小编Res*_*1ve的帖子

我正在尝试对葡萄酒数据集实施多元线性回归。但是当我将 Pytorch 的结果与 Python 的临时代码进行比较时，损失并不相同。

我的暂存代码：

功能：

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)

主要代码：

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] …