NLP06：基于TextCNN的中文文本分类

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公众号：数据挖掘与机器学习笔记

1.TextCNN基本原理

主要看第二张图：

• 第一层为输入层，输入是一个$n\times k$的矩阵,图中为$7\times 5$。其中$n$为句子中的单词数，$k$为词向量维度。词向量可以是预训练好的，也可以在网络中重新开始训练。第一张图中输入有两个矩阵，其中一个使用的预训练好的向量，另一个则作为训练参数。
• 第二层为卷积层，可以把矩阵理解为一张channels为1的图像，使用宽度同词向量维度一样的卷积核去做卷积运算，且卷积核只在高度方向(单词方向).因此每次卷积核滑动的位置都是完整的单词，保证了单词作为语言中最小粒度的合理性。假设词向量维度为embedding_dim,卷积核高度为filter_window_size,则卷积核大小为(embedding_dim,filter_window_size),卷积后的大小为(sequence_len-filter_window_size,1)
• 第三层为卷积层，经过max_pooling后得到一个标量。实际中会使用num_filters卷积核同时卷积，每个卷积核得到的标量拼接在一起形成一个向量。此外，也会使用多个filter_window_size(如图2中3个filter_window_size分别为3、4、5)，每个filter_window_size会得到一个向量，最后把所有的向量拼接在一起，然后接一个softmax进行分类。

2. TextCNN实现

import torch
from torch import nn
from torch.nn import functional as F
import math
from torch.utils.data import Dataset,DataLoader
import numpy as np
import random
from sklearn.model_selection import train_test_split
import pandas as pd
import re
from tensorflow.keras.preprocessing import sequencemaxlen=300
batch_size=128

2.1 数据预处理

def textToChars(filePath):"""读取文本文件并进行处理:param filePath:文件路径:return:"""lines = []df=pd.read_excel(filePath,header=None)df.columns=['content']for index, row in df.iterrows():row=row['content']row = re.sub("[^\u4e00-\u9fa5]", "", str(row))  # 只保留中文lines.append(list(row))return linesdef getWordIndex(vocabPath):"""获取word2Index,index2Word:param vocabPath:词汇文件:return:"""word2Index = {}with open(vocabPath, encoding="utf-8") as f:for line in f.readlines():word2Index[line.strip()] = len(word2Index)index2Word = dict(zip(word2Index.values(), word2Index.keys()))return word2Index, index2Worddef lodaData(posFile, negFile, word2Index):"""获取训练数据:param posFile:正样本文件:param negFile:负样本文件:param word2Index::return:"""posLines = textToChars(posFile)negLines = textToChars(negFile)posIndexLines = [[word2Index[word] if word2Index.get(word) else 0 for word in line] for line in posLines]negIndexLines = [[word2Index[word] if word2Index.get(word) else 0 for word in line] for line in negLines]lines = posIndexLines + negIndexLinesprint("训练样本和测试样本共：%d 个"%(len(lines)))# lens = [len(line) for line in lines]labels = [1] * len(posIndexLines) + [0] * len(negIndexLines)padSequences = sequence.pad_sequences(lines, maxlen=maxlen, padding="post", truncating="post")X_train,X_test,y_train,y_test=train_test_split(padSequences,np.array(labels),test_size=0.2,random_state=42)return X_train,X_test,y_train,y_test

vocabPath="/content/drive/My Drive/data/vocab.txt"
negFilePath="/content/drive/My Drive/data/text_classify/sentiment/neg.xls"
posFilePath="/content/drive/My Drive/data/text_classify/sentiment/pos.xls"
word2Index, index2Word=getWordIndex(vocabPath)
X_train,X_test,y_train,y_test=lodaData(posFile=posFilePath,negFile=negFilePath,word2Index=word2Index)
print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)

class MyDataset(Dataset):def __init__(self,features,labels):"""features:文本向量化后的特征labels:标签向量 """self.features=featuresself.labels=labelsdef __len__(self):return self.features.shape[0]def __getitem__(self,index):return self.features[index],self.labels[index]train_dataset=MyDataset(X_train,y_train)
test_dataset=MyDataset(X_test,y_test)
train_dataloader=DataLoader(train_dataset,batch_size=batch_size,shuffle=True)
test_dataloader=DataLoader(test_dataset,batch_size=batch_size,shuffle=False)

