0import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms # Data augmentation and normalization for training transform_train = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train) trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2) # Define a simple CNN with dropout and batch normalization class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 32, 3, padding=1) self.bn1 = nn.BatchNorm2d(32) self.conv2 = nn.Conv2d(32, 64, 3, padding=1) self.bn2 = nn.BatchNorm2d(64) self.pool = nn.MaxPool2d(2, 2) self.dropout = nn.Dropout(0.25) self.fc1 = nn.Linear(64 * 8 * 8, 512) self.bn3 = nn.BatchNorm1d(512) self.dropout2 = nn.Dropout(0.5) self.fc2 = nn.Linear(512, 10) def forward(self, x): x = self.pool(torch.relu(self.bn1(self.conv1(x)))) x = self.pool(torch.relu(self.bn2(self.conv2(x)))) x = self.dropout(x) x = x.view(-1, 64 * 8 * 8) x = torch.relu(self.bn3(self.fc1(x))) x = self.dropout2(x) x = self.fc2(x) return x net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001, weight_decay=1e-4) # weight_decay adds L2 regularization # Training loop for epoch in range(10): # 10 epochs net.train() running_loss = 0.0 correct = 0 total = 0 for inputs, labels in trainloader: optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) _, predicted = outputs.max(1) total += labels.size(0) correct += predicted.eq(labels).sum().item() train_loss = running_loss / total train_acc = 100. * correct / total net.eval() val_loss = 0.0 val_correct = 0 val_total = 0 with torch.no_grad(): for inputs, labels in testloader: outputs = net(inputs) loss = criterion(outputs, labels) val_loss += loss.item() * inputs.size(0) _, predicted = outputs.max(1) val_total += labels.size(0) val_correct += predicted.eq(labels).sum().item() val_loss /= val_total val_acc = 100. * val_correct / val_total print(f'Epoch {epoch+1}: Train Loss={train_loss:.4f}, Train Acc={train_acc:.2f}%, Val Loss={val_loss:.4f}, Val Acc={val_acc:.2f}%')
Before: Training accuracy: 98%, Validation accuracy: 70%, Training loss: 0.05, Validation loss: 0.85
After: Training accuracy: 93%, Validation accuracy: 82%, Training loss: 0.18, Validation loss: 0.45
torchvision.datasets in PyTorch?from module import Class.from torchvision.datasets import CIFAR10 is correct. Other options have syntax errors or wrong order.from torchvision.datasets import CIFAR10 train_data = CIFAR10(root='./data', train=True, download=True) print(len(train_data))What error will occur if
download=True is omitted and data is not already downloaded?download=False (default) and data is missing, it raises an error because it won't download automatically.ToTensor() before normalization.Compose to chain ToTensor() and Normalize() in order.ToTensor() or apply transforms incorrectly.Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.