NLP（二十二）使用LSTM进行语言建模以预测最优词

• 预处理
  数据集使用Facebook上的BABI数据集
  将文件提取成可训练的数据集，包括：文章 问题 答案

def get_data(infile):
    stories,questions,answers = [],[],[]
    story_text = []
    fin = open(infile,'rb')
    for line in fin:
        line = line.decode('utf-8').strip()
        lno,text = line.split(' ',1)
        if '\t' in text:
            question,answer,_ = text.split('\t')
            stories.append(story_text)
            questions.append(question)
            answers.append(answer)
            story_text = []
        else:
            story_text.append(text)
    fin.close()
    return stories,questions,answers

data_train = get_data('qa1_single-supporting-fact_train.txt')
data_test = get_data('qa1_single-supporting-fact_test.txt')
print('\nTrain observations:',len(data_train[0]),
      'Test observations:',len(data_test[0]),'\n')

输出：

Train observations: 10000 Test observations: 1000

• 如何实现
  1.预处理：创建字典并将文章，问题和答案映射到词表，进一步映射成向量形式
  2.模型创建和验证：训练模型并在验证数据集上测试
  3.预测结果：测试集测试数据的结果
• 代码

from __future__ import division,print_function
import collections
import itertools
import nltk
import numpy as np
import matplotlib.pyplot as plt
import os
import random

def get_data(infile):
    stories,questions,answers = [],[],[]
    story_text = []
    fin = open(infile,'rb')
    for line in fin:
        line = line.decode('utf-8').strip()
        lno,text = line.split(' ',1) # 去掉前面的数字标记
        if '\t' in text: # 有制表符的是 问题 和 答案
            question,answer,_ = text.split('\t')
            stories.append(story_text)
            questions.append(question)
            answers.append(answer)
            story_text = []
        else: # 没制表符的是文章
            story_text.append(text)
    fin.close()
    return stories,questions,answers

data_train = get_data('qa1_single-supporting-fact_train.txt')
data_test = get_data('qa1_single-supporting-fact_test.txt')
print('\nTrain observations:',len(data_train[0]),
      'Test observations:',len(data_test[0]),'\n')
print(data_train[0][1],data_train[1][1],data_train[2][1])
# ['Daniel went back to the hallway.', 'Sandra moved to the garden.'] Where is Daniel?  hallway
print(np.array(data_train).shape)
# (3, 10000)

dictnry = collections.Counter() # 返回列表元素出现次数的 字典，这里没有参数是一个空字典
for stories,questions,answers in [data_train,data_test]:
    for story in stories:
        for sent in story:
            for word in nltk.word_tokenize(sent):
                dictnry[word.lower()] += 1
    for question in questions:
        for word in nltk.word_tokenize(question):
            dictnry[word.lower()] += 1
    for answer in answers:
        for word in nltk.word_tokenize(answer):
            dictnry[word.lower()] += 1

word2indx = {w:(i+1) for i,(w,_) in enumerate(dictnry.most_common())} # 按词频排序
word2indx['PAD'] = 0
indx2word = {v:k for k,v in word2indx.items()}

vocab_size = len(word2indx) # 一共有22个不重复单词
print('vocabulary size:',len(word2indx))

story_maxlen = 0
question_maxlen = 0
for stories,questions,answers in [data_train,data_test]:
    for story in stories:
        story_len = 0
        for sent in story:
            swords = nltk.word_tokenize(sent)
            story_len += len(swords)
        if story_len > story_maxlen:
            story_maxlen = story_len
    for question in questions:
        question_len = len(nltk.word_tokenize(question))
        if question_len > question_maxlen:
            question_maxlen = question_len
print('Story maximum length:',story_maxlen,'Question maximum length:',question_maxlen)
# 文章单词最大长度为14，问题中的单词最大长度为4，长度不够的补0，维度相同便于并向计算

from keras.layers import Input
from keras.layers.core import Activation,Dense,Dropout,Permute
from keras.layers.embeddings import Embedding
from keras.layers.merge import add,concatenate,dot
from keras.layers.recurrent import LSTM
from keras.models import Model
from keras.preprocessing.sequence import pad_sequences
from keras.utils import np_utils

