Evaluate without learning

torch / nn / optim: build and train neural nets
datasets / transforms: download + preprocess Fashion-MNIST
sklearn metrics: precision/recall/F1 + confusion matrix

def validate(model, val_loader, criterion, device): model.eval() running_loss = 0.0 correct = 0 total = 0 with torch.no_grad(): for images, labels in val_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) loss = criterion(outputs, labels) running_loss += loss.item() _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() epoch_loss = running_loss / len(val_loader) epoch_acc = 100 * correct / total return epoch_loss, epoch_acc

Important: Validation uses torch.no_grad() so we don't store gradients.

Deep Learning Essentials – Session 2