我不明白为什么下面的代码会给出不同的结果。我正在打印预测数组的前3个组件来比较结果。my_features和feat有完全不同的结果，但它们应该是相同的，因为模型和数据都是相同的。加载和图像预处理中应该有什么问题，但我找不到。任何帮助将不胜感激。

import tensorflow as tf
import os
import numpy as np
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications import MobileNetV3Small
from tensorflow.keras.applications.imagenet_utils import preprocess_input

model= MobileNetV3Small(weights='imagenet', include_top=False, pooling='avg')

DatasetPath= "DB"
imagePathList= sorted(os.listdir(DatasetPath))
imagePathList= [os.path.join(DatasetPath, imagePath) for imagePath in imagePathList]

def read_image(filename):
    image_string = tf.io.read_file(filename)
    image = tf.image.decode_jpeg(image_string, channels=3)
    image = tf.image.convert_image_dtype(image, tf.float32)
    image = tf.image.resize(image, [224,224])
    image = tf.keras.applications.mobilenet_v3.preprocess_input(image)
    return image

ds_imagePathList= tf.data.Dataset.from_tensor_slices(imagePathList)
dataset = ds_imagePathList.map(read_image, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(32, drop_remainder=False)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
my_features = model.predict(dataset)

my_features[0][:3]

第二个片段

def loadProcessedImage(path):
    #img = image.load_img(path, target_size=model.input_shape[1:3])
    img = image.load_img(path, target_size= (224,224,3))
    imgP = image.img_to_array(img)
    imgP = np.expand_dims(imgP, axis=0)
    imgP = preprocess_input(imgP)
    return img, imgP

img, x = loadProcessedImage(imagePathList[0])
feat = model.predict(x)

feat = feat.flatten()
feat[:3]