tensorflow实战之训练word2vec模型

# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import math
import collections
import pickle as pkl
from pprint import pprint
#from pymongo import MongoClient
import re
import jieba
import os.path as path
import os

class word2vec():
    def __init__(self,
                 vocab_list=None,
                 embedding_size=200,
                 win_len=3, # 单边窗口长
                 num_sampled=1000,
                 learning_rate=1.0,
                 logdir='/tmp/simple_word2vec',
                 model_path= None
                 ):

        # 获得模型的基本参数
        self.batch_size     = None # 一批中数据个数, 目前是根据情况来的
        if model_path!=None:
            self.load_model(model_path)
        else:
            # model parameters
            assert type(vocab_list)==list
            self.vocab_list     = vocab_list
            self.vocab_size     = vocab_list.__len__()
            self.embedding_size = embedding_size
            self.win_len        = win_len
            self.num_sampled    = num_sampled
            self.learning_rate  = learning_rate
            self.logdir         = logdir

            self.word2id = {}   # word => id 的映射
            for i in range(self.vocab_size):
                self.word2id[self.vocab_list[i]] = i

            # train times
            self.train_words_num = 0 # 训练的单词对数
            self.train_sents_num = 0 # 训练的句子数
            self.train_times_num = 0 # 训练的次数（一次可以有多个句子）

            # train loss records
            self.train_loss_records = collections.deque(maxlen=10) # 保存最近10次的误差
            self.train_loss_k10 = 0

        self.build_graph()
        self.init_op()
        if model_path!=None:
            tf_model_path = os.path.join(model_path,'tf_vars')
            self.saver.restore(self.sess,tf_model_path)

    def init_op(self):
        self.sess = tf.Session(graph=self.graph)
        self.sess.run(self.init)
        self.summary_writer = tf.summary.FileWriter(self.logdir, self.sess.graph)

    def build_graph(self):
        self.graph = tf.Graph()
        with self.graph.as_default():
            self.train_inputs = tf.placeholder(tf.int32, shape=[self.batch_size])
            self.train_labels = tf.placeholder(tf.int32, shape=[self.batch_size, 1])
            self.embedding_dict = tf.Variable(
                tf.random_uniform([self.vocab_size,self.embedding_size],-1.0,1.0)
            )
            self.nce_weight = tf.Variable(tf.truncated_normal([self.vocab_size, self.embedding_size],
                                                              stddev=1.0/math.sqrt(self.embedding_size)))
            self.nce_biases = tf.Variable(tf.zeros([self.vocab_size]))

            # 将输入序列向量化
            embed = tf.nn.embedding_lookup(self.embedding_dict, self.train_inputs) # batch_size

            # 得到NCE损失
            self.loss = tf.reduce_mean(
                tf.nn.nce_loss(
                    weights = self.nce_weight,
                    biases = self.nce_biases,
                    labels = self.train_labels,
                    inputs = embed,
                    num_sampled = self.num_sampled,
                    num_classes = self.vocab_size
                )
            )

            # tensorboard 相关
            tf.summary.scalar('loss',self.loss)  # 让tensorflow记录参数

            # 根据 nce loss 来更新梯度和embedding
            self.train_op = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(self.loss)  # 训练操作

            # 计算与指定若干单词的相似度
            self.test_word_id = tf.placeholder(tf.int32,shape=[None])
            vec_l2_model = tf.sqrt(  # 求各词向量的L2模
                tf.reduce_sum(tf.square(self.embedding_dict),1,keep_dims=True)
            )

            avg_l2_model = tf.reduce_mean(vec_l2_model)
            tf.summary.scalar('avg_vec_model',avg_l2_model)

            self.normed_embedding = self.embedding_dict / vec_l2_model
            # self.embedding_dict = norm_vec # 对embedding向量正则化
            test_embed = tf.nn.embedding_lookup(self.normed_embedding, self.test_word_id)
            self.similarity = tf.matmul(test_embed, self.normed_embedding, transpose_b=True)

