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最近花周末两天时间利用pytorch实现了TextCNN进行了中文文本分类，在此进行记录。
相关代码详见：https://github.com/PingHGao/textCNN_pytorch
数据获取
中文数据是从https://github.com/brightmart/nlp_chinese_corpus下载的。具体是第3个，百科问答Json版，因为感觉大小适中，适合用来学习。下载下来得到两个文件：baike_qa_train.json和baike_qa_valid.json。内容如下:
{"qid": "qid_1815059893214501395", "category": "烦恼-恋爱", "title": "请问深入骨髓地喜欢一个人怎么办我不能确定对方是不是喜欢我，我却想 ", "desc": "我不能确定对方是不是喜欢我，我却想分分秒秒跟他在一起，有谁能告诉我如何能想他少一点", "answer": "一定要告诉他你很喜欢他 很爱他!! 虽然不知道你和他现在的关系是什么！但如果真的觉得很喜欢就向他表白啊！！起码你努力过了！ 女生主动多少占一点优势的！！呵呵 只愿曾经拥有！ 到以后就算感情没现在这么强烈了也不会觉得遗憾啊~！ 与其每天那么痛苦的想他 恋他 还不如直接告诉他 ！ 不要怕回破坏你们现有的感情！因为如果不告诉他 你可能回后悔一辈子！！ "}
数据预处理
样本选取
下下来的数据类别非常多，为了简化，我从中帅选了少量的样本进行学习。具体来说，我只选择了标题前2个字为教育、健康、生活、娱乐和游戏五个类别，同时各个类别各5000个。代码如下：
# -*- coding: utf-8 -*-
'''
从原数据中选取部分数据；
选取数据的title前两个字符在字典WantedClass中；
且各个类别的数量为WantedNum
'''
import jieba
import json
TrainJsonFile = 'baike_qa2019/baike_qa_train.json'
MyTainJsonFile = 'baike_qa2019/my_traindata.json'
StopWordFile = 'stopword.txt'
WantedClass = {'教育': 0, '健康': 0, '生活': 0, '娱乐': 0, '游戏': 0}
WantedNum = 5000
numWantedAll = WantedNum * 5
def main():
Datas = open(TrainJsonFile , 'r', encoding='utf_8').readlines()
f = open(MyTainJsonFile , 'w', encoding='utf_8')
numInWanted = 0
for line in Datas:
data = json.loads(line)
cla = data['category'][0:2]
if cla in WantedClass and WantedClass[cla] < WantedNum:
json_data = json.dumps(data, ensure_ascii=False)
f.write(json_data)
f.write('\n')
WantedClass[cla] += 1
numInWanted += 1
if numInWanted >= numWantedAll:
break
if __name__ == '__main__':
main()
生成词表
在有了训练数据之后，我们需要得到训练数据中所有的“title”对应的词表。也就是说我们首先对每个标题使用jieba分词工具进行分词，之后去除停用词，剩下的就构成了我们的词表。具体代码如下：
# -*- coding: utf-8 -*-
'''
将训练数据使用jieba分词工具进行分词。并且剔除stopList中的词。
得到词表：
词表的每一行的内容为：词 词的序号 词的频次
'''
import json
import jieba
from tqdm import tqdm
trainFile = 'baike_qa2019/my_traindata.json'
stopwordFile = 'stopword.txt'
wordLabelFile = 'wordLabel.txt'
lengthFile = 'length.txt'
def read_stopword(file):
data = open(file, 'r', encoding='utf_8').read().split('\n')
return data
def main():
worddict = {}
stoplist = read_stopword(stopwordFile)
datas = open(trainFile, 'r', encoding='utf_8').read().split('\n')
datas = list(filter(None, datas))
data_num = len(datas)
len_dic = {}
for line in datas:
line = json.loads(line)
title = line['title']
title_seg = jieba.cut(title, cut_all=False)
length = 0
for w in title_seg:
if w in stoplist:
continue
length += 1
if w in worddict:
worddict[w] += 1
else:
worddict[w] = 1
if length in len_dic:
len_dic[length] += 1
else:
len_dic[length] = 1
wordlist = sorted(worddict.items(), key=lambda item:item[1], reverse=True)
f = open(wordLabelFile, 'w', encoding='utf_8')
ind = 0
for t in wordlist:
d = t[0] + ' ' + str(ind) + ' ' + str(t[1]) + '\n'
ind += 1
f.write(d)
for k, v in len_dic.items():