def data_vectorization(data,word2indx,story_maxlen,question_maxlen): # 词 => 词向量
    Xs,Xq,Y = [],[],[]
    stories,questions,answers = data
    for story,question,answer in zip(stories,questions,answers):
        xs = [[word2indx[w.lower()] for w in nltk.word_tokenize(s)] for s in story] #
        xs = list(itertools.chain.from_iterable(xs))
        xq = [word2indx[w.lower()] for w in nltk.word_tokenize(question)]
        Xs.append(xs)
        Xq.append(xq)
        Y.append(word2indx[answer.lower()])
    return pad_sequences(Xs,maxlen=story_maxlen),pad_sequences(Xq,maxlen=question_maxlen),\
           np_utils.to_categorical(Y,num_classes=len(word2indx))

Xstrain,Xqtrain,Ytrain = data_vectorization(data_train,word2indx,story_maxlen,question_maxlen)
Xstest,Xqtest,Ytest = data_vectorization(data_test,word2indx,story_maxlen,question_maxlen)
print('Train story',Xstrain.shape,'Train question',Xqtrain.shape,'Train answer',Ytrain.shape)
print('Test story',Xstest.shape,'Test question',Xqtest.shape,'Test answer',Ytest.shape)

# 超参数
EMBEDDING_SIZE = 128
LATENT_SIZE = 64
BATCH_SIZE = 64
NUM_EPOCHS = 40

# 输入层
story_input = Input(shape=(story_maxlen,))
question_input = Input(shape=(question_maxlen,))

# Story encoder embedding
# 将正整数（索引）转换为固定大小的密集向量。
# 例如，[[4]，[20]]->[[0.25，0.1]，[0.6，-0.2]] 此层只能用作模型中的第一层
story_encoder = Embedding(input_dim=vocab_size,output_dim=EMBEDDING_SIZE,input_length=story_maxlen)(story_input)
story_encoder = Dropout(0.2)(story_encoder)

# Question encoder embedding
question_encoder = Embedding(input_dim=vocab_size,output_dim=EMBEDDING_SIZE,input_length=question_maxlen)(question_input)
question_encoder = Dropout(0.3)(question_encoder)

# 返回两个张量的点积
match = dot([story_encoder,question_encoder],axes=[2,2])

# 将故事编码为问题的向量空间
story_encoder_c = Embedding(input_dim=vocab_size,output_dim=question_maxlen,input_length=story_maxlen)(story_input)
story_encoder_c = Dropout(0.3)(story_encoder_c)

# 结合两个向量 match和story_encoder_c
response = add([match,story_encoder_c])
response = Permute((2,1))(response)

# 结合两个向量 response和question_encoder
answer = concatenate([response, question_encoder], axis=-1)
answer = LSTM(LATENT_SIZE)(answer)
answer = Dropout(0.2)(answer)
answer = Dense(vocab_size)(answer)
output = Activation("softmax")(answer)

model = Model(inputs=[story_input, question_input], outputs=output)
model.compile(optimizer="adam", loss="categorical_crossentropy",metrics=["accuracy"])

print(model.summary())

# 模型训练
history = model.fit([Xstrain,Xqtrain],[Ytrain],batch_size=BATCH_SIZE,epochs=NUM_EPOCHS,
                    validation_data=([Xstest,Xqtest],[Ytest]))

# 画出准确率和损失函数
plt.title('Episodic Memory Q&A Accuracy')
plt.plot(history.history['acc'],color='g',label='train')
plt.plot(history.history['val_acc'],color='r',label='validation')
plt.legend(loc='best')
plt.show()

# get predictions of labels

ytest = np.argmax(Ytest, axis=1)
Ytest_ = model.predict([Xstest, Xqtest])
ytest_ = np.argmax(Ytest_, axis=1)

# 随机选择几个问题测试
NUM_DISPLAY = 10
for i in random.sample(range(Xstest.shape[0]),NUM_DISPLAY):
    story = " ".join([indx2word[x] for x in Xstest[i].tolist() if x != 0])
    question = " ".join([indx2word[x] for x in Xqtest[i].tolist()])
    label = indx2word[ytest[i]]
    prediction = indx2word[ytest_[i]]
    print(story, question, label, prediction)

输出：

转载于:https://www.cnblogs.com/peng8098/p/nlp_22.html