>- **🍨 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/rbOOmire8OocQ90QM78DRA) 中的学习记录博客** >- **🍖 原作者：[K同学啊 | 接辅导、项目定制](https://mtyjkh.blog.csdn.net/)**

import  torch
import torch.nn as nn
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warnings



warnings.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 DataLoader

def 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 =0.5
        self.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 =0,0,0
    log_interval =500
    start_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loss
        total_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_time
            print('|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 =0,0,0
            start_time =time.time()

def evaluate(dataloader):
    model.eval()  #切换为测试模式
    total_acc,train_loss,total_count =0,0,0

    with 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_count



from 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 =None

train_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_acc
    print('-'*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))

文本构建向量的基本原理：

下面是运行结果：

D:\Code\pythonProject_PyTorch\venv\Scripts\python.exe D:\Code\pythonProject_PyTorch\PytorchText.py 
[475, 21, 30, 5297]
[475, 21, 30, 5297]
9
|epoch 1| 500/1782batches|train_acc 0.721train_loss 0.01110
|epoch 1|1000/1782batches|train_acc 0.871train_loss 0.00606
|epoch 1|1500/1782batches|train_acc 0.877train_loss 0.00562
---------------------------------------------------------------------
|epoch 1|time:11.86s|valid_acc 0.782valid_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
---------------------------------------------------------------------
|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
---------------------------------------------------------------------
|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
---------------------------------------------------------------------
|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
---------------------------------------------------------------------
|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
---------------------------------------------------------------------
|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:11.57s|valid_acc 0.914valid_loss 0.004
---------------------------------------------------------------------
|epoch 10| 500/1782batches|train_acc 0.950train_loss 0.00246
|epoch 10|1000/1782batches|train_acc 0.950train_loss 0.00243
|epoch 10|1500/1782batches|train_acc 0.949train_loss 0.00249
---------------------------------------------------------------------
|epoch 10|time:11.74s|valid_acc 0.914valid_loss 0.004
---------------------------------------------------------------------
Checking the results of test dataset.
test accuracy    0.909

Process finished with exit code 0

总结：PyTorch version、torchtext version、Supported Python version版本一定要对应，可以参考：https://blog.csdn.net/shiwanghualuo/article/details/122860521