有没有一种简单的方法可以通过内置函数来自动重新训练keras NLP模型？

import pandas as pd
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

# 加载和准备数据
data = pd.read_csv('data.csv')
texts = data['text'].tolist()
labels = data['label'].tolist()

# 文本预处理
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
data = pad_sequences(sequences)

# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)

# 模型定义
model = Sequential()
model.add(Embedding(len(word_index) + 1, 100, input_length=data.shape[1]))
model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))

# 模型训练
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=10)

# 模型评估
loss, accuracy = model.evaluate(x_test, y_test)
print('Test loss:', loss)
print('Test accuracy:', accuracy)

# 模型保存
model.save('nlp_model.h5')