This program loads CIFAR-10 images, trains a simple model for one batch, and checks accuracy on one batch of test images.
import torch
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader
import torchvision.models as models
import torch.nn as nn
import torch.optim as optim
# Load CIFAR-10 training and test data with transform to tensor
train_data = CIFAR10(root='./data', train=True, download=True, transform=ToTensor())
test_data = CIFAR10(root='./data', train=False, download=True, transform=ToTensor())
# Data loaders
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)
# Use a simple pretrained model (ResNet18) and adjust for 10 classes
model = models.resnet18(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, 10)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Train for 1 epoch (for demo)
model.train()
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
break # only 1 batch for quick demo
# Evaluate on test data
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
break # only 1 batch for quick demo
accuracy = 100 * correct / total
print(f'Accuracy on 1 batch of test images: {accuracy:.2f}%')
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