深度学习案例教程 课件6.6实践任务新闻主题分类

第六节

实践任务-新闻主题分类本章的实践任务是关于新闻主题的分类，以一段新闻报道中的文本描述内容为输入，使用模型帮助我们判断它最有可能属于哪一种类型的新闻，这是典型的文本分类问题，我们这里假定每种类型是互斥的，即文本描述有且只有一种类型。实践任务第六节

实践任务-新闻主题分类AG_NEWS数据集是一个包含了100多万篇文章的集合。学术社区为了数据挖掘、信息检索、数据压缩等方面的学术研究整理了这个数据集。AG_NEWS数据集有4个标签，即模型中的数据分类数量为4，这4个标签为：World、Sports、Business和Sci/Tec。Torchtext库提供了一些原始数据迭代器，这些迭代器可以查看原始的字符串数据。例如，AG_NEWS数据集迭代器可以生成包含文本和标签的元组。实践1数据处理importtorchfromtorchtext.datasetsimportAG_NEWStrain_iter=iter(AG_NEWS(split='train'))print(next(train_iter))代码实现实践任务1-数据处理【结果】(3,"WallSt.BearsClawBackIntotheBlack(Reuters)Reuters-Short-sellers,WallStreet'sdwindling\\bandofultra-cynics,areseeinggreen

again.")实践任务1-数据处理tokenizer=get_tokenizer('basic_english') train_iter=AG_NEWS(split='train') #分词生成器defyield_tokens(data_iter):for_,textindata_iter:yieldtokenizer(text)vocab=build_vocab_from_iterator(yield_tokens(train_iter),specials=["<unk>"])vocab.set_default_index(vocab["<unk>"])print(vocab(['here','is','an','example']))

#结果：[475,21,30,5297]代码实现实践任务1-数据处理tokenizer=get_tokenizer('basic_english') train_iter=AG_NEWS(split='train') #分词生成器defyield_tokens(data_iter):for_,textindata_iter:yieldtokenizer(text)vocab=build_vocab_from_iterator(yield_tokens(train_iter),specials=["<unk>"])vocab.set_default_index(vocab["<unk>"])print(vocab(['here','is','an','example']))

#结果：[475,21,30,5297]代码实现实践任务1-数据处理tokenizer=get_tokenizer('basic_english') train_iter=AG_NEWS(split='train') #分词生成器defyield_tokens(data_iter):for_,textindata_iter:yieldtokenizer(text)vocab=build_vocab_from_iterator(yield_tokens(train_iter),specials=["<unk>"])vocab.set_default_index(vocab["<unk>"])print(vocab(['here','is','an','example']))

#结果：[475,21,30,5297]代码实现实践任务1-数据处理tokenizer=get_tokenizer('basic_english') train_iter=AG_NEWS(split='train') #分词生成器defyield_tokens(data_iter):for_,textindata_iter:yieldtokenizer(text)vocab=build_vocab_from_iterator(yield_tokens(train_iter),specials=["<unk>"])vocab.set_default_index(vocab["<unk>"])print(vocab(['here','is','an','example']))

#结果：[475,21,30,5297]代码实现实践任务1-数据处理tokenizer=get_tokenizer('basic_english') train_iter=AG_NEWS(split='train') #分词生成器defyield_tokens(data_iter):for_,textindata_iter:yieldtokenizer(text)vocab=build_vocab_from_iterator(yield_tokens(train_iter),specials=["<unk>"])vocab.set_default_index(vocab["<unk>"])print(vocab(['here','is','an','example']))

#结果：[475,21,30,5297]代码实现实践任务1-数据处理tokenizer=get_tokenizer('basic_english') train_iter=AG_NEWS(split='train') #分词生成器defyield_tokens(data_iter):for_,textindata_iter:yieldtokenizer(text)vocab=build_vocab_from_iterator(yield_tokens(train_iter),specials=["<unk>"])vocab.set_default_index(vocab["<unk>"])print(vocab(['here','is','an','example']))

