Consider the following PyTorch code that defines a small 3-layer fully connected neural network import torch import torch.nn as nn class SmallNet(nn.Module): def init(self): super(SmallNet, self).init() self.fc1 = nn.Linear(4, 6) # input -> hidden1 self.fc2 = nn.Linear(6, 3) # hidden1 -> hidden2 self.fc3 = nn.Linear(3, 2) # hidden2 -> output def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x How many trainable parameters (weights + biases) does this network have in total?

Introduction to Deep Learning and Generative AI · Quiz 1 · 26 Oct 2025 · September 2025 term

Question 7: Consider the following PyTorch code that defines a small …

Question 7

+2 marksNumerical answer

Consider the following PyTorch code that defines a small 3-layer fully connected neural network

python

1import torch
2import torch.nn as nn
3
4class SmallNet(nn.Module):
5    def __init__(self):
6        super(SmallNet, self).__init__()
7        self.fc1 = nn.Linear(4, 6)   # input -> hidden1
8        self.fc2 = nn.Linear(6, 3)   # hidden1 -> hidden2
9        self.fc3 = nn.Linear(3, 2)   # hidden2 -> output
10    def forward(self, x):
11        x = torch.relu(self.fc1(x))
12        x = torch.relu(self.fc2(x))
13        x = self.fc3(x)
14        return x

How many trainable parameters (weights + biases) does this network have in total?

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Correct answer: 59

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Question 7 of 20 in the IIT Madras BS Introduction to Deep Learning and Generative AI (Deep Learning and GenAI) Quiz 1 paper sat on 26 Oct 2025, in the September 2025 term (IIT M DIPLOMA AN EXAM QDD2 26 Oct 2025). It carries 2 marks.

Question 7: Consider the following PyTorch code that defines a small …

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