greeting

See merge request !1

# Conflicts:
#   Hello.py

# Evaluate the optimized ResNet on test data
correct, total = 0, 0
net.eval()
with torch.no_grad():
    for x, y in testloader:
        x, y = x.to(device), y.to(device)
        outputs = net(x)
        _, predicted = torch.max(outputs.data, 1)
        total += y.size(0)
        correct += (predicted == y).sum().item()

accuracy = correct / total
print(f'Accuracy after CMA-ES optimization: {accuracy:.4f}')