TypeError: tuple indices must be integers or slices, not str

Traceback (most recent call last):
  File "E:/python/pycharm/course2_week2/main.py", line 278, in <module>
    parameters = model(train_X, train_Y, layers_dims, beta=0.9,optimizer="momentum",is_plot=True)
  File "E:/python/pycharm/course2_week2/main.py", line 240, in model
    A3,cache = opt_utils.forward_propagation(minibatch_X,parameters)
  File "E:\python\pycharm\course2_week2\opt_utils.py", line 100, in forward_propagation
    W1 = parameters["W1"]
TypeError: tuple indices must be integers or slices, not str

以上报错是我在学习吴恩达深度学习时，自己打的代码出现的报错，解决方案就是将

def model(X,Y,layers_dims,optimizer,learning_rate = 0.0007,mini_batch_size = 64,beta = 0.9,beta1 = 0.9,beta2 = 0.999,epsilon = 1e-8,num_epoches = 10000,print_cost = True,is_plot = True):

    L = len(layers_dims)
    costs = []
    t = 0 #每学完一个mini_batch 就增加1
    seed = 10 # 随机种子

    parameters = opt_utils.initialize_parameters(layers_dims)

    if optimizer == 'gd':
        pass # 不使用任何优化器，直接使用梯度下降法
    elif optimizer == 'momentum':
        v = initialize_velocity(parameters) #使用动量
    elif optimizer == 'adam' :
        v,s = initialize_adam(parameters)#使用adam
    else :
        print("optimizer参数错误，程序退出")
        exit(1)

    #开始学习
    for i in range(num_epoches):

        seed = seed + 1 #定义随机minibatches，在每次遍历数据集之后增加种子以重新排列数据集，使每次数据的顺序都不同
        minibatches = random_mini_batches(X,Y,mini_batch_size,seed)

        for minibatch in minibatches:
            (minibatch_X,minibatch_Y) = minibatch

            A3,cache = opt_utils.forward_propagation(minibatch_X,parameters)

            cost = opt_utils.compute_cost(A3,minibatch_Y)

            grads = opt_utils.backward_propagation(minibatch_X,minibatch_Y,cache)

            if optimizer == 'gd':
                parameters = update_parameters_with_gd(parameters,grads,learning_rate)

            elif optimizer == 'momentum':
                parameters,v = update_parameters_with_momentun(parameters,grads,v,beta,learning_rate)

            elif optimizer == 'adam' :
                t += 1
                parameters,v,s = update_parameters_with_adam(parameters,grads,v,s,t,learning_rate,beta1,beta2,epsilon)

        if i % 100 == 0:
            costs.append(cost)

            if print_cost and i %1000 == 0:
                print("第" + str(i) + "次遍历整个数据集，当前误差值：" + str(cost))

    if is_plot:
        plt.plot(costs)
        plt.ylabel("cost")
        plt.xlabel("epochs(per 100)")
        plt.title("learning rate = " + str(learning_rate))
        plt.show()

    return parameters

中更新参数时候的选择momentum和adam时

 parameters = update_parameters_with_momentun(parameters,grads,v,beta,learning_rate)

parameters = update_parameters_with_adam(parameters,grads,v,s,t,learning_rate,beta1,beta2,epsilon)

修改为一下：（再引用函数的时候，参数写少了）

parameters,v = update_parameters_with_momentun(parameters,grads,v,beta,learning_rate)

parameters,v,s = update_parameters_with_adam(parameters,grads,v,s,t,learning_rate,beta1,beta2,epsilon)