在循环中使用Keras时，如何防止内存使用量激增

import numpy as np
from keras.utils import Sequence

class DataGenerator(Sequence):
    def __init__(self, x_set, y_set, batch_size):
        self.x, self.y = x_set, y_set
        self.batch_size = batch_size

    def __len__(self):
        return int(np.ceil(len(self.x) / float(self.batch_size)))

    def __getitem__(self, idx):
        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]
        
        # 这里可以添加数据预处理步骤
        # batch_x = preprocess(batch_x)
        
        return batch_x, batch_y

# 示例数据
x_train = np.random.rand(1000, 28, 28, 1)
y_train = np.random.randint(0, 10, (1000, 1))

# 创建数据生成器
train_generator = DataGenerator(x_train, y_train, batch_size=32)

# 使用数据生成器训练模型
model.fit(train_generator, epochs=5)