[Chatbot] Chapter6 양방향 LSTM

양방향 LSTM

• RNN이나 LSTM은 일반 신경망과 다르게 시퀀스 또는 시계열 데이터 처리에 특화되어 은닉층에서 과거의 정보를 기억할 수 있음
• 순환 신경망 구조 특성상 데이터가 입력 순을오 처리되기 때문에 이전 시점의 정보만 활용할 수 밖에 없음 (단점)
• 문장이 길어질수록 성능이 저하됨
• 기존 LSTM 계층에 역방향으로 처리하는 LSTM 계층을 하나 더 추가해 양방향에서 문장의 패턴을 분석할 수 있도록 구성된 게 양방향 LSTM
• 양방향에서 처리하므로 시퀀스 길이가 길어져도 정보 손실 없이 처리 가능

Library Call

import numpy as np
from random import random
from tensorflow.keras.models import  Sequential, Model
from tensorflow.keras.layers import Input, Dense, LSTM, Bidirectional, TimeDistributed

Sequence

# 시퀀스 생성
def get_sequence(n_timesteps):
    # 0~1 사이 랜덤 시퀀스 생성
    X = np.array([random() for _ in range(n_timesteps)])

    # 클래스 분류 기준
    limit = n_timesteps / 4.0

    # 누적합 시퀀스에서 클래스 결정
    # 누적합 항목이 limit보다 작은 경우 0, 아닌 경우 1로 분류
    y = np.array([0 if x < limit else 1 for x in np.cumsum(X)])

    # LSTM 입력을 위해 3차원 텐서로 변경
    X = X.reshape(1, n_timesteps, 1)
    y = y.reshape(1, n_timesteps, 1)

    return X, y

# 하이퍼 파라미터
n_units = 20
n_timesteps = 4

Modeling

랜덤으로 시퀀스를 생성해 임의의 분류 기준에 맞는 클래스를 예측하는 양방향 LSTM 모델 예제

# 양방향 LSTM 모델 정의 - Functional Model
input = Input(shape=(n_timesteps, 1))
x = Bidirectional(LSTM(units=n_units, return_sequences=True))(input)
output = TimeDistributed(Dense(units=1, activation='sigmoid'))(x)

model = Model(inputs=input, outputs=output)
model.summary()

Model: "model_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_5 (InputLayer)        [(None, 4, 1)]            0         
                                                                 
 bidirectional_8 (Bidirectio  (None, 4, 40)            3520      
 nal)                                                            
                                                                 
 time_distributed_8 (TimeDis  (None, 4, 1)             41        
 tributed)                                                       
                                                                 
=================================================================
Total params: 3,561
Trainable params: 3,561
Non-trainable params: 0
_________________________________________________________________

# 양방향 LSTM 모델 정의 - Sequential Model
model = Sequential()
model.add(Input(shape=(n_timesteps, 1)))
model.add(Bidirectional(LSTM(units=n_units, return_sequences=True)))
model.add(TimeDistributed(Dense(units=1, activation='sigmoid')))

model.summary()

Model: "sequential_9"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 bidirectional_10 (Bidirecti  (None, 4, 40)            3520      
 onal)                                                           
                                                                 
 time_distributed_10 (TimeDi  (None, 4, 1)             41        
 stributed)                                                      
                                                                 
=================================================================
Total params: 3,561
Trainable params: 3,561
Non-trainable params: 0
_________________________________________________________________

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

# 모델 학습
# epoch마다 학습 데이터를 생성해서 학습
for epoch in range(1000):
    X, y = get_sequence(n_timesteps)
    
    model.fit(X, y, epochs=1, batch_size=1, verbose=2)

