停用词 stop_words.txt 文件可以自己生成，每个一行，内容可参考另一篇文档

2800.txt 文件是待训练文件，可以自己任意找一篇中文简体文档， 格式为txt文件

-- coding: utf-8 --

"""
Created on Sat Jul 13 13:22:43 2019
@author:
"""

-- 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

需要指定文档目录 os.chdir("D:\tensorflow\word2vec")

class word2vec():
def init(self,
vocab_list=None,
embedding_size=200,
win_len=3, # 单边窗口长
num_sampled=1000,
learning_rate=0.5,
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) #skip gram method
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']  #替换成自己文档中的单词
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)