第N3周：Pytorch文本分类入门

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊 | 接辅导、项目定制
🚀 文章来源：K同学的学习圈子

这里借用K同学的一张图片大致说明本次任务流程。
在这里插入图片描述

1.本次所用AG News数据集介绍

AG News数据集是一个用于文本分类任务的广泛使用的数据集，包含了来自AG新闻网站的四个类别的新闻文章。这些类别分别是：World (世界)， Sports (体育)， Business (商业)和 Sci/Tech (科学/技术)。每个类别都包含约30,000篇新闻文章，总共有约120,000篇新闻文章。

AG News数据集是一个用于自然语言处理和机器学习的常用基准数据集，可以用于测试文本分类算法的性能。这个数据集具有以下特点：

大规模：AG News数据集包含大量的新闻文章，可以用于训练深度学习模型。
多样性：数据集中包含不同主题的新闻文章，可以用于测试模型在不同类别上的分类能力。
易于使用：数据集已经被广泛使用，有很多开源项目和教程可以帮助用户开始使用。

AG News数据集可以用于多种自然语言处理任务，例如文本分类、情感分析和主题识别等。

2.`TextClassificationModel`架构

TextClassificationModel类继承自nn.Module，其中包含了__init__方法用来初始化模型的各个组件，init_weights方法用来初始化权重，forward方法定义了数据在模型中的流动方式。
模型包含了一个词嵌入层（embedding）和一个全连接层（fc）。
init_weights方法用来对模型的权重进行初始化。
forward方法接受输入的文本序列（text）和偏移量（offsets），通过词嵌入层得到嵌入表示，然后通过全连接层进行分类预测。
num_class表示分类的类别数量，vocab_size表示词典大小，em_size表示词嵌入的维度。
最后，创建了一个model对象，并将其移动到指定的设备（device）上。

3.代码

import  torch
import torch.nn as nn
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warningswarnings.filterwarnings("ignore")
#win10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")from torchtext.datasets import AG_NEWS
train_iter = AG_NEWS(split='train')#加载 AG News 数据集from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator#返回分词器
tokenizer = get_tokenizer('basic_english')def yield_tokens(data_iter):for _, text in data_iter:yield tokenizer(text)vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])#设置默认索引
print(vocab(['here', 'is', 'an', 'example']))text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
print(text_pipeline('here is an example '))
print(label_pipeline('10'))from torch.utils.data import DataLoaderdef collate_batch(batch):label_list,text_list,offsets =[],[],[0]for(_label,_text)in batch:#标签列表label_list.append(label_pipeline(_label))#文本列表processed_text =torch.tensor(text_pipeline(_text),dtype=torch.int64)text_list.append(processed_text)#偏移量，即语句的总词汇量offsets.append(processed_text.size(0))label_list =torch.tensor(label_list,dtype=torch.int64)text_list=torch.cat(text_list)offsets=torch.tensor(offsets[:-1]).cumsum(dim=0)#返回维度dim中输入元素的累计和return label_list.to(device),text_list.to(device),offsets.to(device)
#数据加载器
dataloader =DataLoader(train_iter,batch_size=8,shuffle   =False,collate_fn=collate_batch)from torch import nn
class TextClassificationModel(nn.Module):def __init__(self,vocab_size,embed_dim,num_class):super(TextClassificationModel,self).__init__()self.embedding =nn.EmbeddingBag(vocab_size,#词典大小embed_dim,#嵌入的维度sparse=False)#self.fc =nn.Linear(embed_dim,num_class)self.init_weights()def init_weights(self):initrange =0.5self.embedding.weight.data.uniform_(-initrange,initrange)self.fc.weight.data.uniform_(-initrange,initrange)self.fc.bias.data.zero_()def forward(self,text,offsets):embedded =self.embedding(text,offsets)return self.fc(embedded)num_class = len(set([label for(label,text)in train_iter]))
vocab_size = len(vocab)
em_size = 64
model = TextClassificationModel(vocab_size,em_size,num_class).to(device)import time
def train(dataloader):model.train()  #切换为训练模式total_acc,train_loss,total_count =0,0,0log_interval =500start_time   =time.time()for idx,(label,text,offsets) in enumerate(dataloader):predicted_label =model(text,offsets)optimizer.zero_grad()#grad属性归零loss =criterion(predicted_label,label)#计算网络输出和真实值之间的差距，labe1为真实值loss.backward()#反向传播optimizer.step()  #每一步自动更新#记录acc与losstotal_acc   +=(predicted_label.argmax(1)==label).sum().item()train_loss  +=loss.item()total_count +=label.size(0)if idx %log_interval ==0 and idx >0:elapsed =time.time()-start_timeprint('|epoch {:1d}|{:4d}/{:4d}batches''|train_acc {:4.3f}train_loss {:4.5f}'.format(epoch,idx,len(dataloader),total_acc/total_count,train_loss/total_count))total_acc,train_loss,total_count =0,0,0start_time =time.time()def evaluate(dataloader):model.eval()  #切换为测试模式total_acc,train_loss,total_count =0,0,0with torch.no_grad():for idx,(label,text,offsets)in enumerate(dataloader):predicted_label =model(text,offsets)loss = criterion(predicted_label,label)  #计算loss值#记录测试数据total_acc   +=(predicted_label.argmax(1)==label).sum().item()train_loss  +=loss.item()total_count +=label.size(0)return total_acc/total_count,train_loss/total_countfrom torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
#超参数
EPOCHS=10 #epoch
LR=5  #学习率
BATCH_SIZE=64 #batch size for training
criterion =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()#加载数据
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])
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)for epoch in range(1,EPOCHS +1):epoch_start_time =time.time()train(train_dataloader)val_acc,val_loss =evaluate(valid_dataloader)if total_accu is not None and total_accu >val_acc:scheduler.step()else:total_accu =val_accprint('-'*69)print('|epoch {:1d}|time:{:4.2f}s|''valid_acc {:4.3f}valid_loss {:4.3f}'.format(epoch,time.time()-epoch_start_time,val_acc,val_loss))print('-'*69)print('Checking the results of test dataset.')
test_acc,test_loss =evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(test_acc))