len_dic[k] = round(v * 1.0 / data_num, 3)
len_list = sorted(len_dic.items(), key=lambda item:item[0], reverse=True)
f = open(lengthFile, 'w')
for t in len_list:
d = str(t[0]) + ' ' + str(t[1]) + '\n'
f.write(d)
if __name__ == "__main__":
main()
得到的词表内容如下：
的 0 17615
我 1 7921
是 2 6048
了 3 5105
有 4 4694
什么 5 4565
吗 6 3113
在 7 2877
怎么 8 2447
啊 9 2133
将中文标题转化为数字向量
有了词表，我们就可以文本转化为数字了。比如下面这句话：
- “我爱人工智能啊” （原始句子）
- 我 / 爱 / 人工智能 / 啊 （jieba分词结果）
- 我 / 爱 / 人工智能 （去除停用词啊）
- 1 5 102 0 0（将其数字化，“我”对应1，人工智能对应102。假设我们设定句子长度为5.则需要在后面加两个0）
具体代码
#-*- coding: utf_8 -*-
import json
import sys, io
import jieba
import random
sys.stdout = io.TextIOWrapper(sys.stdout.buffer,encoding='gb18030') #改变标准输出的默认编码
trainFile = 'baike_qa2019/my_traindata.json'
stopwordFile = 'stopword.txt'
wordLabelFile = 'wordLabel.txt'
trainDataVecFile = 'traindata_vec.txt'
maxLen = 20
labelFile = 'label.txt'
def read_labelFile(file):
data = open(file, 'r', encoding='utf_8').read().split('\n')
label_w2n = {}
label_n2w = {}
for line in data:
line = line.split(' ')
name_w = line[0]
name_n = int(line[1])
label_w2n[name_w] = name_n
label_n2w[name_n] = name_w
return label_w2n, label_n2w
def read_stopword(file):
data = open(file, 'r', encoding='utf_8').read().split('\n')
return data
def get_worddict(file):
datas = open(file, 'r', encoding='utf_8').read().split('\n')
datas = list(filter(None, datas))
word2ind = {}
for line in datas:
line = line.split(' ')
word2ind[line[0]] = int(line[1])
ind2word = {word2ind[w]:w for w in word2ind}
return word2ind, ind2word
def json2txt():
label_dict, label_n2w = read_labelFile(labelFile)
word2ind, ind2word = get_worddict(wordLabelFile)
traindataTxt = open(trainDataVecFile, 'w')
stoplist = read_stopword(stopwordFile)
datas = open(trainFile, 'r', encoding='utf_8').read().split('\n')
datas = list(filter(None, datas))
random.shuffle(datas)
for line in datas:
line = json.loads(line)
title = line['title']
cla = line['category'][0:2]
cla_ind = label_dict[cla]
title_seg = jieba.cut(title, cut_all=False)
title_ind = [cla_ind]
for w in title_seg:
if w in stoplist:
continue
title_ind.append(word2ind[w])
length = len(title_ind)
if length > maxLen + 1:
title_ind = title_ind[0:21]
if length < maxLen + 1:
title_ind.extend([0] * (maxLen - length + 1))
for n in title_ind:
traindataTxt.write(str(n) + ',')
traindataTxt.write('\n')
def main():
json2txt()
if __name__ == "__main__":
main()
得到的新的数据如下：
4,1,0,1731,1448,386,3219,38,47,56,102,1374,1,0,386,3219,392,2,14116,3,102,
3,7522,0,4792,31,146,16345,434,31,4,37414,118,16345,104,208,831,0,0,0,0,0,
4,2241,314,25,7,68,1077,54,10165,143,5841,6,714,60,237,23837,3,163,30752,0,0,
4,742,126,2,5,124,16503,3629,36296,3629,1981,3629,776,16503,34415,0,0,0,0,0,0,
2,8,969,16772,13,9776,0,486,8,248,16772,9,0,0,0,0,0,0,0,0,0,
其中每一行第一个数字为类别，剩下20个数字为句子内容。这里决定得最大句子长度为20.
