loss function #152
Description
i am trying to understand the loss function :
def calc_loss(self, y_true, y_pred): """ 矩阵计算batch内的cos loss """ y_true = y_true[::2] norms = (y_pred ** 2).sum(axis=1, keepdims=True) ** 0.5 y_pred = y_pred / norms y_pred = torch.sum(y_pred[::2] * y_pred[1::2], dim=1) * 20 y_pred = y_pred[:, None] - y_pred[None, :] y_true = y_true[:, None] < y_true[None, :] y_true = y_true.float() y_pred = y_pred - (1 - y_true) * 1e12 y_pred = y_pred.view(-1) y_pred = torch.cat((torch.tensor([0]).float().to(self.device), y_pred), dim=0) return torch.logsumexp(y_pred, dim=0)
- why we are taking alternate values from true labels?
- why we are taking dot product between alternate ypred?
if possible can you share any link or documentation of paper for this. thanks