该方法已过时，您需要使用Dataframes.value，而不是在输出中显示结果：（我的电脑有问题的sklearn库创建坏文件夹访问，但修复和tf.experiments所有设置需要更新）
示例：您可以使用Dataframe中的数据集或此Dataframe.value（）方法，如问题中所示，追加是一个过程，但它是必需的。

import tensorflow as tf
import tensorflow_io as tfio

import pandas as pd

import matplotlib.pyplot as plt

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
variables = pd.read_excel('F:\\temp\\Python\\excel\\Book 13 (2) (3).xlsx', index_col=None, header=[0])

list_label = [ ]
list_Image = [ ]
list_file_actual = [ ]
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]

for Index, Image, Label in variables.values:
    print( Label )
    list_label.append( Label )
    
    image = tf.io.read_file( Image )
    image = tf.io.decode_image(image)
    list_file_actual.append(image)
    image = tf.image.resize(image, [32,32], method='nearest')
    list_Image.append(image)

list_label = tf.cast( list_label, dtype=tf.int32 )
list_label = tf.constant( list_label, shape=( 54, 1, 1 ) )
list_Image = tf.cast( list_Image, dtype=tf.int32 )
list_Image = tf.constant( list_Image, shape=( 54, 1, 32, 32, 3 ) )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices(( list_Image, list_label ))
list_Image = tf.constant( list_Image, shape=( 54, 32, 32, 3) ).numpy()

print( "===========================================" )
print( "type of variables: " )
print( type(variables) )
print( variables )
print( "variables.values: " )
print( variables.values )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 32, 32, 3 )),
    tf.keras.layers.Normalization(mean=3., variance=2.),
    tf.keras.layers.Normalization(mean=4., variance=6.),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Reshape((256, 225)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(192, activation='relu'),
    tf.keras.layers.Dense(10),
])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class custom_callback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if( logs['accuracy'] >= 0.97 ):
            self.model.stop_training = True
    
custom_callback = custom_callback()

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
    learning_rate=0.000001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
    name='Nadam'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    reduction=tf.keras.losses.Reduction.AUTO,
    name='sparse_categorical_crossentropy'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'] )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=180, epochs=10000, callbacks=[custom_callback] )

plt.figure(figsize=(6, 6))
plt.title("Actors recognitions")
for i in range(36):
    img = tf.keras.preprocessing.image.array_to_img(
        list_Image[i],
        data_format=None,
        scale=True
    )
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    predictions = model.predict(img_array)
    score = tf.nn.softmax(predictions[0])
    plt.subplot(6, 6, i + 1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(list_file_actual[i])
    plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" +  str(list_label_actual[tf.math.argmax(score)]))
    
plt.show()

input('...')

输出：有效输出，方法正在运行

type of variables:
<class 'pandas.core.frame.DataFrame'>
    Index                                              Image  Label
0       1  F:\Pictures\actor -Kib\228972730_3779945970218...      0
1       2  F:\Pictures\actor -Kib\264502873_4668588048021...      0

variables.values:
...

 [65
  'F:\\Pictures\\actor-Ploy\\body_theguest_พลอยจบแล้วไปไหน_07-683x1024.jpg'
  1]
 [66 'F:\\Pictures\\actor-Ploy\\d35wA_5f.jpg' 1]
 [67 'F:\\Pictures\\actor-Ploy\\DiNXmgdVAAEluT3.jpg' 1]]

Epoch 46/10000
54/54 [==============================] - 3s 47ms/step - loss: 0.5079 - accuracy: 0.6852
Epoch 47/10000
54/54 [==============================] - 5s 93ms/step - loss: 0.5024 - accuracy: 0.7037