Intermediate Deep Learning with PyTorch
Michal Oleszak
Machine Learning Engineer
train_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(45),
transforms.RandomAutocontrast(),
transforms.ToTensor(),
transforms.Resize((128, 128))
])
criterion = nn.CrossEntropyLoss()
net = Net(num_classes=7) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001)
for epoch in range(10): for images, labels in dataloader_train: optimizer.zero_grad() outputs = net(images) loss = criterion(outputs, labels) loss.backward() optimizer.step()
Intermediate Deep Learning with PyTorch