#结果：[475,21,30,5297]代码实现实践任务-新闻主题分类文本分类模型是由PyTorch库中的nn.EmbeddingBag层和一个用于分类的线性连接层组成。nn.EmbeddingBag在默认状态下计算所有词向量的平均值。虽然，文本文章由不同的长度，但是nn.EmbeddingBag模型不需要填充，因为文本的长度保存在偏移量中了。实践2模型搭建实践任务-新闻主题分类classTextClassificationModel(nn.Module):def__init__(self,vocab_size,embed_dim,num_class):super(TextClassificationModel,self).__init__()self.embedding=nn.EmbeddingBag(vocab_size,embed_dim,sparse=True)self.fc=nn.Linear(embed_dim,num_class)self.init_weights()definit_weights(self):initrange=0.5self.embedding.weight.data.uniform_(-initrange,initrange)self.fc.weight.data.uniform_(-initrange,initrange)self.fc.bias.data.zero_()代码实现实践任务-新闻主题分类classTextClassificationModel(nn.Module):def__init__(self,vocab_size,embed_dim,num_class):super(TextClassificationModel,self).__init__()self.embedding=nn.EmbeddingBag(vocab_size,embed_dim,sparse=True)self.fc=nn.Linear(embed_dim,num_class)self.init_weights()definit_weights(self):initrange=0.5self.embedding.weight.data.uniform_(-initrange,initrange)self.fc.weight.data.uniform_(-initrange,initrange)self.fc.bias.data.zero_()代码实现实践任务-新闻主题分类classTextClassificationModel(nn.Module):def__init__(self,vocab_size,embed_dim,num_class):super(TextClassificationModel,self).__init__()self.embedding=nn.EmbeddingBag(vocab_size,embed_dim,sparse=True)self.fc=nn.Linear(embed_dim,num_class)self.init_weights()definit_weights(self):initrange=0.5self.embedding.weight.data.uniform_(-initrange,initrange)self.fc.weight.data.uniform_(-initrange,initrange)self.fc.bias.data.zero_()代码实现实践任务-新闻主题分类defforward(self,text,offsets):embedded=self.embedding(text,offsets)returnself.fc(embedded)deftrain(dataloader):model.train()total_acc,total_count=0,0log_interval=500start_time=time.time()代码实现实践任务-新闻主题分类defforward(self,text,offsets):embedded=self.embedding(text,offsets)returnself.fc(embedded)deftrain(dataloader):model.train()total_acc,total_count=0,0log_interval=500start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类foridx,(label,text,offsets)inenumerate(dataloader):optimizer.zero_grad()predicted_label=model(text,offsets)loss=criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)optimizer.step()total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=

