• 預(yù)處理
  數(shù)據(jù)集使用Facebook上的BABI數(shù)據(jù)集
  將文件提取成可訓(xùn)練的數(shù)據(jù)集，包括：文章 問題 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,answersdata_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')

輸出：

Train observations: 10000 Test observations: 1000

• 如何實現(xiàn)
  1.預(yù)處理：創(chuàng)建字典并將文章，問題和答案映射到詞表，進(jìn)一步映射成向量形式
  2.模型創(chuàng)建和驗證：訓(xùn)練模型并在驗證數(shù)據(jù)集上測試
  3.預(yù)測結(jié)果：測試集測試數(shù)據(jù)的結(jié)果
• 代碼

from __future__ import division,print_function import collections import itertools import nltk import numpy as np import matplotlib.pyplot as plt import os import randomdef 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) # 去掉前面的數(shù)字標(biāo)記if '\t' in text: # 有制表符的是 問題 和 答案question,answer,_ = text.split('\t')stories.append(story_text)questions.append(question)answers.append(answer)story_text = []else: # 沒制表符的是文章story_text.append(text)fin.close()return stories,questions,answersdata_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() # 返回列表元素出現(xiàn)次數(shù)的 字典，這里沒有參數(shù)是一個空字典 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()] += 1for question in questions:for word in nltk.word_tokenize(question):dictnry[word.lower()] += 1for answer in answers:for word in nltk.word_tokenize(answer):dictnry[word.lower()] += 1word2indx = {w:(i+1) for i,(w,_) in enumerate(dictnry.most_common())} # 按詞頻排序 word2indx['PAD'] = 0 indx2word = {v:k for k,v in word2indx.items()}vocab_size = len(word2indx) # 一共有22個不重復(fù)單詞 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 = 0for sent in story:swords = nltk.word_tokenize(sent)story_len += len(swords)if story_len > story_maxlen:story_maxlen = story_lenfor 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) # 文章單詞最大長度為14，問題中的單詞最大長度為4，長度不夠的補0，維度相同便于并向計算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_utilsdef data_vectorization(data,word2indx,story_maxlen,question_maxlen): # 詞 => 詞向量Xs,Xq,Y = [],[],[]stories,questions,answers = datafor 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)# 超參數(shù) EMBEDDING_SIZE = 128 LATENT_SIZE = 64 BATCH_SIZE = 64 NUM_EPOCHS = 40# 輸入層 story_input = Input(shape=(story_maxlen,)) question_input = Input(shape=(question_maxlen,))# Story encoder embedding # 將正整數(shù)（索引）轉(zhuǎn)換為固定大小的密集向量。 # 例如，[[4]，[20]]->[[0.25，0.1]，[0.6，-0.2]] 此層只能用作模型中的第一層 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)# 返回兩個張量的點積 match = dot([story_encoder,question_encoder],axes=[2,2])# 將故事編碼為問題的向量空間 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)# 結(jié)合兩個向量 match和story_encoder_c response = add([match,story_encoder_c]) response = Permute((2,1))(response)# 結(jié)合兩個向量 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())# 模型訓(xùn)練 history = model.fit([Xstrain,Xqtrain],[Ytrain],batch_size=BATCH_SIZE,epochs=NUM_EPOCHS,validation_data=([Xstest,Xqtest],[Ytest]))# 畫出準(zhǔn)確率和損失函數(shù) 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 labelsytest = np.argmax(Ytest, axis=1) Ytest_ = model.predict([Xstest, Xqtest]) ytest_ = np.argmax(Ytest_, axis=1)# 隨機選擇幾個問題測試 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)

輸出：

轉(zhuǎn)載于:https://www.cnblogs.com/peng8098/p/nlp_22.html

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