1/1 - 3s - loss: 0.6984 - accuracy: 0.0000e+00 - 3s/epoch - 3s/step
1/1 - 0s - loss: 0.7006 - accuracy: 0.0000e+00 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.7051 - accuracy: 0.0000e+00 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.7013 - accuracy: 0.2500 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.7038 - accuracy: 0.0000e+00 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.6930 - accuracy: 0.7500 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.6959 - accuracy: 0.2500 - 8ms/epoch - 8ms/step
1/1 - 0s - loss: 0.6946 - accuracy: 0.5000 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.6963 - accuracy: 0.2500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6952 - accuracy: 0.2500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6905 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6926 - accuracy: 0.2500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6914 - accuracy: 0.5000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6871 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.6801 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6847 - accuracy: 0.7500 - 7ms/epoch - 7ms/step
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1/1 - 0s - loss: 0.6688 - accuracy: 1.0000 - 5ms/epoch - 5ms/step
1/1 - 0s - loss: 0.6616 - accuracy: 1.0000 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.6692 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.6853 - accuracy: 0.5000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6704 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6632 - accuracy: 1.0000 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.6617 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6639 - accuracy: 1.0000 - 31ms/epoch - 31ms/step
1/1 - 0s - loss: 0.6481 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6575 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6786 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6519 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6628 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.7119 - accuracy: 0.2500 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.6495 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6540 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6475 - accuracy: 0.7500 - 27ms/epoch - 27ms/step
1/1 - 0s - loss: 0.6499 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.6376 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6199 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6276 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6311 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6265 - accuracy: 0.7500 - 30ms/epoch - 30ms/step
1/1 - 0s - loss: 0.6270 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6170 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.6523 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6107 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5973 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.6310 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5903 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.5843 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.5654 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5779 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6191 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5419 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6182 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5625 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5361 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.5039 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.4712 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5382 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.4136 - accuracy: 1.0000 - 8ms/epoch - 8ms/step
1/1 - 0s - loss: 0.6607 - accuracy: 0.5000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.5939 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.4086 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.5777 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.9532 - accuracy: 0.2500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.3417 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.3156 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.2957 - accuracy: 1.0000 - 7ms/epoch - 7ms/step
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1/1 - 0s - loss: 0.3864 - accuracy: 0.7500 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.5204 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
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1/1 - 0s - loss: 0.2658 - accuracy: 0.7500 - 5ms/epoch - 5ms/step
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1/1 - 0s - loss: 0.2022 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.7422 - accuracy: 0.7500 - 8ms/epoch - 8ms/step
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1/1 - 0s - loss: 0.1860 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0751 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1704 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.2394 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1409 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.4273 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0606 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1636 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.2528 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0697 - accuracy: 1.0000 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.2576 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.2888 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1300 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1762 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1393 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0974 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0754 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1893 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0977 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.4277 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1984 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0908 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.2031 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.6500 - accuracy: 0.5000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0825 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1145 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1111 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.7472 - accuracy: 0.5000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.4791 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1096 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1726 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1468 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.2281 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1502 - accuracy: 1.0000 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.1066 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1556 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0916 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0578 - accuracy: 1.0000 - 7ms/epoch - 7ms/step
1/1 - 0s - loss: 0.4093 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.3721 - accuracy: 0.7500 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.0691 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1712 - accuracy: 1.0000 - 6ms/epoch - 6ms/step
1/1 - 0s - loss: 0.1692 - accuracy: 1.0000 - 6ms/epoch - 6ms/step

# get_sequence 함수의 X 부분 예시
print(random())
print(np.array([random() for _ in range(5)]))

0.18470805796753775
[0.3674843  0.05786435 0.32903239 0.15887972 0.91681222]

Evaluate

# 모델 평가
X, y = get_sequence(n_timesteps)
yhat = model.predict(X, verbose=0)

for i in range(n_timesteps):
    if yhat[0, i] > 0.5:
        pred = 1
    else: 
        pred = 0
    print('실제값 : ', y[0, i], '예측값 : ', pred)

실제값 :  [0] 예측값 :  0
실제값 :  [0] 예측값 :  0
실제값 :  [1] 예측값 :  1
실제값 :  [1] 예측값 :  1