label.size(0)ifidx%log_interval==0andidx>0:elapsed=time.time()-start_timeprint('|epoch{:3d}|{:5d}/{:5d}batches''|accuracy{:8.3f}'.format(epoch,idx,len(dataloader),total_acc/total_count))total_acc,total_count=0,0start_time=time.time()代码实现实践任务-新闻主题分类defevaluate(dataloader):model.eval()total_acc,total_count=0,0withtorch.no_grad():foridx,(label,text,offsets)inenumerate(dataloader):predicted_label=model(text,offsets)loss=criterion(predicted_label,label)total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=label.size(0)returntotal_acc/total_count实践任务-新闻主题分类defevaluate(dataloader):model.eval()total_acc,total_count=0,0withtorch.no_grad():foridx,(label,text,offsets)inenumerate(dataloader):predicted_label=model(text,offsets)loss=criterion(predicted_label,label)total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=label.size(0)returntotal_acc/total_count实践任务-新闻主题分类defevaluate(dataloader):model.eval()total_acc,total_count=0,0withtorch.no_grad():foridx,(label,text,offsets)inenumerate(dataloader):predicted_label=model(text,offsets)loss=criterion(predicted_label,label)total_acc+=(predicted_label.argmax(1)==label).sum().item()total_count+=label.size(0)returntotal_acc/total_count实践任务-新闻主题分类因为原始的AG_NEWS没有测试数据集，我们将数据集拆分为训练集和测试集，其中训练集占比为95%，测试集占5%。这里使用PyTorch核心库中torch.utils.data.dataset.random_split来拆分数据。CrossEntropyLoss在一个类中结合了nn.LogSoftmax和nn.NLLLoss，当想训练一个有多个类别的分类模型的时候非常实用。SGD作为优化器实现了随机梯度下降。初始的学习率为5，使用StepLR在学习的过程中调整学习率。实践3模型训练实践任务-新闻主题分类#超参数EPOCHS=10LR=5BATCH_SIZE=64criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR)scheduler=torch.optim.lr_scheduler.StepLR(optimizer,1.0,gamma=0.1)total_accu=Nonetrain_iter,test_iter=AG_NEWS()train_dataset=to_map_style_dataset(train_iter)test_dataset=to_map_style_dataset(test_iter)num_train=int(len(train_dataset)*0.95)split_train_,split_valid_=\random_split(train_dataset,[num_train,len(train_dataset)-num_train])代码实现实践任务-新闻主题分类#超参数EPOCHS=10LR=5BATCH_SIZE=64criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR)scheduler=torch.optim.lr_scheduler.StepLR(optimizer,1.0,gamma=0.1)total_accu=Nonetrain_iter,test_iter=AG_NEWS()train_dataset=to_map_style_dataset(train_iter)test_dataset=to_map_style_dataset(test_iter)num_train=int(len(train_dataset)*0.95)split_train_,split_valid_=\random_split(train_dataset,[num_train,len(train_dataset)-num_train])代码实现实践任务-新闻主题分类#超参数EPOCHS=10LR=5BATCH_SIZE=64criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR)scheduler=torch.optim.lr_scheduler.StepLR(optimizer,1.0,gamma=0.1)total_accu=Nonetrain_iter,test_iter=AG_NEWS()train_dataset=to_map_style_dataset(train_iter)test_dataset=to_map_style_dataset(test_iter)num_train=int(len(train_dataset)*0.95)split_train_,split_valid_=\random_split(train_dataset,[num_train,len(train_dataset)-num_train])代码实现实践任务-新闻主题分类#超参数EPOCHS=10LR=5BATCH_SIZE=64criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(),lr=LR)scheduler=torch.optim.lr_scheduler.StepLR(optimizer,1.0,gamma=0.1)total_accu=Nonetrain_iter,test_iter=AG_NEWS()train_dataset=to_map_style_dataset(train_iter)test_dataset=to_map_style_dataset(test_iter)num_train=int(len(train_dataset)*0.95)split_train_,split_valid_=\random_split(train_dataset,[num_train,len(train_dataset)-num_train])代码实现实践任务-新闻主题分类train_dataloader=DataLoader(split_train_,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)valid_dataloader=DataLoader(split_valid_,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)test_dataloader=DataLoader(test_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)forepochinrange(1,EPOCHS+1):epoch_start_time=time.time()train(train_dataloader)accu_val=evaluate(valid_dataloader)iftotal_accuisnotNoneandtotal_accu>accu_val:scheduler.step()else:total_accu=accu_val代码实现实践任务-新闻主题分类train_dataloader=DataLoader(split_train_,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)valid_dataloader=DataLoader(split_valid_,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)test_dataloader=DataLoader(test_dataset,batch_size=BATCH_SIZE,shuffle=True,collate_fn=collate_batch)forepochinrange(1,EPOCHS+1):epoch_start_time=time.time()train(train_dataloader)accu_val=evaluate(valid_dataloader)iftotal_accuisnotNoneandtotal_accu>accu_val:scheduler.step()else:total_accu=accu_val代码实现实践任务-新闻主题分类在前面已经定义了评估模型准确率的函数evaluate，下面使用模型来测试一则新闻，代码如下：ag_news_label={1:"World",2:"Sports",3:"