tensorflow CNN 里出现编码问题需要怎么解决（utf-8）

import tensorflow as tf
import matplotlib.pyplot as plt
import pathlib
import random

all_image_path=list('C:/Users/admin/Desktop/onlypic/'+df['FileName'].astype(str)+'.png')
all_image_label=list(df['label'])
image_count = len(all_image_path)
def load_preprosess_image(image_path,image_label):
    #读取图片
    img_raw = tf.io.read_file(image_path)
    #img_raw=tf.gfile.FastGFile(image_path,'rb').read() 
    #解码图片
    img_tensor = tf.image.decode_jpeg(img_raw,channels=3)
    #统一图片大小
    img_tensor = tf.image.resize(img_tensor,[256,256])
    #转换数据类型
    img_tensor = tf.cast(img_tensor, tf.float32)
    #归一化
    img = img_tensor/255
    #将总标签列表中每个标签各自成列表
    label = tf.reshape(image_label,[1])
    return img,label
path_ds = tf.data.Dataset.from_tensor_slices((all_image_path,all_image_label))
dataset = path_ds.map(load_preprosess_image)
#划分训练集和测试集
#测试个数取整
test_count = int(image_count*0.2)
#训练个数
train_count = image_count - test_count
#训练数据集
train_dataset = dataset.skip(test_count)
#测试数据集
test_dataset = dataset.take(test_count)
#电脑性能好的话batch值可以大点
batch_size = 16
#训练数据集重复,乱序,规定batch值
train_dataset = train_dataset.repeat().shuffle(buffer_size=train_count).batch(batch_size)
#测试数据集不用过多处理
test_dataset = test_dataset.batch(batch_size)

covn_base = tf.keras.applications.VGG16(weights='imagenet',include_top=False)
model = tf.keras.Sequential()
model.add(covn_base)
#全局平均值化 降维
model.add(tf.keras.layers.GlobalAveragePooling2D())
model.add(tf.keras.layers.Dense(512,activation='relu'))
model.add(tf.keras.layers.Dense(1,activation='sigmoid'))

#训练模型
#预训练网络权重不被训练
covn_base.trainable = False
#没自定义训练,均默认
#配置优化器,损失函数,显示准确率
model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['acc'])
#步长
steps_per_epoch = train_count//batch_size
validation_steps = test_count//batch_size

#记录数据
history = model.fit(train_dataset,epochs=10,steps_per_epoch=steps_per_epoch,validation_data=test_dataset,validation_steps=validation_steps)

#调用参数,绘制图型
plt.subplot(211)
plt.plot(history.epoch,history.history['acc'])
plt.plot(history.epoch,history.history['val_acc'])
plt.subplot(212)
plt.plot(history.epoch,history.history.get('loss'))
plt.plot(history.epoch,history.history.get('val_loss'))
plt.show()

#测试准确率高的话,保存一下,以备后有
model.save('destinguish_Cat_Ddo.h5')

代码是来自https://blog.csdn.net/weixin_48395629/article/details/107948076

我用的图片集是一些png图片，但是总是会报错，用其他的模型也经常是这样，想知道这个大概是什么原因，需要如何解决呢？

UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-254-abce13a0b2bc> in <module>()
     18 
     19 #记录数据
---> 20 history = model.fit(train_dataset,epochs=10,steps_per_epoch=steps_per_epoch,validation_data=test_dataset,validation_steps=validation_steps)
     21 
     22 #调用参数,绘制图型

~\Anaconda31\lib\site-packages\tensorflow\python\keras\engine\training_v1.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    806         max_queue_size=max_queue_size,
    807         workers=workers,
--> 808         use_multiprocessing=use_multiprocessing)
    809 
    810   def evaluate(self,

~\Anaconda31\lib\site-packages\tensorflow\python\keras\engine\training_arrays_v1.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    662         validation_steps=validation_steps,
    663         validation_freq=validation_freq,
--> 664         steps_name='steps_per_epoch')
    665 
    666   def evaluate(self,

~\Anaconda31\lib\site-packages\tensorflow\python\keras\engine\training_arrays_v1.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)
    292           else:
    293             actual_inputs = ins()
--> 294           batch_outs = f(actual_inputs)
    295         except errors.OutOfRangeError:
    296           if is_dataset:

~\Anaconda31\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
   3955 
   3956     fetched = self._callable_fn(*array_vals,
-> 3957                                 run_metadata=self.run_metadata)
   3958     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   3959     output_structure = nest.pack_sequence_as(

~\Anaconda31\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
   1480         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1481                                                self._handle, args,
-> 1482                                                run_metadata_ptr)
   1483         if run_metadata:
   1484           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd5 in position 105: invalid continuation byte