tensorflow2.3 InvalidArgumentError: jpeg::Uncompress failed
[[{{node decode_image/DecodeImage}}]] [Op:IteratorGetNext]

Solution:
check whether the picture is damaged before training:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import os


num_skipped = 0
for folder_name in ("Fruit apples", "Fruit bananas", "Fruit oranges"):
    folder_path = os.path.join(".\data\image_data", folder_name)
    for fname in os.listdir(folder_path):

        fpath = os.path.join(folder_path, fname)

        try:
            fobj = open(fpath, mode="rb")
            is_jfif = tf.compat.as_bytes("JFIF") in fobj.peek(10)
            
        finally:
            fobj.close()

        if not is_jfif:
            num_skipped += 1
            # Delete corrupted image
            os.remove(fpath)

print("Deleted %d images" % num_skipped)

Delete the damaged picture and train again to solve the problem
if an error is prompted again, use:

# Determine if an image is corrupt from local
def is_valid_image(path):
    '''
    Check if the file is corrupt
    '''
    try:
        bValid = True
        fileObj = open(path, 'rb')  # Open in binary form
        buf = fileObj.read()
        if not buf.startswith(b'\xff\xd8'): # whether to start with \xff\xd8
            bValid = False
        elif buf[6:10] in (b'JFIF', b'Exif'): # ASCII code of "JFIF"
            if not buf.rstrip(b'\0\r\n').endswith(b'\xff\xd9'): # whether it ends with \xff\xd9
                bValid = False
        else:
            try:
                Image.open(fileObj).verify()
            except Exception as e:
                bValid = False
                print(e)
    except Exception as e:
        return False
    return bValid
  
 num_skipped = 0
for folder_name in ("fruit-apple", "fruit-banana", "fruit-orange"):
    #os.path.join() joins two or more pathname components
    folder_path = os.path.join(". \data\image_data", folder_name)
    # os.listdir(path) lists the subdirectories under this directory
    for fname in os.listdir(folder_path):
        fpath = os.path.join(folder_path, fname)
        flag1 = is_valid_image(fpath)
        if not flag1:
            print(flag1)
            print(fpath)#Print the path and name of the error file
 

Adjust the error file and train again to solve the problem.