3. Desing and implement a Convolutional Neural Network(CNN) for classification of image dataset.
✅ Install Required Packages
pip install torch torchvisionPROGRAM:
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
# ------------------------------
# 1. Load Dataset (MNIST)
# ------------------------------
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=64, shuffle=False)
# ------------------------------
# 2. Define CNN Model
# ------------------------------
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1) # 1→16
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1) # 16→32
self.fc1 = nn.Linear(32 * 7 * 7, 128)
self.fc2 = nn.Linear(128, 10) # 10 classes (digits 0–9)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x))) # [1,28,28] → [16,14,14]
x = self.pool(F.relu(self.conv2(x))) # [16,14,14] → [32,7,7]
x = x.view(-1, 32 * 7 * 7) # flatten
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# ------------------------------
# 3. Initialize Model
# ------------------------------
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# ------------------------------
# 4. Training
# ------------------------------
epochs = 5
for epoch in range(epochs):
running_loss = 0.0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch+1}/{epochs}, Loss: {running_loss/len(train_loader):.4f}")
# ------------------------------
# 5. Evaluation
# ------------------------------
correct, total = 0, 0
with torch.no_grad():
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"\nTest Accuracy: {100 * correct / total:.2f}%")OUTPUT:
Epoch 1/5, Loss: 0.2170
Epoch 2/5, Loss: 0.0638
Epoch 3/5, Loss: 0.0431
Epoch 4/5, Loss: 0.0334
Epoch 5/5, Loss: 0.0259
Test Accuracy: 98.86%