模型搭建
模型包含embedding层，卷积层，dropout层和全连接层。
具体的参数为：
textCNN_param = {
'vocab_size': len(word2ind),
'embed_dim': 60,
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
结构如下：
import torch
import torch.nn as nn
from torch.nn import functional as F
import math
class textCNN(nn.Module):
def __init__(self, param):
super(textCNN, self).__init__()
ci = 1 # input chanel size
kernel_num = param['kernel_num'] # output chanel size
kernel_size = param['kernel_size']
vocab_size = param['vocab_size']
embed_dim = param['embed_dim']
dropout = param['dropout']
class_num = param['class_num']
self.param = param
self.embed = nn.Embedding(vocab_size, embed_dim, padding_idx=1)
self.conv11 = nn.Conv2d(ci, kernel_num, (kernel_size[0], embed_dim))
self.conv12 = nn.Conv2d(ci, kernel_num, (kernel_size[1], embed_dim))
self.conv13 = nn.Conv2d(ci, kernel_num, (kernel_size[2], embed_dim))
self.dropout = nn.Dropout(dropout)
self.fc1 = nn.Linear(len(kernel_size) * kernel_num, class_num)
def init_embed(self, embed_matrix):
self.embed.weight = nn.Parameter(torch.Tensor(embed_matrix))
@staticmethod
def conv_and_pool(x, conv):
# x: (batch, 1, sentence_length, )
x = conv(x)
# x: (batch, kernel_num, H_out, 1)
x = F.relu(x.squeeze(3))
# x: (batch, kernel_num, H_out)
x = F.max_pool1d(x, x.size(2)).squeeze(2)
# (batch, kernel_num)
return x
def forward(self, x):
# x: (batch, sentence_length)
x = self.embed(x)
# x: (batch, sentence_length, embed_dim)
# TODO init embed matrix with pre-trained
x = x.unsqueeze(1)
# x: (batch, 1, sentence_length, embed_dim)
x1 = self.conv_and_pool(x, self.conv11) # (batch, kernel_num)
x2 = self.conv_and_pool(x, self.conv12) # (batch, kernel_num)
x3 = self.conv_and_pool(x, self.conv13) # (batch, kernel_num)
x = torch.cat((x1, x2, x3), 1) # (batch, 3 * kernel_num)
x = self.dropout(x)
logit = F.log_softmax(self.fc1(x), dim=1)
return logit
def init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
训练
有了数据和模型，只剩下训练了，使用pytorch是非常方便的，就把代码贴出来吧
数据加载相关代码：
from torch.utils.data import Dataset, DataLoader
import torch
import random
import numpy as np
trainDataFile = 'traindata_vec.txt'
valDataFile = 'valdata_vec.txt'
def get_valdata(file=valDataFile):
valData = open(valDataFile, 'r').read().split('\n')
valData = list(filter(None, valData))
random.shuffle(valData)
return valData
class textCNN_data(Dataset):
def __init__(self):
trainData = open(trainDataFile, 'r').read().split('\n')
trainData = list(filter(None, trainData))
random.shuffle(trainData)
self.trainData = trainData
def __len__(self):
return len(self.trainData)
def __getitem__(self, idx):
data = self.trainData[idx]
data = list(filter(None, data.split(',')))
data = [int(x) for x in data]
cla = data[0]
sentence = np.array(data[1:])
return cla, sentence
def textCNN_dataLoader(param):
dataset = textCNN_data()
batch_size = param['batch_size']
shuffle = param['shuffle']
return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
if __name__ == "__main__":
dataset = textCNN_data()
cla, sen = dataset.__getitem__(0)
print(cla)
print(sen)
训练代码如下：
import torch
import os
import torch.nn as nn
import numpy as np
import time
from model import textCNN
import sen2inds
import textCNN_data
word2ind, ind2word = sen2inds.get_worddict('wordLabel.txt')
label_w2n, label_n2w = sen2inds.read_labelFile('label.txt')
textCNN_param = {
'vocab_size': len(word2ind),
'embed_dim': 60,
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
dataLoader_param = {
'batch_size': 128,
'shuffle': True,
}
def main():
#init net
print('init net...')
