网站建设人员管理制度,企业型网站建设怎样收费,模板建站能建个门户网站吗,宠物网站开发与实现结论中文文本分类-Pytorch实现 #x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 一、准备工作
1. 任务说明
本次使用Pytorch实现中文文本分类。主要代码与文本分类代码基本一致#xff0c;不同的是本次任务使用…中文文本分类-Pytorch实现 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 一、准备工作
1. 任务说明
本次使用Pytorch实现中文文本分类。主要代码与文本分类代码基本一致不同的是本次任务使用了本地的中文数据数据示例如下
2.加载数据
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms,datasets
import os,PIL,pathlib,warningswarnings.filterwarnings(ignore) #忽略警告信息device torch.device(cuda if torch.cuda.is_available() else cpu)
device输出
device(typecpu)import pandas as pd#加载自定义中文数据
train_data pd.read_csv(./train.csv,sep\t,header None)
train_data.head()输出
#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[:])二、数据预处理
#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])输出
[2, 10, 13, 973, 1079, 146, 7724, 7574, 7793, 1, 186, 28]label_name list(set(train_data[1].values[:]))
print(label_name)输出
[FilmTele-Play, Alarm-Update, Weather-Query, Audio-Play, Radio-Listen, Travel-Query, Music-Play, Video-Play, HomeAppliance-Control, Calendar-Query, TVProgram-Play, Other]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, 10, 13, 973, 1079, 146, 7724, 7574, 7793, 1, 186, 28]
7lambda表达式的语法为lambda arguments: expression 其中arguments是函数的参数可以有多个参数用逗号分隔。expression是一个表达式它定义了函数的返回值。 text_pipeline函数 将原始文本数据转换为整数列表使用了之前构建的vocab词表和tokenizer分词器函数。具体步骤 接受一个字符串x作为输入使用tokenizer将其分词将每个词在vocab词表中的索引放入一个列表返回 label_pipeline函数 将原始标签数据转换为整数它接受一个字符串x作为输入并使用 label_index.index(x) 方法获取x在label_name列表中的索引作为输出。 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 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), dtypetorch.int64)text_list.append(processed_text)#偏移量offsets.append(processed_text.size(0))label_list torch.tensor(label_list,dtypetorch.int64)text_list torch.cat(text_list)offsets torch.tensor(offsets[:-1]).cumsum(dim0) #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
1. 搭建模型
#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sparseFalse) #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)2. 初始化模型
#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)3. 定义训练与评估函数
#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{: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 0,0,0staet_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
#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_accprint(- * 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)输出
[还有双鸭山到淮阴的汽车票吗13号的 从这里怎么回家 随便播放一首专辑阁楼里的佛里的歌 ...黎耀祥陈豪邓萃雯畲诗曼陈法拉敖嘉年杨怡马浚伟等到场出席 百事盖世群星星光演唱会有谁 下周一视频会议的闹钟帮我开开]
|epoch1| 50/ 152 batches|train_acc0.953 train_loss0.00282
|epoch1| 100/ 152 batches|train_acc0.953 train_loss0.00271
|epoch1| 150/ 152 batches|train_acc0.952 train_loss0.00292
---------------------------------------------------------------------
| epoch 1 | time:5.50s | valid_acc 0.949 valid_loss 0.003
---------------------------------------------------------------------
|epoch2| 50/ 152 batches|train_acc0.961 train_loss0.00231
|epoch2| 100/ 152 batches|train_acc0.967 train_loss0.00204
|epoch2| 150/ 152 batches|train_acc0.963 train_loss0.00228
---------------------------------------------------------------------
| epoch 2 | time:5.06s | valid_acc 0.949 valid_loss 0.003
---------------------------------------------------------------------
|epoch3| 50/ 152 batches|train_acc0.975 train_loss0.00173
|epoch3| 100/ 152 batches|train_acc0.973 train_loss0.00177
|epoch3| 150/ 152 batches|train_acc0.972 train_loss0.00166
---------------------------------------------------------------------
| epoch 3 | time:5.07s | valid_acc 0.948 valid_loss 0.003
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|epoch4| 50/ 152 batches|train_acc0.984 train_loss0.00137
|epoch4| 100/ 152 batches|train_acc0.987 train_loss0.00123
|epoch4| 150/ 152 batches|train_acc0.983 train_loss0.00119
---------------------------------------------------------------------
| epoch 4 | time:5.07s | valid_acc 0.950 valid_loss 0.003
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|epoch5| 50/ 152 batches|train_acc0.985 train_loss0.00125
|epoch5| 100/ 152 batches|train_acc0.987 train_loss0.00119
|epoch5| 150/ 152 batches|train_acc0.986 train_loss0.00120
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| epoch 5 | time:5.03s | valid_acc 0.949 valid_loss 0.003
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|epoch6| 50/ 152 batches|train_acc0.985 train_loss0.00118
|epoch6| 100/ 152 batches|train_acc0.989 train_loss0.00114
|epoch6| 150/ 152 batches|train_acc0.985 train_loss0.00120
---------------------------------------------------------------------
| epoch 6 | time:5.40s | valid_acc 0.949 valid_loss 0.003
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|epoch7| 50/ 152 batches|train_acc0.984 train_loss0.00119
|epoch7| 100/ 152 batches|train_acc0.986 train_loss0.00119
|epoch7| 150/ 152 batches|train_acc0.989 train_loss0.00112
---------------------------------------------------------------------
| epoch 7 | time:5.71s | valid_acc 0.949 valid_loss 0.003
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|epoch8| 50/ 152 batches|train_acc0.985 train_loss0.00115
|epoch8| 100/ 152 batches|train_acc0.986 train_loss0.00128
|epoch8| 150/ 152 batches|train_acc0.989 train_loss0.00107
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| epoch 8 | time:5.22s | valid_acc 0.949 valid_loss 0.003
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|epoch9| 50/ 152 batches|train_acc0.988 train_loss0.00114
|epoch9| 100/ 152 batches|train_acc0.983 train_loss0.00127
|epoch9| 150/ 152 batches|train_acc0.989 train_loss0.00109
---------------------------------------------------------------------
| epoch 9 | time:5.28s | valid_acc 0.949 valid_loss 0.003
---------------------------------------------------------------------
|epoch10| 50/ 152 batches|train_acc0.986 train_loss0.00115
|epoch10| 100/ 152 batches|train_acc0.987 train_loss0.00117
|epoch10| 150/ 152 batches|train_acc0.986 train_loss0.00119
---------------------------------------------------------------------
| epoch 10 | time:5.22s | valid_acc 0.949 valid_loss 0.003
---------------------------------------------------------------------test_acc,test_loss evaluate(valid_dataloader)
print(模型准确率为{:5.4f}.format(test_acc))输出
模型准确率为0.94922. 测试指定数据
#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
该文本的类别是: Travel-Query五、总结 训练神经网络时可使用梯度裁剪的方法来防止梯度爆炸使得模型训练更加稳定
