| import torch | |
| def evaluate_model(model, testloader): | |
| """ | |
| Evaluates the model on the test set. | |
| """ | |
| correct = 0 | |
| total = 0 | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| model.eval() | |
| with torch.no_grad(): | |
| for data in testloader: | |
| images, labels = data[0].to(device), data[1].to(device) | |
| outputs = model(images) | |
| _, predicted = torch.max(outputs.data, 1) | |
| total += labels.size(0) | |
| correct += (predicted == labels).sum().item() | |
| accuracy = correct / total | |
| return accuracy | |