第N2周：中文文本分类-Pytorch实现

一、前言

🍨 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

● 难度：夯实基础⭐⭐
● 语言：Python3、Pytorch3
● 时间：4月23日-4月28日
🍺要求：
1、熟悉NLP的基础知识

二、准备工作

环境搭建
Python 3.8
pytorch == 1.8.1
torchtext == 0.9.1

三、数据预处理

1.加载数据

import torch
import torch.nn as nn
import os,PIL,pathlib,warningswarnings.filterwarnings("ignore")             #忽略警告信息# win10系统
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
import pandas as pd# 加载自定义中文数据
train_data = pd.read_csv('./data/train.csv', sep='\t', header=None)
train_data.head()
# 构造数据集迭代器
def coustom_data_iter(texts, labels):for x, y in zip(texts, labels):yield x, ytrain_iter = coustom_data_iter(train_data[0].values[:], train_data[1].values[:])

2.构建词典

from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
# conda install jieba -y
import jieba# 中文分词方法
tokenizer = jieba.lcutdef 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'))

3.生成数据批次和迭代器

from torch.utils.data import DataLoaderdef 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，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 text_list.to(device),label_list.to(device), offsets.to(device)# 数据加载器，调用示例
dataloader = DataLoader(train_iter,batch_size=8,shuffle   =False,collate_fn=collate_batch)

三、模型构建

1. 搭建模型

from torch import nnclass 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) # 初始化权重self.fc.weight.data.uniform_(-initrange, initrange)        self.fc.bias.data.zero_()                                  # 偏置值归零def forward(self, text, offsets):embedded = self.embedding(text, offsets)return self.fc(embedded)

2. 初始化模型

num_class  = len(label_name)
vocab_size = len(vocab)
em_size    = 64
model      = TextClassificationModel(vocab_size, em_size, num_class).to(device)

3. 定义训练与评估函数

import timedef train(dataloader):model.train()  # 切换为训练模式total_acc, train_loss, total_count = 0, 0, 0log_interval = 50start_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为真实值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, (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

1. 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
EPOCHS     = 10 # epoch
LR         = 5  # 学习率
BATCH_SIZE = 64 # batch size for trainingcriterion = 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 = coustom_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_accprint('-' * 69)print('| epoch {:1d} | time: {:4.2f}s | ''valid_acc {:4.3f} valid_loss {:4.3f} | lr {:4.6f}'.format(epoch,time.time() - epoch_start_time,val_acc,val_loss,lr))print('-' * 69)