我有非?；镜木€性回歸樣本。下面的實現（沒有正則化）class Learning:    def assume(self, weights, x):        return np.dot(x, np.transpose(weights))    def cost(self, weights, x, y, lam):        predict = self.assume(weights, x) \            .reshape(len(x), 1)        val = np.sum(np.square(predict - y), axis=0)        assert val is not None        assert val.shape == (1,)        return val[0] / 2 * len(x)    def grad(self, weights, x, y, lam):        predict = self.assume(weights, x)\            .reshape(len(x), 1)        val = np.sum(np.multiply(            x, (predict - y)), axis=0)        assert val is not None        assert val.shape == weights.shape        return val / len(x)我想檢查漸變，它是否有效，與scipy.optimize.learn = Learning()INPUTS = np.array([[1, 2],          [1, 3],          [1, 6]])OUTPUTS = np.array([[3], [5], [11]])WEIGHTS = np.array([1, 1])t_check_grad = scipy.optimize.check_grad(    learn.cost, learn.grad, WEIGHTS,INPUTS, OUTPUTS, 0)print(t_check_grad)# Output will be 73.2241602235811!!!我從頭到尾手動檢查了所有計算。它實際上是正確的實現。但是在輸出中我看到了非常大的差異！是什么原因？