scipy.optimize 中 minimize()函数拟合损失函数的报错问题

问题遇到的现象和发生背景

有三个ndarray: X (100,3) y (100,1) theta(3,) theta是初始参数（[0，0，0]）
我的损失函数为

def Cost_Function(theta, X, y):
    return np.mean(-y * np.log(sigmoid(X @ theta)) - (1 - y) * np.log(1 - sigmoid(X @ theta)))

梯度函数

def gradient(theta, X, y):
    return (1 / len(X)) * X.T @ (sigmoid(X @ theta) - y)

我试图用scipy.optimize 的minimize函数来拟合求theta

res = opt.minimize(fun=Cost_Function, x0=theta, args=(X, y), method='Newton-CG', jac=gradient)

报错1:The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

改为x0=theta.all()后，报错2：matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1 is different from 3)