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import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import tqdm
# Define transformations
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize images for ResNet
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# Load CIFAR-10 Dataset
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)
# Define Model (ResNet-18)
model = torchvision.models.resnet18(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, 10) # Adjust for 10 CIFAR-10 classes
# Define Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Train the Model
num_epochs = 5
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for images, labels in tqdm.tqdm(trainloader):
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}/{num_epochs}, Loss: {running_loss/len(trainloader)}")
# Save the Trained Model
torch.save(model.state_dict(), "model.pth")
print("Model training complete and saved as model.pth!")
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