Consider the following code snippet for modifying an AlexNet architecture to adapt it for a custom classification task with 10 output classes. Fill in the blank portion with the most appropriate code snippet. import torch import torch.nn as nn from torchvision.models import alexnet class CustomAlexNet(nn.Module): def init(self, num_classes=10): super(CustomAlexNet, self).init() self.alexnet = alexnet(pretrained=True) # Blank portion def forward(self, x): x = self.alexnet.features(x) x = self.alexnet.avgpool(x) x = torch.flatten(x, 1) x = self.alexnet.classifier(x) return x model = CustomAlexNet() Which of the following code snippets correctly fills the blank portion to modify the AlexNet classifier while preserving the pretrained feature extraction layers?

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Deep Learning Practice End Term: 13 April 2025 (January 2025 term)

Question 1

+5 marksOne correct option

Consider the following code snippet for modifying an AlexNet architecture to adapt it for a custom classification task with 10 output classes. Fill in the blank portion with the most appropriate code snippet.

python

1import torch
2import torch.nn as nn
3from torchvision.models import alexnet
4
5class CustomAlexNet(nn.Module):
6    def __init__(self, num_classes=10):
7        super(CustomAlexNet, self).__init__()
8        self.alexnet = alexnet(pretrained=True)
9        # Blank portion
10    def forward(self, x):
11        x = self.alexnet.features(x)
12        x = self.alexnet.avgpool(x)
13        x = torch.flatten(x, 1)
14        x = self.alexnet.classifier(x)
15        return x
16
17model = CustomAlexNet()

Which of the following code snippets correctly fills the blank portion to modify the AlexNet classifier while preserving the pretrained feature extraction layers?

Question 2

+5 marksOne correct option

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?

A
(227, 227, 3)
B
(227, 227, 16)
C
(225, 225, 16)
D
(114, 114, 16)

Question 3

+5 marksOne correct option

A
0.25
B
0.29
C
0.45
D
0.21

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More on the Deep Learning Practice End Term 13 Apr 2025 paper

The IIT Madras BS Deep Learning Practice (Deep Learning Practice) End Term paper sat on 13 Apr 2025, in the January 2025 term: 20 questions for 100 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

Feature	Deep Learning Practice End Term 13 Apr 2025 at a glance
Term	January 2025 term
Subject	Deep Learning Practice
Course code	BSDA5013
Questions	20
Marks	100
Duration	180 min
MCQ	15
MSQ	5
Official paper	IIT M FOUNDATION AN EXAM QDF3 13 Apr 2025
Negative marking	No negative marking.
Updated	25 Sept 2026

Deep Learning Practice End Term: 13 April 2025 (January 2025 term)

Question 1

Question 2

Question 3

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