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- 🍨 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
1.准备工作
1.1.加载数据
import torch
import torch.nn as nn
import torchvision
import os,PIL,warnings
import pandas as pdwarnings.filterwarnings('ignore')
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
#加载自定义中文数据
train_data=pd.read_csv('train.csv',sep='\t',header=None)
train_data.head()
#构建数据集迭代器
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.数据预处理
2.1.构建词典
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
import jiebatokenizer=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>"])
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'))
2.2.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)
2.3.构建数据集
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_datasetBATCH_SIZE=4
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)
3.模型构建
3.1.定义位置编码函数
import math,os,torch
class PositionalEncoding(nn.Module):
def __init__(self,embed_dim,max_len=500):
super(PositionalEncoding,self).__init__()pe=torch.zeros(max_len,embed_dim)
position=torch.arange(0,max_len,dtype=torch.float).unsqueeze(1)div_term=torch.exp(torch.arange(0,embed_dim,2).float()*(-math.log(100.0)/embed_dim))
pe[:,0::2]=torch.sin(position*div_term)
pe[:,1::2]=torch.cos(position*div_term)
pe=pe.unsqueeze(0).transpose(0,1)self.register_buffer('pe',pe)
def forward(self,x):
x=x+self.pe[:x.size(0)]
return x
3.2.定义Transformer模型
from tempfile import TemporaryDirectory
from typing import Tuple
from torch import nn,Tensor
from torch.nn import TransformerEncoder,TransformerEncoderLayer
from torch.utils.data import datasetclass TransformerModel(nn.Module):
def __init__(self,vocab_size,embed_dim,num_class,nhead=8,d_hid=256,nlayers=12,dropout=0.1):
super().__init__()
self.embedding=nn.EmbeddingBag(vocab_size,
embed_dim,
sparse=False)
self.pos_encoder=PositionalEncoding(embed_dim)#定义编码器层
encoder_layers=TransformerEncoderLayer(embed_dim,nhead,d_hid,dropout)
self.transformer_encoder=TransformerEncoder(encoder_layers,nlayers)
self.embed_dim=embed_dim
self.linear=nn.Linear(embed_dim*4,num_class)def forward(self,src,offsets,src_mask=None):
src=self.embedding(src,offsets)
src=self.pos_encoder(src)
output=self.transformer_encoder(src,src_mask)output=output.view(4,embed_dim*4)
output=self.linear(output)return output
3.3.初始化模型
vocab_size=len(vocab)
embed_dim=64
num_class=len(label_name)model=TransformerModel(vocab_size,
embed_dim,
num_class).to(device)
3.4.定义训练函数
import time
def train(dataloader):
model.train()
total_acc,train_loss,total_count=0,0,0
log_interval=300
start_time=time.time()for idx,(text,label,offsets) in enumerate(dataloader):
predicted_label=model(text,offsets)
optimizer.zero_grad()loss=criterion(predicted_label,label)
loss.backward()
optimizer.step()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()
3.5.定义评估函数
def evaluate(dataloader):
model.eval()
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)
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
4.训练模型
4.1.模型训练
EPOCH=10
criterion=torch.nn.CrossEntropyLoss()
optimizer=torch.optim.SGD(model.parameters(),lr=1e-2)for epoch in range(1,EPOCH+1):
epoch_start_time=time.time()
train(train_dataloader)
val_acc,val_loss=evaluate(valid_dataloader)lr=optimizer.state_dict()['param_groups'][0]['lr']
print('-'*68)
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))
4.2.模型评估
test_acc,test_loss=evaluate(valid_dataloader)
print('模型准确率为:{:5.4f}'.format(test_acc))
5.总结
使用 jieba 分词构建词汇表并将文本转换为数值序列，再通过嵌入层（nn.EmbeddingBag）结合自定义的位置编码（PositionalEncoding）将输入嵌入空间映射，随后利用多层 TransformerEncoder 编码文本语义特征，最后通过线性层进行多类别分类，训练过程使用交叉熵损失函数和 SGD 优化器，在模型评估阶段输出准确率和损失值；其中 Transformer 是一种基于注意力机制的深度学习结构