• 🍨 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

目录

一、准备工作

1.任务说明

 文本分类流程图：

 2.加载数据

​编辑 二、数据的预处理

1.构建词典

2.生成数据批次和迭代器

三、模型构建

四、训练模型

五、小结

---

一、准备工作

1.任务说明

本次将使用PyTorch实现中文文本分类。主要代码与N1周基本一致，不同的是本次任务中使用了本地的中文数据，数据示例如下:

本周任务:

1.学习如何进行中文本文预处理

2.根据文本内容(第1列)预测文本标签(第2列)

进阶任务:

1.尝试根据第一周的内容独立实现，尽可能的不看本文的代码

2.构建更复杂的网络模型，将准确率提升至91% 

 文本分类流程图：

 2.加载数据

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

warnings.filterwarnings("ignore")   #忽略警告信息

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

import pandas as pd
 #加载自定义中文数据
train_data = pd.read_csv('./train.csv',sep='\t',header = None)
#构造数据集迭代器
def coustom_data_iter(texts,labels):
 for x,y in zip(texts,labels):
  yield x,y
 
train_iter =coustom_data_iter(train_data[0].values[:],train_data[1].values[:])

输出： 

 二、数据的预处理

1.构建词典

#构建词典
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
import jieba

#中文分词方法
tokenizer = jieba.lcut
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，如果找不到单词，则会选择默认索引
vocab(['我','想','看','和平','精英','上','战神','必备','技巧','的','游戏','视频'])
label_name = list(set(train_data[1].values[:]))
print(label_name)
text_pipeline = lambda x : vocab(tokenizer(x))
label_pipeline = lambda x : label_name.index(x)

print(text_pipeline('我想看和平精英上战神必备技巧的游戏视频'))
print(label_pipeline('Video-Play'))

输出：

2.生成数据批次和迭代器

#生成数据批次和迭代器
from torch.utils.data import DataLoader

def collate_batch(batch):
    label_list, text_list, offsets = [],[],[0]         
    for(_text, _label) 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
        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 text_list.to(device), label_list.to(device), offsets.to(device)

#数据加载器
dataloader = DataLoader(
    train_iter,
    batch_size = 8,
    shuffle = False,
    collate_fn = collate_batch
)

三、模型构建

#搭建模型
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)
#初始化模型
#定义实例
num_class = len(label_name)
vocab_size = len(vocab)
em_size = 64
model = TextClassificationModel(vocab_size, em_size, num_class).to(device)
#定义训练与评估函数
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 = 50
    start_time = time.time()
    for idx, (text,label, 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，label为真
        loss.backward()                                   #反向传播
        torch.nn.utils.clip_grad_norm_(model.parameters(),0.1)  #梯度裁剪
        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{:d}|{: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
            staet_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,(text,label,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

四、训练模型

#拆分数据集并运行模型
from torch.utils.data.dataset   import random_split
from torchtext.data.functional  import to_map_style_dataset

# 超参数设定
EPOCHS      = 10   #epoch
LR          = 5    #learningRate
BATCH_SIZE  = 64   #batch size for training

#设置损失函数、选择优化器、设置学习率调整函数
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

# 构建数据集
train_iter = custom_data_iter(train_data[0].values[:],train_data[1].values[:])
train_dataset   = to_map_style_dataset(train_iter)
split_train_, split_valid_ = random_split(train_dataset,
                                         [int(len(train_dataset)*0.8),int(len(train_dataset)*0.2)])

                                           
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)

for epoch in range(1, EPOCHS + 1):
    epoch_start_time = time.time()
    train(train_dataloader)
    val_acc, val_loss = evaluate(valid_dataloader)
    #获取当前的学习率
    lr = optimizer.state_dict()['param_groups'][0]['lr']
    if total_accu is not None and total_accu > val_acc:
        scheduler.step()
    else:
        total_accu = val_acc
    print('-' * 69)
    print('| epoch {:d} | 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)
test_acc,test_loss = evaluate(valid_dataloader)
print('模型准确率为：{:5.4f}'.format(test_acc))
#测试指定的数据
def predict(text, text_pipeline):
    with torch.no_grad():
        text = torch.tensor(text_pipeline(text))
        output = model(text, torch.tensor([0]))
        return output.argmax(1).item()

ex_text_str = "还有双鸭山到淮阴的汽车票吗13号的"
model = model.to("cpu")

print("该文本的类别是: %s" %label_name[predict(ex_text_str,text_pipeline)])

