pure_pytorch.py

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyperparameters
num_epochs = 50
batch_size = 64
learning_rate = 0.001

# Data transformations
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

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

# Load CIFAR-10 dataset
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)

# Define the CNN architecture
class CIFAR10CNN(nn.Module):
    def __init__(self):
        super(CIFAR10CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = self.pool(torch.relu(self.conv3(x)))
        x = x.view(-1, 64 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the model, loss function, and optimizer
model = CIFAR10CNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5)

# TensorBoard setup
writer = SummaryWriter('runs/cifar10_cnn_experiment')

# Training loop
total_step = len(train_loader)
for epoch in range(num_epochs):
    model.train()
    train_loss = 0.0
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_loss += loss.item()

        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{total_step}], Loss: {loss.item():.4f}')

    # Calculate average training loss for the epoch
    avg_train_loss = train_loss / len(train_loader)
    writer.add_scalar('training loss', avg_train_loss, epoch)

    # Validation
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        val_loss = 0.0
        for images, labels in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels)
            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        accuracy = 100 * correct / total
        avg_val_loss = val_loss / len(test_loader)
        print(f'Validation Accuracy: {accuracy:.2f}%')
        writer.add_scalar('validation loss', avg_val_loss, epoch)
        writer.add_scalar('validation accuracy', accuracy, epoch)

    # Learning rate scheduling
    scheduler.step(avg_val_loss)

# Final test
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

writer.close()

# Save the model
torch.save(model.state_dict(), 'cifar10_cnn.pth')