基于神经网络思维的Logistic Regression优化函数成本列表长度不匹配错误

你实现的Logistic Regression梯度下降优化函数optimize触发了AssertionError，提示costs列表长度应为2但实际是1。

错误原因 #

核心问题是代码缩进错误：for循环体内部的关键逻辑（梯度提取、参数更新、cost收集）都被写到了循环外面，导致循环只重复执行了propagate计算，而参数更新和cost记录只在循环结束后执行一次，完全不符合梯度下降的迭代逻辑。

比如原代码中，dw、db的获取，参数更新，costs.append等代码都不在for循环的缩进块内，这些逻辑仅执行一次，而非每次迭代都执行，自然无法收集到预期数量的cost值。

修复后的代码 #

def optimize(w, b, X, Y, num_iterations=200, learning_rate=0.009, print_cost=False):
    """
    This function optimizes w and b by running a gradient descent algorithm

    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of shape (num_px * num_px * 3, number of examples)
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)
    num_iterations -- number of iterations of the optimization loop
    learning_rate -- learning rate of the gradient descent update rule
    print_cost -- True to print the loss every 100 steps
    
    Returns:
    params -- dictionary containing the weights w and bias b
    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function
    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.
    
    Tips:
    You basically need to write down two steps and iterate through them:
        1) Calculate the cost and the gradient for the current parameters. Use propagate().
        2) Update the parameters using gradient descent rule for w and b.
    """
    
    import copy
    w = copy.deepcopy(w)
    b = copy.deepcopy(b)
 
    costs = []
    
    for i in range(num_iterations):
        # Cost and gradient calculation 
        grads, cost = propagate(w, b, X, Y)
        
        # Retrieve derivatives from grads
        dw = grads["dw"]
        db = grads["db"]
        
        # Update rule
        w = w - (learning_rate * dw)
        b = b - (learning_rate * db)
        
        # Record the costs
        if (i % 100 == 0):
            costs.append(cost)
          
            # Print the cost every 100 training iterations
            if print_cost:
                print ("Cost after iteration %i: %f" %(i, cost))
    
    params = {"w": w,
              "b": b}
    
    grads = {"dw": dw,
             "db": db}
    
    return params, grads, costs

修复说明 #

1. 将dw、db的提取、参数更新代码、costs.append逻辑全部缩进，放入for循环的代码块内，确保每次迭代都执行梯度计算、参数更新和cost记录。
2. 当num_iterations=200时，i会在0和100时满足i%100==0，此时costs列表会添加两次cost，长度变为2，符合测试用例的要求。

内容的提问来源于stack exchange，提问作者Hassan Afif