python中的梯度下降函数 - 损失功能的错误或权重

Question

我正在使用梯度功能进行练习，但我仍然无法获得预期的结果。也就是说，我收到2条错误消息：

1. 损失功能的错误输出错误。检查您如何实现矩阵乘法。

2. 重量矩阵theta的错误值。检查您如何更新权重矩阵。

应用功能时（请参见下文），我注意到每次迭代时的成本都会下降，但仍然不会收敛到练习中所需的结果。我已经在公式上尝试了几种改编，但尚未解决。

# gradientDescent

def渐变渐变（x，y，theta，alpha，num_iters）：

Input:
    x: matrix of features which is (m,n+1)
    y: corresponding labels of the input matrix x, dimensions (m,1)
    theta: weight vector of dimension (n+1,1)
    alpha: learning rate
    num_iters: number of iterations you want to train your model for
Output:
    J: the final cost
    theta: your final weight vector
Hint: you might want to print the cost to make sure that it is going down.

### START CODE HERE ###
# get 'm', the number of rows in matrix x
m = len(x)

for i in range(0, num_iters):
    
    # get z, the dot product of x and theta
    # z = predictins
    z = np.dot(x, theta)
    h = sigmoid(z)
    loss = z - y
    
    # calculate the cost function
    J =  (-1/m) * np.sum(loss)
    print("Iteration %d | Cost: %f" % (i, J))#
            
    gradient = np.dot(x.T, loss)
    
    #update theta
    theta = theta - (1/m) * alpha * gradient
    
    
### END CODE HERE ###
J = float(J)
return J, theta

Answer 1

问题在于，我错误地应用了成本函数的公式和计算权重的公式：