CNNs in PyTorch

Introduction

Convolutional Neural Networks (CNNs) are highly effective for image classification and recognition tasks. In this tutorial, you will learn how to build, train, and evaluate a CNN using PyTorch on the CIFAR-10 dataset.

1️⃣ Install and Import Dependencies

First, ensure PyTorch and torchvision are installed:

pip install torch torchvision

Now import the required libraries:

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

2️⃣ Prepare the Dataset

Using CIFAR-10 for colored image classification:

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

3️⃣ Define the CNN Model

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.fc1 = nn.Linear(64 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = x.view(-1, 64 * 8 * 8)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = SimpleCNN()

4️⃣ Define Loss Function and Optimizer

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

5️⃣ Training the Model

epochs = 10

for epoch in range(epochs):
    running_loss = 0.0
    for images, labels in trainloader:
        optimizer.zero_grad()
        outputs = net(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f"Epoch {epoch+1}, Loss: {running_loss/len(trainloader):.4f}")

print("Training completed!")

6️⃣ Evaluating the Model

correct = 0
total = 0

with torch.no_grad():
    for images, labels in testloader:
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Test Accuracy: {100 * correct / total:.2f}%")

Conclusion

✅ You have successfully built, trained, and evaluated a CNN using PyTorch.
✅ You now understand how to prepare datasets, define models, train, and assess performance for image classification tasks using CNNs.

What’s Next?

✅ Experiment with deeper architectures, adding dropout and batch normalization.
✅ Visualize feature maps to understand what your CNN is learning.
✅ Explore advanced CNN architectures like ResNet on superml.org.

Join the SuperML Community to share your CNN projects and receive peer feedback.

Happy Building with PyTorch! 🚀