粒子群优化出现浮点数无法引用

Question

请问一下，这里出现浮点数无法引用，TypeError: 'float' object is not subscriptable

for i_episode in range(MAX_EPISODES):
    """初始化s"""
    random.seed(7)
    fit = -1e5  # 全局最佳适应值
    # 初始粒子适应度计算
    print("计算初始全局最优")
    for i in range(pN):
        for j in range(dim):
            V[i][j] = random.uniform(0, 1)
            if j == 1:
                X[i][j] = random.uniform(DOWN[j], UP[j])
            else:
                X[i][j] = round(random.randint(DOWN[j], UP[j]), 0)
        pbest[i] = X[i]
        le, pred, y_t = training_bus(X[i])
        tmp = function(pred, y_t, le)


#其中bus函数是
def training_bus(x):
    neurons = int(x[0])
    dropout = round(x[1], 6)
    batch_size = int(x[2])
    model = build_model(neurons, dropout)
    print('neurons:' + str(int(x[0])) + '  dropout:' + str(dropout) + '   batch_size:' + str(batch_size))
    model.fit(
        x_train,
        y_train,
        batch_size=batch_size,
        epochs=10,
        verbose=0)
    trainScore = model_score(model, x_train, y_train,)
    model_test_score(model, x_test, y_test)
    # model.save('neurons' + str(int(X[0])) + '_dropout' + str(dropout) + '_batch_size' + str(batch_size) + '.h5')

    finish = [int(X[0]), dropout, batch_size, round(trainScore, 6)]
    model_scores.append(finish)
    #writeOneCsv(finish, '模型参数效果比较.csv')
    # 训练完成后可直接加载模型
    # model_lstm = load_model('GRU_bus_' + str(X[0]) + '_' + str(X[1]) + '_' + str(X[2]) + '_' + '.h5')
    pred = model.predict(x_test)
    le = len(pred)
    y_t = y_test.reshape(-1, 1)
    # print(y_t.shape)
    return le, pred, y_t

TypeError: 'float' object is not subscriptable

尝试str ,还是不对

le, pred, y_t = str(training_bus(X[i]))

Answer 1

错误在

le, pred, y_t = training_bus(X[i])