            # 变量初始化
            self.init = tf.global_variables_initializer()

            self.merged_summary_op = tf.summary.merge_all()

            self.saver = tf.train.Saver()

    def train_by_sentence(self, input_sentence=[]):
        #  input_sentence: [sub_sent1, sub_sent2, ...]
        # 每个sub_sent是一个单词序列，例如['这次','大选','让']
        sent_num = input_sentence.__len__()
        batch_inputs = []
        batch_labels = []
        for sent in input_sentence:
            for i in range(sent.__len__()):
                start = max(0,i-self.win_len)
                end = min(sent.__len__(),i+self.win_len+1)
                for index in range(start,end):
                    if index == i:
                        continue
                    else:
                        input_id = self.word2id.get(sent[i])
                        label_id = self.word2id.get(sent[index])
                        if not (input_id and label_id):
                            continue
                        batch_inputs.append(input_id)
                        batch_labels.append(label_id)
        if len(batch_inputs)==0:
            return
        batch_inputs = np.array(batch_inputs,dtype=np.int32)
        batch_labels = np.array(batch_labels,dtype=np.int32)
        batch_labels = np.reshape(batch_labels,[batch_labels.__len__(),1])

        feed_dict = {
            self.train_inputs: batch_inputs,
            self.train_labels: batch_labels
        }
        _, loss_val, summary_str = self.sess.run([self.train_op,self.loss,self.merged_summary_op], feed_dict=feed_dict)

        # train loss
        self.train_loss_records.append(loss_val)
        # self.train_loss_k10 = sum(self.train_loss_records)/self.train_loss_records.__len__()
        self.train_loss_k10 = np.mean(self.train_loss_records)
        if self.train_sents_num % 1000 == 0 :
            self.summary_writer.add_summary(summary_str,self.train_sents_num)
            print("{a} sentences dealed, loss: {b}"
                  .format(a=self.train_sents_num,b=self.train_loss_k10))

        # train times
        self.train_words_num += batch_inputs.__len__()
        self.train_sents_num += input_sentence.__len__()
        self.train_times_num += 1

    def cal_similarity(self,test_word_id_list,top_k=10):
        sim_matrix = self.sess.run(self.similarity, feed_dict={self.test_word_id:test_word_id_list})
        sim_mean = np.mean(sim_matrix)
        sim_var = np.mean(np.square(sim_matrix-sim_mean))
        test_words = []
        near_words = []
        for i in range(test_word_id_list.__len__()):
            test_words.append(self.vocab_list[test_word_id_list[i]])
            nearst_id = (-sim_matrix[i,:]).argsort()[1:top_k+1]
            nearst_word = [self.vocab_list[x] for x in nearst_id]
            near_words.append(nearst_word)
        return test_words,near_words,sim_mean,sim_var

    def save_model(self, save_path):

        if os.path.isfile(save_path):
            raise RuntimeError('the save path should be a dir')
        if not os.path.exists(save_path):
            os.mkdir(save_path)

        # 记录模型各参数
        model = {}
        var_names = ['vocab_size',      # int       model parameters
                     'vocab_list',      # list
                     'learning_rate',   # int
                     'word2id',         # dict
                     'embedding_size',  # int
                     'logdir',          # str
                     'win_len',         # int
                     'num_sampled',     # int
                     'train_words_num', # int       train info
                     'train_sents_num', # int
                     'train_times_num', # int
                     'train_loss_records',  # int   train loss
                     'train_loss_k10',  # int
                     ]
        for var in var_names:
            model[var] = eval('self.'+var)

        param_path = os.path.join(save_path,'params.pkl')
        if os.path.exists(param_path):
            os.remove(param_path)
        with open(param_path,'wb') as f:
            pkl.dump(model,f)

