def multi_class_correct(y_pre: Tensor,
y_true: Tensor,
threshold=0.5,
device='cpu') -> Tensor:
y_pre, y_true = y_pre.argmax(dim=1), y_true.argmax(dim=1)
same = torch.as_tensor(y_pre == y_true, dtype=torch.int).to(device)
return torch.sum(same)
def accuracy(outputs:Tensor, actual:Tensor, dim=-1)->Tensor:
correct = (actual == outputs.argmax(dim))
return 100 * correct.sum() // len(correct)
