【NLP自然语言处理】智能续话小程序

文章目录

• 代码
• 效果展示

代码

# -*-coding:utf-8-*-
import time

from keras_preprocessing.sequence import pad_sequences

start = time.time()
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import tensorflow.keras.layers as layers;

tokenizer = Tokenizer()

# 要学习的语句
data = "Please forgive me for falling in love with you.\n" \
       "Forgive me for loving you with all my heart.\n" \
       "Forgive me for never wanting to be apart.\n" \
       "Two star-crossed lovers in perfect harmony Just give me a chance and you will agree.\n" \
       "I was meant for you.\n" \
       "And you were meant for me."

print(data)

corpus = data.split("\n")
print("---------------------------------------------------")
print(corpus)

tokenizer.fit_on_texts(corpus)

total_words = len(tokenizer.word_index)+1

input_seq = []
for line in corpus:
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1,len(token_list)):
        n_gram_seq = token_list[:i+1]
        input_seq.append(n_gram_seq)

max_seq_len = max([len(x) for x in input_seq])

# 前置0填充
input_seq = np.array(pad_sequences(input_seq,maxlen=max_seq_len,padding='pre'))
print("---------------------------------------------------")
print(input_seq)

# 特征和标签的划分
xs = input_seq[:,:-1]
label = input_seq[:,-1]

# 将标签转化为one-hot独热编码
ys = tf.keras.utils.to_categorical(label,num_classes=total_words)

# 模型
model = keras.Sequential()
model.add(layers.Embedding(total_words,240,input_length=max_seq_len-1))
model.add(layers.Bidirectional(layers.LSTM(150)))
model.add(layers.Dense(total_words,activation='softmax'))
adam = keras.optimizers.Adam(lr=0.01)
model.compile(loss="categorical_crossentropy",optimizer=adam,metrics=['accuracy'])

# 开始训练
history = model.fit(xs,ys,epochs=50,verbose=1)

# 测试
test_text = "I want to"
next_words = 20

for _ in range(next_words):
    token_list = tokenizer.texts_to_sequences(([test_text]))[0]
    token_list = pad_sequences([token_list],maxlen=max_seq_len-1,padding='pre')
    predicted = model.predict_classes(token_list,verbose=0)
    # print(tokenizer.word_index.items())
    output_word = ""
    for word,index in tokenizer.word_index.items():
        if index == predicted:
            output_word = word
            test_text = (test_text + " " + output_word)
            break

    print(test_text)

print("共用时：",(time.time()-start),"秒")

效果展示

• I want to …
  I want to meant for you with all my heart 我想全心全意为你着想
• I hate you …
  I hate you were meant for me for never wanting to be apart 我恨你是我命中注定永远不想分开的
• I want to eat …
  I want to eat star crossed lovers in perfect harmony just give me a chance 我想要完美和谐吃着满天星的恋人给我一个机会