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)