部门定制网站建设公司,网站建设 010,怎么做磁力网站,网页设计与制作笔记#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 | 接辅导、项目定制#x1f680; 文章来源#xff1a;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#xff1a; 2.加载数据
编辑 二、… 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,warningswarnings.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,ytrain_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 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
#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)
#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 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
#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)
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。
