plot_chainer_MLP.py 文件源码

def __call__(self, x, t):
        y = self.predictor(x)

        if self.loss == "euclidean":
            return F.mean_squared_error(y, t)

        elif self.loss == "sdtw":
            loss = 0
            for i in range(y.shape[0]):
                y_i = F.reshape(y[i], (-1,1))
                t_i = F.reshape(t[i], (-1,1))
                loss += SoftDTWLoss(self.gamma)(y_i, t_i)
            return loss

        else:
            raise ValueError("Unknown loss")