可以提供基于tensorflow的cnn-lstm模型训练代码吗

当然可以，以下是一个基于tensorflow的cnn-lstm模型训练代码示例：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM, Conv1D, MaxPooling1D, GlobalMaxPooling1D
from tensorflow.keras.callbacks import ModelCheckpoint

# Load and preprocess input data
# ...

# Define the model
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_shape)))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(100))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Define the checkpoint
filepath = "cnn_lstm_model.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

# Train the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, callbacks=callbacks_list)

如果你需要更加详细的代码实现，你可以在Google或GitHub上搜索cnn-lstm模型的实现代码，例如：https://github.com/ahmedbesbes/character-based-cnn-lstm-attention-ner 的实现。