为什么数据集的SGD丢失与pytorch代码和用于线性回归的草稿python代码不匹配？

如何解决为什么数据集的SGD丢失与pytorch代码和用于线性回归的草稿python代码不匹配？

我正在尝试在wine数据集上实现多元线性回归。但是，当我将Pytorch的结果与Python的草稿代码进行比较时，损失并不相同。

我的便签代码：

功能：

obj.size -> 'size'

主要代码：

def yinfer(X,beta):
  return beta[0] + np.dot(X,beta[1:]) 

def cost(X,Y,beta):
  sum = 0
  m = len(Y)
  for i in range(m): 
    sum = sum + ( yinfer(X[i],beta) - Y[i])*(yinfer(X[i],beta) - Y[i])
  return  sum/(1.0*m)

我使用了与Pytorch代码相同的权重

我的Pytorch代码：

alpha = 0.005
b=[0,0.04086357,-0.02831656,0.09622949,-0.15162516,0.60188454,0.47528714,-0.6066466,-0.22995654,-0.58388734,0.20954669,-0.67851365]
beta = np.array(b)
print(beta)
iterations = 1000
arr_cost = np.zeros((iterations,2))
m = len(Y)
temp_beta = np.zeros(12)
for i in range(iterations):
  for k in range(m): 
        temp_beta[0] =  yinfer(X[k,:],beta) - Y[k]
        temp_beta[1:] = (yinfer(X[k,beta) - Y[k])*X[k,:]
        beta = beta - alpha*temp_beta/(1.0*m)    #(m*np.linalg.norm(temp_beta))
  arr_cost[i] = [i,cost(X,beta)]
  #print(cost(X,beta))
plt.scatter(arr_cost[0:iterations,0],arr_cost[0:iterations,1])

class LinearRegression(nn.Module):
  def __init__(self,n_input_features):
    super(LinearRegression,self).__init__()
    self.linear=nn.Linear(n_input_features,1)
    # self.linear.weight.data=b.view(1,-1)
    self.linear.bias.data.fill_(0.0)
    nn.init.xavier_uniform_(self.linear.weight)
    # nn.init.xavier_normal_(self.linear.bias)
  def forward(self,x):
    y_predicted=self.linear(x)
    return y_predicted
model=LinearRegression(11)

我的DataLoader：

criterion = nn.MSELoss()
num_epochs=1000
for epoch in range(num_epochs):
  for x,y in train_data:
    y_pred=model(x)
    loss=criterion(y,y_pred)
    # print(loss)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

有人可以告诉我为什么发生这种情况，或者我的代码中有任何错误吗？