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CNN Model Architecture for CIFAR-10 Classification
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class CIFAR10CNN(nn.Module):
"""
Convolutional Neural Network for CIFAR-10 classification
"""
def __init__(self, num_classes=10):
super(CIFAR10CNN, self).__init__()
# Convolutional Layer 1
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
# Convolutional Layer 2
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
# Convolutional Layer 3
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
# Max Pooling
self.pool = nn.MaxPool2d(2, 2)
# Fully Connected Layers
# After three 2x2 pools, 32x32 image becomes 4x4
self.fc1 = nn.Linear(128 * 4 * 4, 512)
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(512, num_classes)
def forward(self, x):
# Layer 1: Conv -> BN -> ReLU -> Pool
x = self.pool(F.relu(self.bn1(self.conv1(x))))
# Layer 2: Conv -> BN -> ReLU -> Pool
x = self.pool(F.relu(self.bn2(self.conv2(x))))
# Layer 3: Conv -> BN -> ReLU -> Pool
x = self.pool(F.relu(self.bn3(self.conv3(x))))
# Flatten
x = x.view(-1, 128 * 4 * 4)
# FC Layers
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
def get_model(num_classes=10, device='cpu'):
model = CIFAR10CNN(num_classes=num_classes)
model = model.to(device)
return model
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
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