TextCNN网络结构如图所示：
利用TextCNN做文本分类基本流程（以句子分类为例）：
（1）将句子转成词，利用词建立字典
（2）词转成向量（word2vec，Glove，bert，nn.embedding）
（3）句子补0操作变成等长
（4）建TextCNN模型，训练，测试
TextCNN按照流程的一个例子。
1，预测结果不是很好，句子太少
2，没有用到复杂的word2vec的模型
3，句子太少，没有eval function。
import torch
import torch.nn as nn
import torch.nn.functional as Fsentence = [['The movie is great'],['Tom has a headache today'],['I think the apple is bad'],\['You are so beautiful']]
Label = [1,0,0,1]test_sentence = ['The apple is great']class sentence2id:def __init__(self,sentence):self.sentence = sentenceself.dic = {}self.words = []self.words_num = 0def sen2sen(self,sentence): ##大写转小写senten = []if type(sentence[0])== list:for sen in sentence:sen = sen[0].lower()senten.append([sen]) else:senten.append(sentence)senten = sen2sen(senten)return sentendef countword(self): ##统计单词个数 ############[建库过程不涉及到test模块]#############for sen in self.sentence:sen = sen[0].split(' ') ##空格分隔for word in sen:self.words.append(word.lower())self.words = list(set(self.words))self.words = sorted(self.words)self.words_num = len(self.words)return self.words,self.words_numdef word2id(self): ### 创建词汇表flag = 1for word in self.words:if flag <= self.words_num:self.dic[word] = flagflag += 1#print(self.dic)return self.dicdef sen2id(self,sentence): ###sentence = self.sen2sen(sentence)sentoid = []for sen in sentence:senten = []for word in sen[0].split():senten.append(self.dic[word])sentoid.append(senten)return sentoiddef padded(sentence,pad_token): #token'<pad>'max_len = len(sentence[0])for i in range(0,len(sentence)-1):if max_len < len(sentence[i+1]):max_len = len(sentence[i+1])i += 1for i in range(0,len(sentence)):for j in range(0,max_len-len(sentence[i])):sentence[i].append(pad_token)return sentenceclass ModelEmbeddings(nn.Module):def __init__(self,words_num,embed_size,pad_token): super(ModelEmbeddings, self).__init__()self.words_num = words_numself.embed_size = embed_sizeself.Embedding = nn.Embedding(words_num,embed_size,pad_token)class textCNN(nn.Module):def __init__(self,words_num,embed_size,class_num,dropout_rate=0.1):super(textCNN, self).__init__()self.words_num = words_numself.embed_size = embed_size self.class_num = class_numself.conv1 = nn.Conv2d(1,3,(2,self.embed_size)) ###in_channels, out_channels, kernel_sizeself.conv2 = nn.Conv2d(1,3,(3,self.embed_size)) self.conv3 = nn.Conv2d(1,3,(4,self.embed_size))self.max_pool1 = nn.MaxPool1d(5)self.max_pool2 = nn.MaxPool1d(4)self.max_pool3 = nn.MaxPool1d(3)self.dropout = nn.Dropout(dropout_rate)self.linear = nn.Linear(3*3*1,class_num)# 3 -> out_channels 3 ->kernel_size 1 ->max_pooldef forward(self,sen_embed): #(batch,max_len,embed_size)sen_embed = sen_embed.unsqueeze(1) #(batch,in_channels,max_len,embed_size)conv1 = F.relu(self.conv1(sen_embed)) # ->(batch_size,out_channels.size,1)conv2 = F.relu(self.conv2(sen_embed))conv3 = F.relu(self.conv3(sen_embed))conv1 = torch.squeeze(conv1,dim=3)conv2 = torch.squeeze(conv2,dim=3)conv3 = torch.squeeze(conv3,dim=3)x1 = self.max_pool1(conv1)x2 = self.max_pool2(conv2)x3 = self.max_pool3(conv3)x = torch.cat((x1,x2),dim=1)x = torch.cat((x,x3),dim=1).squeeze(dim=2)output = self.linear(self.dropout(x))return output def train(model,sentence,label):optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)criterion = nn.CrossEntropyLoss()steps = 0best_acc = 0model.train()print ("-"*80)print('Training....')for epoch in range(1,2): ##2个epochfor step,x in enumerate(torch.split(sentence,1,dim=0)):target = torch.zeros(1)target[0] = label[step]target = torch.tensor(target,dtype=torch.long)optimizer.zero_grad()output = model(x)loss = criterion(output, target)#loss.backward()loss.backward(retain_graph=True)optimizer.step()if step % 2 == 0:result = torch.max(output,1)[1].view(target.size())corrects = (result.data == target.data).sum()accuracy = corrects*100.0/1 ####1 is batch size print('Epoch:',epoch,'step:',step,'- loss: %.6f'% loss.data.item(),\'acc: %.4f'%accuracy)return modelif __name__ == '__main__':test = sentence2id(sentence)test.sen2sen(sentence)word,words_num = test.countword()test.word2id()sen_train = test.sen2id(sentence)sen_test = test.sen2id(test_sentence)X_train = torch.LongTensor((padded(sen_train,0)))X_test = torch.LongTensor((padded(sen_test,0)))Embedding = ModelEmbeddings(words_num+1,10,0)X_train_embed = Embedding.Embedding(X_train)X_test_embed = Embedding.Embedding(X_test)#print(X_train_embed.size())#print(X_test_embed.size())## TextCNNtextcnn = textCNN(words_num,10,2)model = train(textcnn,sen_embed,Label) print(torch.max(model(X_test_embed),1)[1])
其中建立卷积层时，可以采用nn.ModuleList()，因为用起来不熟练，就直接展开了。等学好了，再补。