深度学习笔记40_中文文本分类-Pytorch实现

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")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)
print(train_data.head())

                       0              1
0      还有双鸭山到淮阴的汽车票吗13号的   Travel-Query
1                从这里怎么回家   Travel-Query
2       随便播放一首专辑阁楼里的佛里的歌     Music-Play
3              给看一下墓王之王嘛  FilmTele-Play
4  我想看挑战两把s686打突变团竞的游戏视频     Video-Play

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.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print(vocab(['我','想','看','和平','精英','上','战神','必备','技巧','的','游戏','视频']))

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'))

结果：[2, 10, 13, 973, 1079, 146, 7724, 7574, 7793, 1, 186, 28]
4

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)

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)

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

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

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)

Batch [50/152], Loss: 0.0340, Accuracy: 0.4203
Batch [100/152], Loss: 0.0235, Accuracy: 0.5851
Batch [150/152], Loss: 0.0309, Accuracy: 0.6572
---------------------------------------------------------------------
| epoch 1 | time: 0.55s | valid_acc 0.814 valid_loss 0.012 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0104, Accuracy: 0.8165
Batch [100/152], Loss: 0.0099, Accuracy: 0.8215
Batch [150/152], Loss: 0.0092, Accuracy: 0.8329
---------------------------------------------------------------------
| epoch 2 | time: 0.44s | valid_acc 0.855 valid_loss 0.008 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0068, Accuracy: 0.8790
Batch [100/152], Loss: 0.0065, Accuracy: 0.8778
Batch [150/152], Loss: 0.0064, Accuracy: 0.8809
---------------------------------------------------------------------
| epoch 3 | time: 0.44s | valid_acc 0.874 valid_loss 0.007 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0050, Accuracy: 0.9105
Batch [100/152], Loss: 0.0051, Accuracy: 0.9101
Batch [150/152], Loss: 0.0048, Accuracy: 0.9130
---------------------------------------------------------------------
| epoch 4 | time: 0.44s | valid_acc 0.882 valid_loss 0.006 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0039, Accuracy: 0.9366
Batch [100/152], Loss: 0.0039, Accuracy: 0.9339
Batch [150/152], Loss: 0.0038, Accuracy: 0.9350
---------------------------------------------------------------------
| epoch 5 | time: 0.44s | valid_acc 0.896 valid_loss 0.006 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0028, Accuracy: 0.9519
Batch [100/152], Loss: 0.0030, Accuracy: 0.9517
Batch [150/152], Loss: 0.0030, Accuracy: 0.9494
---------------------------------------------------------------------
| epoch 6 | time: 0.44s | valid_acc 0.898 valid_loss 0.005 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0025, Accuracy: 0.9580
Batch [100/152], Loss: 0.0024, Accuracy: 0.9616
Batch [150/152], Loss: 0.0024, Accuracy: 0.9609
---------------------------------------------------------------------
| epoch 7 | time: 0.44s | valid_acc 0.902 valid_loss 0.005 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0018, Accuracy: 0.9764
Batch [100/152], Loss: 0.0019, Accuracy: 0.9739
Batch [150/152], Loss: 0.0019, Accuracy: 0.9724
---------------------------------------------------------------------
| epoch 8 | time: 0.44s | valid_acc 0.900 valid_loss 0.005 | lr 5.000000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0015, Accuracy: 0.9810
Batch [100/152], Loss: 0.0014, Accuracy: 0.9817
Batch [150/152], Loss: 0.0014, Accuracy: 0.9818
---------------------------------------------------------------------
| epoch 9 | time: 0.49s | valid_acc 0.906 valid_loss 0.005 | lr 0.500000
---------------------------------------------------------------------
Batch [50/152], Loss: 0.0013, Accuracy: 0.9831
Batch [100/152], Loss: 0.0013, Accuracy: 0.9831
Batch [150/152], Loss: 0.0014, Accuracy: 0.9825
---------------------------------------------------------------------
| epoch 10 | time: 0.54s | valid_acc 0.906 valid_loss 0.005 | lr 0.500000
---------------------------------------------------------------------

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 = "随便播放一首专辑阁楼里的佛里的歌"
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)])

该文本的类别是：Travel-Query