在局部极小值处停止梯度下降的条件

Question

我有找到最小成本函数的代码，但是当我添加阈值以在局部最小值处停止梯度下降时，变得低于错误，有人可以帮忙。

  

cannot convert the series to <class 'float'>

Input
Math   CS  
92     98
56     68
88     81
70     80

代码：-

def gradient_descent(x,y):
iterations=50
n=len(x)
exponent = -9
m_curr=b_curr=0
learning_rate=0.01
for i in range(iterations):
    y_pred=m_curr*x+b_curr
    cost=(1/n)*sum([val**2 for val in (y-y_pred)])
    md=-(2/n)*sum(x*(y-y_pred))
    bd=-(2/n)*sum(y-y_pred)
    m_curr=m_curr-learning_rate*md
    b_curr=b_curr-learning_rate*bd
    math.isclose(x,y,rel_tol=math.exp(exponent),abs_tol=0.0)
    print("m {},b {},cost {},iteration {},learning_rate {}".format(m_curr,b_curr,cost,i,learning_rate)) x=df['math'] y=df['cs'] gradient_descent(x,y)