import numpy as np
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import imdb

n_unique_words = 10000
maxlen = 200
batch_size = 128

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=n_unique_words)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen) ------ 데이터 길이가 같지 않을 때 일정한 길이로 맞추어 줍니다.
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
y_train = np.array(y_train) ------ y_train을 배열로 생성
y_test = np.array(y_test)

model = Sequential()
model.add(Embedding(n_unique_words, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64))) ------ LSTM에 양방향 RNN을 적용
model.add(Dropout(0.5)) ------ 50%만 모델에 반영
model.add(Dense(1, activation='sigmoid'))

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

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=4,
          validation_data=[x_test, y_test])