Question 17: Consider the following CNN defined in PyTorch: Based on …

1import torch
2import torch.nn as nn
3import torch.nn.functional as F
4
5class SimpleCNN(nn.Module):
6    def __init__(self):
7        super(SimpleCNN, self).__init__()
8        # Conv Layer 1: in_channels=1 (grayscale), out_channels=8
9        self.conv1 = nn.Conv2d(in_channels=1, out_channels=8,
10        kernel_size=3, stride=1, padding=1)
11        # Pool Layer 1
12        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
13
14        # Conv Layer 2: in_channels=8, out_channels=16
15        self.conv2 = nn.Conv2d(in_channels=8, out_channels=16,
16        kernel_size=3, stride=1, padding=1)
17        # Pool Layer 2
18        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
19
20        # Fully Connected Layers
21        self.fc1 = nn.Linear(16 * 7 * 7, 128)
22        # Flattened from conv2 output
23        self.fc2 = nn.Linear(128, 10)  # Output for 10 classes
24
25    def forward(self, x):
26        x = self.pool1(F.relu(self.conv1(x)))   # Conv1 + Pool1
27        x = self.pool2(F.relu(self.conv2(x)))   # Conv2 + Pool2
28        x = x.view(-1, 16 * 7 * 7)              # Flatten
29        x = F.relu(self.fc1(x))                 # FC1
30        x = self.fc2(x)                         # FC2
31        return x
32
33# Example input
34model = SimpleCNN()
35print(model)