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import torch
import torch.nn as nn
import torch.nn.functional as F
class BetterCNN(nn.Module):
def __init__(self, noOfClasses=39):
super(BetterCNN, self).__init__()
# 32 Channels
# We use padding=1 to keep spatial size same before pooling
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
# 64 Channels
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
# 128 Channels
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
# 256 Channels
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(256)
# Pooling layer
self.pool = nn.MaxPool2d(2, 2)
# Adaptive Pooling
self.adaptive_pool = nn.AdaptiveAvgPool2d((4, 4))
# Classification Head
self.fc1 = nn.Linear(256 * 4 * 4, 1024)
self.dropout = nn.Dropout(0.5) # Dropout after Linear layer
self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Linear(512, noOfClasses)
def forward(self, x):
# Block 1
x = self.conv1(x)
x = self.bn1(x) # BatchNorm
x = F.relu(x)
x = self.pool(x)
# Block 2
x = self.pool(F.relu(self.bn2(self.conv2(x))))
# Block 3
x = self.pool(F.relu(self.bn3(self.conv3(x))))
# Block 4
x = self.pool(F.relu(self.bn4(self.conv4(x))))
# Adapt & Flatten
x = self.adaptive_pool(x)
x = torch.flatten(x, 1) # Flattens to (Batch, 4096)
# Dense Layers
x = F.relu(self.fc1(x))
x = self.dropout(x) # Regularization
x = F.relu(self.fc2(x))
x = self.fc3(x) # No activation needed here (handled by CrossEntropyLoss)
return x
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