在Tensorflow中训练CNN模型时，如何从目录中读取图像作为输入和输出？

import tensorflow as tf
import os

image_dir = 'path/to/image/directory'
label_dir = 'path/to/label/directory'

image_list = []
label_list = []

for filename in os.listdir(image_dir):
    if filename.endswith('.jpg'):  # 根据实际图像格式进行修改
        image_list.append(os.path.join(image_dir, filename))
        label_list.append(os.path.join(label_dir, filename.replace('.jpg', '.txt')))  # 根据实际标签格式进行修改

def parse_image(image_path, label_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3)  # 根据实际图像通道数进行修改
    image = tf.image.convert_image_dtype(image, tf.float32)
    
    label = tf.io.read_file(label_path)
    label = tf.strings.split(label, sep='\n')
    label = tf.strings.to_number(label, out_type=tf.float32)
    
    return image, label

dataset = tf.data.Dataset.from_tensor_slices((image_list, label_list))
dataset = dataset.map(parse_image)

def preprocess(image, label):
    # 进行图像预处理操作，如缩放、裁剪、归一化等
    
    return image, label

dataset = dataset.map(preprocess)
dataset = dataset.batch(batch_size)

model = tf.keras.models.Sequential([
    # 构建CNN模型结构
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(dataset, epochs=num_epochs)