python-DatasetV1Adapter对象不可订阅

我想编程一个神经网络。首先，我有自己的数据集，希望使用嵌入层，因为我有文本数据。

我的数据集（功能和标签）如下所示：

这就是我到目前为止所做的：

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import pandas as pd
from tensorflow import keras
from tensorflow.python.keras import layers
from google.colab import drive
import tensorflow.compat.v1 as tf

tf.disable_v2_behavior()
tf.enable_eager_execution()

# Convert to a tensorflow dataset 

drive.mount('/content/drive')
training_df: pd.DataFrame = pd.read_csv("/content/drive/MyDrive/Data.csv")
features = ['user', 'query', 'weight','word']

training_df.head()
training_df.info()

training_dataset = (
    tf.data.Dataset.from_tensor_slices(
        (
            tf.cast(training_df['user'].values, tf.float32),
            tf.cast(training_df['query'].values, tf.string),
            tf.cast(training_df['weight'].values, tf.float32),
            tf.cast(training_df['word'].values, tf.string),
            tf.cast(training_df['label'].values, tf.float32),
        )
    )
)

vocab_size = 10000

# A dictionary mapping words to an integer index

word_index = training_dataset['query'].get_word_index()

# The first indices are reserved
word_index = {k: (v+3) for k, v in word_index.items()}
word_index[""] = 0
word_index[""] = 1
word_index[""] = 2  # unknown
word_index[""] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

maxlen = 500

train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                                                        value=word_index[""],
                                                        padding='post',
                                                        maxlen=maxlen)

test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                                                       value=word_index[""],
                                                       padding='post',
                                                       maxlen=maxlen)

embedding_dim = 16

model = keras.Sequential([
  layers.Embedding(vocab_size, embedding_dim, input_length=maxlen),
  layers.GlobalAveragePooling1D(),
  layers.Dense(16, activation='relu'),
  layers.Dense(1, activation='sigmoid')
])

model.summary()
model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

history = model.fit(
    train_data,
    train_labels,
    epochs=30,
    batch_size=512,
    validation_split=0.2)

但是，我收到了以下错误消息：

Traceback (most recent call last)
 in ()
     39 # A dictionary mapping words to an integer index
     40 
---> 41 word_index = training_dataset['query'].get_word_index()
     42 
     43 # The first indices are reserved

TypeError: 'DatasetV1Adapter' object is not subscriptable

我做错了什么？我该怎么做？