        # 记录tf模型
        tf_path = os.path.join(save_path,'tf_vars')
        if os.path.exists(tf_path):
            os.remove(tf_path)
        self.saver.save(self.sess,tf_path)

    def load_model(self, model_path):
        if not os.path.exists(model_path):
            raise RuntimeError('file not exists')
        param_path = os.path.join(model_path,'params.pkl')
        with open(param_path,'rb') as f:
            model = pkl.load(f)
            self.vocab_list = model['vocab_list']
            self.vocab_size = model['vocab_size']
            self.logdir = model['logdir']
            self.word2id = model['word2id']
            self.embedding_size = model['embedding_size']
            self.learning_rate = model['learning_rate']
            self.win_len = model['win_len']
            self.num_sampled = model['num_sampled']
            self.train_words_num = model['train_words_num']
            self.train_sents_num = model['train_sents_num']
            self.train_times_num = model['train_times_num']
            self.train_loss_records = model['train_loss_records']
            self.train_loss_k10 = model['train_loss_k10']

if __name__=='__main__':

    # step 1 读取停用词
    stop_words = []
    with open('stop_words.txt',encoding= 'utf-8') as f:
        line = f.readline()
        while line:
            stop_words.append(line[:-1])
            line = f.readline()
    stop_words = set(stop_words)
    print('停用词读取完毕，共{n}个单词'.format(n=len(stop_words)))

    # step2 读取文本，预处理，分词，得到词典
    raw_word_list = []
    sentence_list = []
    with open('2800.txt',encoding='gbk') as f:
        line = f.readline()
        while line:
            while '\n' in line:
                line = line.replace('\n','')
            while ' ' in line:
                line = line.replace(' ','')
            if len(line)>0: # 如果句子非空
                raw_words = list(jieba.cut(line,cut_all=False))
                dealed_words = []
                for word in raw_words:
                    if word not in stop_words and word not in ['qingkan520','www','com','http']:
                        raw_word_list.append(word)
                        dealed_words.append(word)
                sentence_list.append(dealed_words)
            line = f.readline()
    word_count = collections.Counter(raw_word_list)
    print('文本中总共有{n1}个单词,不重复单词数{n2},选取前30000个单词进入词典'
          .format(n1=len(raw_word_list),n2=len(word_count)))
    word_count = word_count.most_common(30000)
    word_list = [x[0] for x in word_count]

    # 创建模型，训练
    w2v = word2vec(vocab_list=word_list,    # 词典集
                   embedding_size=200,
                   win_len=2,
                   learning_rate=1,
                   num_sampled=100,         # 负采样个数
                   logdir='/tmp/280')       # tensorboard记录地址
    

    num_steps = 10000
    for i in range(num_steps):
        #print (i%len(sentence_list))
        sent = sentence_list[i%len(sentence_list)]
        w2v.train_by_sentence([sent])
    w2v.save_model('model')
    
    w2v.load_model('model') 
    test_word = ['天地','级别']
    test_id = [word_list.index(x) for x in test_word]
    test_words,near_words,sim_mean,sim_var = w2v.cal_similarity(test_id)
    print (test_words,near_words,sim_mean,sim_var)

打印结果：

0 sentences dealed, loss: 288.8526306152344
1000 sentences dealed, loss: 104.01668548583984
2000 sentences dealed, loss: 47.82024383544922
3000 sentences dealed, loss: 34.190834045410156
4000 sentences dealed, loss: 23.23956871032715
5000 sentences dealed, loss: 12.838109016418457
6000 sentences dealed, loss: 11.630112648010254
7000 sentences dealed, loss: 7.72381591796875
8000 sentences dealed, loss: 8.49630355834961
9000 sentences dealed, loss: 7.004128456115723
['天地', '级别'] [['美丽', '一声', '条件', '待续', '随手', '解除', '小姐', '讨要', '拳头', '眉头'], ['炼', '盆中', '紧紧', '坊', '灵', '强者', '第十一章', '魔', '拍', '摇']] 0.11028896 0.005840174