E:\BaiduNetdiskDownload\pythonProject_PyTorch\venv\Scripts\python.exe E:\BaiduNetdiskDownload\pythonProject_PyTorch\PytorchText.py 
[475, 21, 30, 5297]
[475, 21, 30, 5297]
9
|epoch 1| 500/1782batches|train_acc 0.721train_loss 0.01010
|epoch 1|1000/1782batches|train_acc 0.871train_loss 0.00616
|epoch 1|1500/1782batches|train_acc 0.877train_loss 0.00542
---------------------------------------------------------------------
|epoch 1|time:11.81s|valid_acc 0.794valid_loss 0.009
---------------------------------------------------------------------
|epoch 2| 500/1782batches|train_acc 0.903train_loss 0.00451
|epoch 2|1000/1782batches|train_acc 0.906train_loss 0.00442
|epoch 2|1500/1782batches|train_acc 0.906train_loss 0.00436
---------------------------------------------------------------------
|epoch 2|time:11.64s|valid_acc 0.845valid_loss 0.007
---------------------------------------------------------------------
|epoch 3| 500/1782batches|train_acc 0.919train_loss 0.00374
|epoch 3|1000/1782batches|train_acc 0.917train_loss 0.00383
|epoch 3|1500/1782batches|train_acc 0.915train_loss 0.00393
---------------------------------------------------------------------
|epoch 3|time:11.61s|valid_acc 0.905valid_loss 0.004
---------------------------------------------------------------------
|epoch 4| 500/1782batches|train_acc 0.927train_loss 0.00339
|epoch 4|1000/1782batches|train_acc 0.926train_loss 0.00342
|epoch 4|1500/1782batches|train_acc 0.922train_loss 0.00352
---------------------------------------------------------------------
|epoch 4|time:11.62s|valid_acc 0.870valid_loss 0.006
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|epoch 5| 500/1782batches|train_acc 0.942train_loss 0.00276
|epoch 5|1000/1782batches|train_acc 0.945train_loss 0.00268
|epoch 5|1500/1782batches|train_acc 0.945train_loss 0.00266
---------------------------------------------------------------------
|epoch 5|time:11.67s|valid_acc 0.913valid_loss 0.004
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|epoch 6| 500/1782batches|train_acc 0.946train_loss 0.00259
|epoch 6|1000/1782batches|train_acc 0.946train_loss 0.00261
|epoch 6|1500/1782batches|train_acc 0.946train_loss 0.00261
---------------------------------------------------------------------
|epoch 6|time:11.71s|valid_acc 0.914valid_loss 0.004
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|epoch 7| 500/1782batches|train_acc 0.948train_loss 0.00255
|epoch 7|1000/1782batches|train_acc 0.946train_loss 0.00260
|epoch 7|1500/1782batches|train_acc 0.948train_loss 0.00250
---------------------------------------------------------------------
|epoch 7|time:11.68s|valid_acc 0.912valid_loss 0.004
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|epoch 8| 500/1782batches|train_acc 0.948train_loss 0.00252
|epoch 8|1000/1782batches|train_acc 0.948train_loss 0.00249
|epoch 8|1500/1782batches|train_acc 0.950train_loss 0.00244
---------------------------------------------------------------------
|epoch 8|time:11.52s|valid_acc 0.913valid_loss 0.004
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|epoch 9| 500/1782batches|train_acc 0.949train_loss 0.00249
|epoch 9|1000/1782batches|train_acc 0.950train_loss 0.00246
|epoch 9|1500/1782batches|train_acc 0.950train_loss 0.00248
---------------------------------------------------------------------
|epoch 9|time:11.43s|valid_acc 0.922valid_loss 0.004
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|epoch 10| 500/1782batches|train_acc 0.950train_loss 0.00235
|epoch 10|1000/1782batches|train_acc 0.950train_loss 0.00255
|epoch 10|1500/1782batches|train_acc 0.949train_loss 0.00229
---------------------------------------------------------------------
|epoch 10|time:11.92s|valid_acc 0.924valid_loss 0.004
---------------------------------------------------------------------
Checking the results of test dataset.
test accuracy    0.909Process finished with exit code 0