梯度下降在输出中返回 nan

Question

我有一个包含 3 个特征和 1 个目标变量的数据。我正在尝试使用梯度下降，然后最小化 RMSE

在尝试运行代码时，我将 nan 作为 cost/error 术语试了很多方法都搞不定

谁能告诉我我的计算哪里出了问题。这是代码： m = len(y)

# calculate gradient
def grad(theta):
    
    dJ = 1/m*np.sum((Xnorm.dot(theta)-ynorm.reshape(len(ynorm),1))*Xnorm,axis=0).reshape(-1,1)
    return dJ

def cost(theta):
    J = np.sum((Xnorm.dot(theta)-ynorm.reshape(len(ynorm),1))**2,axis=0)
    return J

def GD(theta0,learning_rate = 0.0005,epochs=500,TOL=1e-1):
    
    theta_history = [theta0]
    J_history = [cost(theta0)]
    print(J_history)
    
    thetanew = theta0*10000
#     print(f'epoch \t Cost(J) \t')
    for epoch in range(epochs):
        if epoch%100 == 0:
            print('epoch', epoch, 'cost',J_history[-1])
        dJ = grad(theta0)
        J = cost(theta0)
        
        thetanew = theta0 - learning_rate*dJ
        theta_history.append(thetanew)
        J_history.append(J)
        
        if np.sum((thetanew - theta0)**2) < TOL:
            print('Convergence achieved.')
            break
        theta0 = thetanew

    return thetanew,theta_history,J_history

即使是第一个 theta 值，它 returns nan

theta,theta_history,J_history = GD(theta0)

我的变量的形状

Answer 1

我们提出的唯一合理的解决方案是因为成本高..无法将此方法用于此解决方案。我们尝试使用不同的方法，如简单线性回归，它奏效了。

梯度下降在输出中返回 nan

Gradient Descent returning nan in output

python

numpy

gradient-descent