LSTM 순환 신경망 텐서플로로 구현하기

1. 데이터 호출

 

      

      

      

    
    

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from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import Dense

 

      

      

      

    
    

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import numpy as np
from tensorflow.keras.datasets import imdb

(x_train_all, y_train_all), (x_test, y_test) = imdb.load_data(skip_top=20, num_words=1000)

for i in range(len(x_train_all)):
    x_train_all[i] = [w for w in x_train_all[i] if w > 2]
    
x_train = x_train_all[random_index[:20000]]
y_train = y_train_all[random_index[:20000]]
x_val = x_train_all[random_index[20000:]]
y_val = y_train_all[random_index[20000:]]
maxlen=100  #타임 스텝의 길이가 100인 시퀀스 데이터를 만듭니다.
x_train_seq = sequence.pad_sequences(x_train, maxlen=maxlen)
x_val_seq = sequence.pad_sequences(x_val, maxlen=maxlen)

2. LSTM 순환 신경망 만들기

 

      

      

      

    
    

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model_lstm = Sequential()
model_lstm.add(Embedding(1000, 32))
model_lstm.add(LSTM(8))
model_lstm.add(Dense(1, activation='sigmoid'))
model_lstm.summary()

      

      

      

    
    

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Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding_2 (Embedding)     (None, None, 32)          32000     
                                                                 
 lstm_2 (LSTM)               (None, 8)                 1312      
                                                                 
 dense_1 (Dense)             (None, 1)                 9         
                                                                 
=================================================================
Total params: 33,321
Trainable params: 33,321
Non-trainable params: 0
_________________________________________________________________

3. 모델 훈련하기

 

      

      

      

    
    

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

history = model_lstm.fit(x_train_seq, y_train, epochs=10, batch_size=32, 
                         validation_data=(x_val_seq, y_val))

      

      

      

    
    

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Epoch 1/10
625/625 [==============================] - 6s 8ms/step - loss: 0.4474 - accuracy: 0.7947 - val_loss: 0.3754 - val_accuracy: 0.8362
Epoch 2/10
625/625 [==============================] - 5s 8ms/step - loss: 0.3344 - accuracy: 0.8604 - val_loss: 0.3557 - val_accuracy: 0.8408
Epoch 3/10
625/625 [==============================] - 5s 8ms/step - loss: 0.3116 - accuracy: 0.8686 - val_loss: 0.3571 - val_accuracy: 0.8416
Epoch 4/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2954 - accuracy: 0.8756 - val_loss: 0.3573 - val_accuracy: 0.8432
Epoch 5/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2809 - accuracy: 0.8821 - val_loss: 0.3708 - val_accuracy: 0.8410
Epoch 6/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2681 - accuracy: 0.8887 - val_loss: 0.3861 - val_accuracy: 0.8394
Epoch 7/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2563 - accuracy: 0.8921 - val_loss: 0.3869 - val_accuracy: 0.8372
Epoch 8/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2433 - accuracy: 0.8978 - val_loss: 0.4016 - val_accuracy: 0.8344
Epoch 9/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2325 - accuracy: 0.9032 - val_loss: 0.4087 - val_accuracy: 0.8316
Epoch 10/10
625/625 [==============================] - 5s 8ms/step - loss: 0.2252 - accuracy: 0.9071 - val_loss: 0.4177 - val_accuracy: 0.8336

4. 손실 그래프와 정확도 그래프 그리기

 

      

      

      

    
    

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import matplotlib.pyplot as plt
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.show()


plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.show()

5. 검증 세트 정확도 평가하기

      

      

      

    
    

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loss, accuracy = model_lstm.evaluate(x_val_seq, y_val, verbose=0)
print(accuracy)

      

      

      

    
    

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0.8335999846458435