[NATURAL LANGUAGE PROCESSING IN TENSORFLOW] Sequence models and Literature

코세라의   deeplearning.AI tensorflow developer 전문가 자격증 과정내에 Natural Language Processing in TensorFlow

과정의 4주차 sequence models and literature챕터의 코드 예제입니다.

 

laurence to poetry

한 문학의 문장들을 학습시킨 모델을 이용해서, 첫 몇 단어를 seed로 모델에 주면, 이후 단어들을 예측해서 문장들을 자동으로 써나가는 코드이다.

 

import tensorflow as tf
 
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import numpy as np
 
!wget --no-check-certificate \
    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/irish-lyrics-eof.txt \
    -O /tmp/irish-lyrics-eof.txt
 
tokenizer = Tokenizer()
 
data = open('/tmp/irish-lyrics-eof.txt').read()
 
corpus = data.lower().split("\n")
 
tokenizer.fit_on_texts(corpus)
total_words = len(tokenizer.word_index) + 1
 
print(tokenizer.word_index)
print(total_words)
 
input_sequences = []
for line in corpus:
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i+1]
        input_sequences.append(n_gram_sequence)
 
# pad sequences 
max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))
 
# create predictors and label
xs, labels = input_sequences[:,:-1],input_sequences[:,-1]
 
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)
 
print(tokenizer.word_index['in'])
print(tokenizer.word_index['the'])
print(tokenizer.word_index['town'])
print(tokenizer.word_index['of'])
print(tokenizer.word_index['athy'])
print(tokenizer.word_index['one'])
print(tokenizer.word_index['jeremy'])
print(tokenizer.word_index['lanigan'])
 
print(xs[6])
 
print(ys[6])
 
print(xs[5])
print(ys[5])
 
print(tokenizer.word_index)
 
model = Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_len-1))
model.add(Bidirectional(LSTM(150)))
model.add(Dense(total_words, activation='softmax'))
adam = Adam(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
#earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto')
history = model.fit(xs, ys, epochs=100, verbose=1)
#print model.summary()
print(model)
 
import matplotlib.pyplot as plt
 
 
def plot_graphs(history, string):
  plt.plot(history.history[string])
  plt.xlabel("Epochs")
  plt.ylabel(string)
  plt.show()
 
plot_graphs(history, 'accuracy')
 
seed_text = "I've got a bad feeling about this"
next_words = 100
  
for _ in range(next_words):
    token_list = tokenizer.texts_to_sequences([seed_text])[0]
    token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
    predicted = model.predict_classes(token_list, verbose=0)
    output_word = ""
    for word, index in tokenizer.word_index.items():
        if index == predicted:
            output_word = word
            break
    seed_text += " " + output_word
print(seed_text)

 

 

generating text using a character-based RNN 예시 참고 :

www.tensorflow.org/tutorials/text/text_generation

순환 신경망을 활용한 문자열 생성  |  TensorFlow Core