您可以使用所需的特定数量的图像创建Tensorflow记录，并在训练模型之前解析它们。
下面是创建TF记录文件并将其解析为模型的示例代码。我假设图像文件名是你的标签，这些标签是整数，因为你的代码不清楚你使用的是什么标签。

import tensorflow as tf
import numpy as np
import random
import os

def image_feature(value):
    """
    Returns a bytes_list from a string / byte.
    """

    return tf.train.Feature(
        bytes_list=tf.train.BytesList(value=[tf.io.encode_jpeg(value).numpy()])
    )

def bytes_feature(value):
    """Returns a bytes_list from a string / byte."""
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def float_feature(value):
    """Returns a float_list from a float / double."""
    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))

def int64_feature(value):
    """Returns an int64_list from a bool / enum / int / uint."""
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def create_example(image:np.ndarray, label:int):
    feature = {
        "label" : int64_feature(label),
        "image" : image_feature(image),        
    }
    return tf.train.Example(features=tf.train.Features(feature=feature))

path_to_your_images = "/path/to/your/images"

#Here we sample 10000 random images from your image folder
images_path_list = random.sample(os.listdir(path_to_your_images), 10000)

with tf.io.TFRecordWriter("train.tfrec") as writer:
    for file_path in images_path_list:
        img = tf.io.read_file(os.path.join(path_to_your_images, file_path))
        label = int(file_path.strip(".jpg"))
        """ 
        Here we decode the image bytes first and encode it again using encode_jpeg(). 
        This encoding reduces the memory occupied by the TF records file. 
        You can also directly write the raw image bytes to TF records but that would make 
        the file size of TF records to be huge as it saves the actual bytes of the image
        """
        example = create_example(tf.image.decode_jpeg(img, channels=3), label) 
        writer.write(example.SerializeToString())

上面的代码将创建具有10000个图像的TF记录文件。现在您可以使用下面的代码来解析图像和标签，并创建数据集句柄来训练模型

def _parse_dataset(example_proto):
    _feature_description = {
        "image" : tf.io.FixedLenFeature([], tf.string),        
        "label" : tf.io.FixedLenFeature([], tf.int64)
    }
    
    example = tf.io.parse_single_example(example_proto, _feature_description)

    image = tf.io.decode_jpeg(example["image"])    
    
    image = tf.cast(image, tf.float32) / 255.0
    label = example["label"]

def create_dataset(tfrecord_paths, batch_size):
    raw_dataset = tf.data.TFRecordDataset(tfrecord_paths)
    dataset = raw_dataset.map(_parse_dataset)
   
    dataset = dataset.batch(batch_size)
    return dataset

train_dataset = create_dataset("/path/to/train.tfrec",8)

现在，您可以直接在model.fit(train_dataset)中使用train_dataset来训练模型。要从数据集中访问一个示例，可以执行train_dataset.take(1)。这将给予你两个值一个是你的形象形状(batch_size, height, width, channels)和其他是你的label

***注意：***您不仅限于您的图像和标签，您还可以通过feature_description字典将其他信息写入TF记录文件。例如，如果要为每个示例添加图像文件名、图像的宽度和高度，则可以按如下方式修改要素描述

feature = {
        "file_name" : bytes_feature(filename.encode("utf8")),
        "width" : int64_feature(width),
        "height" : int64_feature(height)
        "label" : int64_feature(label),
        "image" : image_feature(image),        
    }

您需要确保在解析数据集时使用相同的特征描述。
希望这能帮上忙。Cheers！！！

**编辑：**如果不使用TF记录，可以执行以下操作

num_images = 10000

path_to_your_images = "/path/to/your/images"

all_image_paths = [os.path.join(path_to_your_images, file) for file in os.listdir(path_to_your_images)]

np.random.shuffle(all_image_paths)

image_path_list = all_image_paths[:num_images]

def process_images(image_file_path):

    image = tf.io.read_file(image_file_path)
    image = tf.image.decode_jpeg(image, channels=3)
    image = tf.image.resize(image,(resize_height, resize_width))
    image = tf.cast(image, tf.float32) / 255.0
    return image

image_dataset = tf.data.Dataset.from_tensor_slices(image_path_list)

image_dataset = image_dataset.map(process_images)

labels = np.zeros(num_images)

label_dataset = tf.data.Dataset.from_tensor_slices(labels)

dataset = tf.data.Dataset.zip((image_dataset, label_dataset))

dataset = dataset.shuffle(num_images).batch(8)

现在您可以使用dataset来训练模型