2.2 TextCNN实现

class TextCnn(nn.Module):def __init__(self, param: dict):super(TextCnn, self).__init__()input_channel = 1  # input channel sizeoutput_channel = param["output_channel"]  # output channel sizekernel_size = param["kernel_size"]vocab_size = param["vocab_size"]embedding_dim = param["embedding_dim"]dropout = param["dropout"]class_num = param["class_num"]self.param = paramself.embedding = nn.Embedding(vocab_size, embedding_dim,padding_idx=0)self.conv1 = nn.Conv2d(input_channel, output_channel, (kernel_size[0], embedding_dim))self.conv2 = nn.Conv2d(input_channel, output_channel, (kernel_size[1], embedding_dim))self.conv3 = nn.Conv2d(input_channel, output_channel, (kernel_size[2], embedding_dim))self.dropout = nn.Dropout(dropout)self.fc1 = nn.Linear(len(kernel_size) * output_channel, class_num)def init_embedding(self, embedding_matrix):self.embedding.weight = nn.Parameter(torch.Tensor(embedding_matrix))@staticmethoddef conv_pool(x, conv):"""卷积+池化:param x:[batch_size,1,sequence_length,embedding_dim]:param conv::return:"""x = conv(x)  # 卷积， [batch_size,output_channel,h_out,1]x = F.relu((x.squeeze(3)))  # 去掉最后一维,[batch_size,output_channel,h_out]x = F.max_pool1d(x, x.size(2)).squeeze(2)  # [batch_size,output_channel]return xdef forward(self, x):"""前向传播:param x:[batch_size,sequence_length]:return:"""x = self.embedding(x)  # [batch_size,sequence_length,embedding_dim]x = x.unsqueeze(1)  # 增加一个channel维度 [batch_size,1,sequence_length,embedding_dim]x1 = self.conv_pool(x, self.conv1)  # [batch_size,output_channel]x2 = self.conv_pool(x, self.conv2)  # [batch_size,output_channel]x3 = self.conv_pool(x, self.conv3)  # [batch_size,output_channel]x = torch.cat((x1, x2, x3), 1)  # [batch_size,output_channel*3]x = self.dropout(x)logit = F.log_softmax(self.fc1(x), dim=1)return logitdef init_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):n = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsm.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_()

2.3 模型训练

textCNNParams={"vocab_size":len(word2Index),"embedding_dim":100,"class_num":2,"output_channel":4,"kernel_size":[3,4,5],"dropout":0.2
}

net=TextCnn(textCNNParams)
# net.init_weights()net.cuda()

optimizer=torch.optim.SGD(net.parameters(),lr=0.01)
criterion=nn.NLLLoss()

for epoch in range(10):total_train_loss=[]net.train()for i,(feature,label) in enumerate(train_dataloader):feature=feature.cuda()label=label.cuda()y_pred=net(feature.long()) #前向计算loss=criterion(y_pred,label) #计算损失optimizer.zero_grad() #清除梯度loss.backward() #计算梯度，误差回传optimizer.step() #更新参数total_train_loss.append(loss.data.item())total_valid_loss=[]pred_true_labels=0net.eval()for i,(feature_test,label_test) in enumerate(test_dataloader):feature_test=feature_test.cuda()label_test=label_test.cuda()with torch.no_grad():pred_test=net(feature_test.long())test_loss=criterion(pred_test,label_test)total_valid_loss.append(test_loss.data.item())# accu=torch.sum((torch.argmax(pred_test,dim=1)==label_test)).data.item()/feature_test.shape[0]pred_true_labels+=torch.sum(torch.argmax(pred_test,dim=1)==label_test).data.item()print("epoch:{},train_loss:{},test_loss:{},accuracy:{}".format(epoch,np.mean(total_train_loss),np.mean(total_valid_loss),pred_true_labels/len(test_dataset)))

2.4 模型测试

def predict_one(sentence,net,word2Index):sentence=re.sub("[^\u4e00-\u9fa5]", "", str(sentence))  # 只保留中文print(sentence)sentence=[word2Index[word] if word2Index.get(word) else 0 for word in sentence]sentence=sentence+[0]*(maxlen-len(sentence)) if len(sentence)<maxlen else sentence[0:300]print(sentence)sentence=torch.tensor(np.array(sentence)).view(-1,len(sentence)).cuda()label=torch.argmax(net(sentence),dim=1).data.item()print(label)

sentence="一次很不爽的购物，页面上说是第二天能到货，结果货是从陕西发出的，卖家完全知道第二天根本到不了货。多处提到送货入户还有100%送货入户也没有兑现，与客服联系多日，还是把皮球踢到快递公司。算是一个教训吧。"
predict_one(sentence,net,word2Index)

代码：https://github.com/chongzicbo/nlp-ml-dl-notes/blob/master/code/textclassification/text_cnn.ipynb

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