput size: 15 × 15; Effective receptive field: 5 × 5
2. BOutput size: 17 × 17; Effective receptive field: 5 × 5
3. COutput size: 13 × 13; Effective receptive field: 5 × 5
4. DOutput size: 15 × 15; Effective receptive field: 3 × 3
Q8MSQ5 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following are valid reasons why Convolutional Neural Networks (CNNs) are preferred over fully connected Multilayer Perceptrons (MLPs) for image processing tasks?
1. ACNNs reduce the number of parameters by using local connections and weight sharing.
2. BCNNs can process images of arbitrary size without any architectural changes.
3. CCNNs exploit the spatial structure of images, making them more efficient at recognizing patterns.
4. DCNNs perform better than MLPs because they always use deeper networks.
5. ECNNs maintain spatial hierarchies by stacking convolution and pooling layers.
6. FCNNs can generalize better to unseen data due to their built-in translational invariance.
Q9NAT5 Marks·Write the number
Asked 2 times Key ↓
Your answer:
Q10NAT5 Marks·Write the number
Asked 3 times Key ↓
Your answer:

End Term · 13 Apr 2025 (AN)End Term

Q11MCQ5 Marks·Choose one option
Asked 3 times Key ↓
An RGB image of size 227 × 227 × 3 is first converted to grayscale and then passed through a 2D Convolutional Neural Network (CNN) layer with the following parameters: – Kernel size: 3 × 3 – Stride: 1 – Padding: 1 – Number of filters: 16 What will be the shape of the output feature map?
1. A(227, 227, 3)
2. B(227, 227, 16)
3. C(225, 225, 16)
4. D(114, 114, 16)
Q12MCQ5 Marks·Choose one option
Key ↓
Consider a Convolutional Neural Network (CNN) where the feature map after the convolutional and pooling layers has a shape of (8, 8, 64). This feature map is flattened and connected to a fully connected layer with 256 output neurons. How many parameters (weights and biases) are in this fully connected layer?
1. A524, 544
2. B65, 536
3. C258, 048
4. D10, 48, 832
Q13MCQ5 Marks·Choose one option
Key ↓
The following PyTorch code implements a simple Convolutional Neural Network (CNN). However, some of the layers are incomplete. Select the correct option to complete the layers.
1. A
2. B
3. C
4. D
Q14MSQ5 Marks·Choose all correct options
Key ↓
Which of the following code snippets correctly implement Min Pooling in PyTorch?
1. A
2. B
3. C
4. D
Q15MSQ5 Marks·Choose all correct options
Key ↓
In deep convolutional neural networks, 1 x 1 convolutions play a significant role in optimizing computation and feature extraction. Which of the following statements correctly describe their role in modern CNN architectures?
1. A1 x 1 convolutions can be used to perform dimensionality reduction by reducing the number of channels before applying computationally expensive convolutions.
2. BThey introduce non-linearity into the network, even when used without activation functions.
3. C1 x 1 convolutions enable cross-channel interactions by linearly combining feature maps from different channels.
4. DIn ResNet architectures, 1 x 1 convolutions are used in bottleneck blocks to match the dimensions when applying skip connections.
5. EWhen applied after a max pooling layer, 1 x 1 convolutions help recover lost spatial information by increasing the resolution of the feature maps.

Official Key

Master Answer Key & Explanations

Official answer keys from IIT Madras BS past papers. Click any question to review.

Reveal Key

Q1D

Q2B

Q3A, B, D, F

Q43029908880

Q564

Q6A

Q7A

Q8A, C, E, F

Q9394264576

Q1025

Q11B

Q12D

Q13A

Q14A, B

Q15A, C, D

Questions and answer keys are reproduced from the official IIT Madras BS papers. Topics and repeated question types are BTechi’s own grouping. BTechi is not affiliated with IIT Madras.