我的代码中有一个错误，当我尝试获取输出时，它给我的成本函数提供了奇怪的输出

Question

我是数据科学行业的新手，一直在从事我的第二个项目。我正在对线性回归实施梯度下降来优化我的成本模型，但我的 C 变量的输出似乎很奇怪。有人可以告诉我我的编码是否正确或者我做错了什么吗？下面是我的代码：

#Model Creation 
import torch 
import random from sklearn.preprocessing 
import StandardScaler

input1 = (MarginMerged.drop(['BTC', 'Date'], 1)) 
input2 = torch.tensor(inputs.astype(float).values) 
output = torch.tensor([MarginMerged['BTC']])

sc=StandardScaler() 
standardized_x = sc.fit_transform(input2) #Standardizing for better convergence of Gradients

w1 = torch.randn(1, requires_grad = True) #Randomizing weights and allowing tracking of gradients 
w2 = torch.randn(1, requires_grad = True) 
w3 = torch.randn(1, requires_grad = True) 
w4 = torch.randn(1, requires_grad = True) 
b = torch.randn(1, requires_grad = True)

def regression(x1, w1, x2, w2, x3, w3, x4, w4, b): #Simple regression 
    function y = x1 * w1 + x2 * w2 + x3 * w3 + x4 * w4 + b return y

#Evaluating Model and Cost Function 
yhat = regression(input2[:, 0], w1, input2[:, 1], w2, input2[:, 2], w3, input2[:, 3], w4, b)

def MSE(yhat, y): 
    sigma = torch.sum((yhat - y) **2) return sigma / len(y)

C = MSE(yhat, output)

Output: 
tensor([  -766136.2741,   -910269.9667,   -827688.1826,   -825344.9633,
          -886781.1021,   -939034.5842,   -999291.1625,   -996561.4835,
         -1355946.4095,  -1101955.2536,  -1142216.8116,  -1212749.9003,
         -1737812.1974,  -1621638.2128,  -1758945.7766,  -2119769.5569,
         -2590650.6940,  -3916453.0230,  -4122350.1070,  -9693393.5401,
        -13584547.6533, -17504209.3513, -24114284.5156, -22302542.7874,
        -19064275.4255, -35323125.1089, -86678884.6189, -66240221.4384,
        -41395859.9706, -34334016.6174, -18819275.6493, -16508314.3713,
         -9682844.7546, -14240738.2622, -11479996.4053,  -9299528.3805,
         -9841421.4224, -15140955.9555, -10965320.5364,  -6465157.0859,
         -6044804.7645,  -4984105.2754,  -7711268.3907, -11424855.3688,
        -12303663.6678, -14704509.5725,  -9483839.7203,  -7645405.3528,
         -5656487.9378,  -5047090.0958,  -3954263.1534,  -5806911.3040,
         -8229332.1597, -12360791.8777, -11092046.1287], dtype=torch.float64,

*Output should be positive due to implementing MSE*