net = textCNN(textCNN_param)
weightFile = 'weight.pkl'
if os.path.exists(weightFile):
print('load weight')
net.load_state_dict(torch.load(weightFile))
else:
net.init_weight()
print(net)
net.cuda()
#init dataset
print('init dataset...')
dataLoader = textCNN_data.textCNN_dataLoader(dataLoader_param)
valdata = textCNN_data.get_valdata()
optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
criterion = nn.NLLLoss()
log = open('log_{}.txt'.format(time.strftime('%y%m%d%H')), 'w')
log.write('epoch step loss\n')
log_test = open('log_test_{}.txt'.format(time.strftime('%y%m%d%H')), 'w')
log_test.write('epoch step test_acc\n')
print("training...")
for epoch in range(100):
for i, (clas, sentences) in enumerate(dataLoader):
optimizer.zero_grad()
sentences = sentences.type(torch.LongTensor).cuda()
clas = clas.type(torch.LongTensor).cuda()
out = net(sentences)
loss = criterion(out, clas)
loss.backward()
optimizer.step()
if (i + 1) % 1 == 0:
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
data = str(epoch + 1) + ' ' + str(i + 1) + ' ' + str(loss.item()) + '\n'
log.write(data)
print("save model...")
torch.save(net.state_dict(), weightFile)
torch.save(net.state_dict(), "model\{}_model_iter_{}_{}_loss_{:.2f}.pkl".format(time.strftime('%y%m%d%H'), epoch, i, loss.item())) # current is model.pkl
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
if __name__ == "__main__":
main()
测试结果以及代码
测试代码
import torch
import os
import torch.nn as nn
import numpy as np
import time
from model import textCNN
import sen2inds
word2ind, ind2word = sen2inds.get_worddict('wordLabel.txt')
label_w2n, label_n2w = sen2inds.read_labelFile('label.txt')
textCNN_param = {
'vocab_size': len(word2ind),
'embed_dim': 60,
'class_num': len(label_w2n),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
def get_valData(file):
datas = open(file, 'r').read().split('\n')
datas = list(filter(None, datas))
return datas
def parse_net_result(out):
score = max(out)
label = np.where(out == score)[0][0]
return label, score
def main():
#init net
print('init net...')
net = textCNN(textCNN_param)
weightFile = 'textCNN.pkl'
if os.path.exists(weightFile):
print('load weight')
net.load_state_dict(torch.load(weightFile))
else:
print('No weight file!')
exit()
print(net)
net.cuda()
net.eval()
numAll = 0
numRight = 0
testData = get_valData('valdata_vec.txt')
for data in testData:
numAll += 1
data = data.split(',')
label = int(data[0])
sentence = np.array([int(x) for x in data[1:21]])
sentence = torch.from_numpy(sentence)
predict = net(sentence.unsqueeze(0).type(torch.LongTensor).cuda()).cpu().detach().numpy()[0]
label_pre, score = parse_net_result(predict)
if label_pre == label and score > -100:
numRight += 1
if numAll % 100 == 0:
print('acc:{}({}/{})'.format(numRight / numAll, numRight, numAll))
if __name__ == "__main__":
main()
测试结果：
acc:0.78(78/100)
acc:0.71(710/1000)
acc:0.7218(3609/5000)
可见准确率超过了0.7
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