Sure, here's the modified code with `tensorflow.keras` instead of `keras`:
```python
# import necessary libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Flatten, Input, concatenate
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Conv1D, MaxPooling1D
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# load train and test data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# data augmentation
datagen = ImageDataGenerator(
        rotation_range=30, 
        width_shift_range=0.2, 
        height_shift_range=0.2, 
        shear_range=0.2, 
        zoom_range=0.2, 
        horizontal_flip=True, 
        vertical_flip=True)

X_train = train_data['text'].values.reshape(-1, 1)
y_train = train_data['label'].values

# create a data generator for text data
text_datagen = datagen.flow(X_train, y_train, batch_size=32)

# prepare data for classification
X = train_data['text']
y = train_data['label']
X_test = test_data['text']

# convert labels to categorical values
y_cat = to_categorical(y)

# split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X, y_cat, test_size=0.2, random_state=42)

# define tokenizer
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(X_train)

# convert text to sequences
X_train_seq = tokenizer.texts_to_sequences(X_train)
X_val_seq = tokenizer.texts_to_sequences(X_val)
X_test_seq = tokenizer.texts_to_sequences(X_test)

# pad sequences
max_seq_length = 500
X_train_pad = pad_sequences(X_train_seq, maxlen=max_seq_length)
X_val_pad = pad_sequences(X_val_seq, maxlen=max_seq_length)
X_test_pad = pad_sequences(X_test_seq, maxlen=max_seq_length)

# define CNN model
def cnn_model(input_layer):
    embedding_layer = Embedding(5000, 100, input_length=max_seq_length)(input_layer)
    conv1 = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')(embedding_layer)
    pool1 = MaxPooling1D(pool_size=2)(conv1)
    conv2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(pool1)
    pool2 = MaxPooling1D(pool_size=2)(conv2)
    conv3 = Conv1D(filters=128, kernel_size=3, padding='same', activation='relu')(pool2)
    pool3 = MaxPooling1D(pool_size=2)(conv3)
    flatten = Flatten()(pool3)
    dense1 = Dense(64, activation='relu')(flatten)
    dropout = Dropout(0.5)(dense1)
    dense2 = Dense(8, activation='softmax')(dropout)
    return dense2

# define models for ensembling
model_definitions = [cnn_model]
# TODO: Add your other model definitions here.

# define inputs and outputs for the models
input_layer = Input(shape=(max_seq_length,))
model_outputs = []

# create a model for each ensemble model and add it to the model_outputs
```
keras` 대신 `tensorflow.keras`로 수정된 코드는 다음과 같습니다:
```python
# 필요한 라이브러리 가져오기
임포트 팬더로 pd
numpy를 np로 임포트
sklearn.model_selection에서 train_test_split을 가져옵니다.
sklearn.metrics에서 f1_score를 가져옵니다.
tensorflow.keras.preprocessing.text에서 토큰화기 가져 오기
tensorflow.keras.preprocessing.sequence에서 pad_sequences 가져오기
tensorflow.keras.utils에서 to_categorical import
tensorflow.keras.models에서 시퀀셜, 모델 임포트
tensorflow.keras.layers에서 임베딩, LSTM, 밀도, 드롭아웃, 평탄화, 입력, 컨케트네이트 가져오기
tensorflow.keras.callbacks에서 EarlyStopping을 가져옵니다.
tensorflow.keras.layers에서 Conv1D, MaxPooling1D를 가져옵니다.
tensorflow.keras.preprocessing.image에서 ImageDataGenerator를 가져옵니다.

# 훈련 및 테스트 데이터 로드
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# 데이터 증강
datagen = 이미지데이터제너레이터(
        rotation_range=30, 
        width_shift_range=0.2, 
        HEIGHT_SHIFT_RANGE=0.2, 
        shear_range=0.2, 
        zoom_range=0.2, 
        horizontal_flip=True, 
        vertical_flip=True)

X_train = train_data['text'].values.reshape(-1, 1)
y_train = train_data['label'].values

# 텍스트 데이터에 대한 데이터 생성기 생성
text_datagen = datagen.flow(X_train, y_train, batch_size=32)

# 분류를 위한 데이터 준비
X = train_data['text']
y = train_data['label']
X_test = test_data['text']

# 라벨을 범주형 값으로 변환
y_cat = to_categorical(y)

# 데이터를 훈련 및 검증 집합으로 분할
X_train, X_val, y_train, y_val = train_test_split(X, y_cat, test_size=0.2, random_state=42)

# 토큰화기 정의
토큰화기 = 토큰화기(num_words=5000)
tokenizer.fit_on_texts(X_train)

# 텍스트를 시퀀스로 변환
X_train_seq = 토큰라이저.텍스트_투_시퀀스(X_train)
X_val_seq = 토큰화기.텍스트_투_시퀀스(X_val)
X_test_seq = 토큰화기.텍스트_투_시퀀스(X_test)

# 패드 시퀀스
max_seq_length = 500
X_train_pad = pad_sequences(X_train_seq, maxlen=max_seq_length)
X_val_pad = pad_sequences(X_val_seq, maxlen=max_seq_length)
X_test_pad = pad_sequences(X_test_seq, maxlen=max_seq_length)

# CNN 모델 정의
def cnn_model(input_layer):
    embedding_layer = 임베딩(5000, 100, 입력_길이=max_seq_길이)(입력_레이어)
    conv1 = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')(embedding_layer)
    pool1 = MaxPooling1D(pool_size=2)(conv1)
    conv2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(pool1)
    pool2 = MaxPooling1D(pool_size=2)(conv2)
    conv3 = Conv1D(filters=128, kernel_size=3, padding='same', activation='relu')(pool2)
    pool3 = MaxPooling1D(pool_size=2)(conv3)
    평탄화 = 평탄화()(pool3)
    dense1 = Dense(64, 활성화='relu')(flatten)
    드롭아웃 = 드롭아웃(0.5)(밀도1)
    dense2 = 밀도(8, 활성화='softmax')(드롭아웃)
    반환 dense2

# 앙상블을 위한 모델 정의
model_definitions = [cnn_model]
# 할 일: 여기에 다른 모델 정의를 추가하세요.

# 모델에 대한 입력과 출력을 정의합니다.
input_layer = Input(shape=(max_seq_length,))
model_outputs = []

# 각 앙상블 모델에 대한 모델을 생성하고 모델_아웃풋에 추가합니다.
```