输出：

|epoch1|  50/ 152 batches|train_acc0.431 train_loss0.03045
|epoch1| 100/ 152 batches|train_acc0.700 train_loss0.01936
|epoch1| 150/ 152 batches|train_acc0.768 train_loss0.01370
---------------------------------------------------------------------
| epoch 1 | time:1.58s | valid_acc 0.789 valid_loss 0.012
---------------------------------------------------------------------
|epoch2|  50/ 152 batches|train_acc0.818 train_loss0.01030
|epoch2| 100/ 152 batches|train_acc0.831 train_loss0.00932
|epoch2| 150/ 152 batches|train_acc0.850 train_loss0.00811
---------------------------------------------------------------------
| epoch 2 | time:1.47s | valid_acc 0.837 valid_loss 0.008
---------------------------------------------------------------------
|epoch3|  50/ 152 batches|train_acc0.870 train_loss0.00688
|epoch3| 100/ 152 batches|train_acc0.887 train_loss0.00658
|epoch3| 150/ 152 batches|train_acc0.893 train_loss0.00575
---------------------------------------------------------------------
| epoch 3 | time:1.46s | valid_acc 0.866 valid_loss 0.007
---------------------------------------------------------------------
|epoch4|  50/ 152 batches|train_acc0.906 train_loss0.00507
|epoch4| 100/ 152 batches|train_acc0.918 train_loss0.00468
|epoch4| 150/ 152 batches|train_acc0.915 train_loss0.00478
---------------------------------------------------------------------
| epoch 4 | time:1.47s | valid_acc 0.886 valid_loss 0.006
---------------------------------------------------------------------
|epoch5|  50/ 152 batches|train_acc0.938 train_loss0.00378
|epoch5| 100/ 152 batches|train_acc0.935 train_loss0.00379
|epoch5| 150/ 152 batches|train_acc0.932 train_loss0.00376
---------------------------------------------------------------------
| epoch 5 | time:1.51s | valid_acc 0.890 valid_loss 0.006
---------------------------------------------------------------------
|epoch6|  50/ 152 batches|train_acc0.951 train_loss0.00310
|epoch6| 100/ 152 batches|train_acc0.952 train_loss0.00287
|epoch6| 150/ 152 batches|train_acc0.950 train_loss0.00289
---------------------------------------------------------------------
| epoch 6 | time:1.50s | valid_acc 0.894 valid_loss 0.006
---------------------------------------------------------------------
|epoch7|  50/ 152 batches|train_acc0.963 train_loss0.00233
|epoch7| 100/ 152 batches|train_acc0.963 train_loss0.00244
|epoch7| 150/ 152 batches|train_acc0.965 train_loss0.00222
---------------------------------------------------------------------
| epoch 7 | time:1.49s | valid_acc 0.898 valid_loss 0.005
---------------------------------------------------------------------
|epoch8|  50/ 152 batches|train_acc0.975 train_loss0.00183
|epoch8| 100/ 152 batches|train_acc0.976 train_loss0.00176
|epoch8| 150/ 152 batches|train_acc0.971 train_loss0.00188
---------------------------------------------------------------------
| epoch 8 | time:1.67s | valid_acc 0.900 valid_loss 0.005
---------------------------------------------------------------------
|epoch9|  50/ 152 batches|train_acc0.982 train_loss0.00145
|epoch9| 100/ 152 batches|train_acc0.982 train_loss0.00139
|epoch9| 150/ 152 batches|train_acc0.980 train_loss0.00141
---------------------------------------------------------------------
| epoch 9 | time:2.05s | valid_acc 0.901 valid_loss 0.006
---------------------------------------------------------------------
|epoch10|  50/ 152 batches|train_acc0.990 train_loss0.00108
|epoch10| 100/ 152 batches|train_acc0.984 train_loss0.00119
|epoch10| 150/ 152 batches|train_acc0.986 train_loss0.00105
---------------------------------------------------------------------
| epoch 10 | time:1.98s | valid_acc 0.900 valid_loss 0.005
---------------------------------------------------------------------
模型准确率为：0.8996
该文本的类别是: Travel-Query

五、小结

• 数据加载：
  • 定义一个生成器函数，将文本和标签成对迭代。这是为了后续的数据处理和加载做准备。
• 分词与词汇表：
  • 使用jieba进行中文分词，jieba.lcut可以将中文文本切割成单个词语列表。
  • 使用torchtext的build_vocab_from_iterator从分词后的文本中构建词汇表，并设置默认索引为<unk>，表示未知词汇。这对处理未见过的词汇非常重要。
• 数据管道：创建文本和标签处理管道。

  • 创建两个处理管道：

  • text_pipeline：将文本转换为词汇表中的索引。
  • label_pipeline：将标签转换为索引。
• 模型构建：定义带嵌入层和全连接层的文本分类模型。
  • 定义一个文本分类模型TextClassificationModel，包括一个嵌入层nn.EmbeddingBag和一个全连接层nn.Linear。nn.EmbeddingBag在处理变长序列时性能较好，因为它不需要明确的填充操作。