• 🍨?本文為🔗365天深度學習訓練營中的學習記錄博客
• 🍖?原作者：K同學啊

一、準備工作

數據格式：

import torch
from torch import nn
import torchvision
from torchvision import transforms,datasets
import os,PIL,pathlib,warningswarnings.filterwarnings("ignore")device = torch.device("cuda" if torch.cuda.is_available else "cpu")import pandas as pd# CSV 格式通常為 無表頭（header=None），以制表符（sep='\t'）分隔
train_data = pd.read_csv('./data/train.csv',sep='\t',header=None)
train_data.head()

# 構造數據集迭代器
def custom_data_iter(texts,labels):for x,y in zip(texts,labels):yield x,ytrain_iter = custom_data_iter(train_data[0].values[:],train_data[1].values[:])

二、數據預處理

from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
import jieba# 中文分詞方法
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>"])label_name = list(set(train_data[1].values[:]))text_pipeline = lambda x:vocab(tokenizer(x))
label_pipeline = lambda x:label_name.index(x)

三、模型搭建

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,embed_dim)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)

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

import timedef train(dataloader):model.train()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()loss = criterion(predicted_label,label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(),0.1) # 梯度裁剪optimizer.step()total_acc += (predicted_label.argmax(1)==label).sum().item()train_loss += loss.item()*label.size(0)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()total_acc,test_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()test_loss += loss.item()*label.size(0)total_count += label.size(0)return total_acc/total_count,test_loss/total_count

四、訓練模型

from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset# 超參數
EPOCHS = 10
LR = 5
BATCH_SIZE = 64criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),lr=LR)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,1.0,gamma=0.1)
total_accu = Nonetrain_iter = custom_data_iter(train_data[0].values[:],train_data[1].values[:])
train_dataset = to_map_style_dataset(train_iter)num_train = int(len(train_dataset)*0.8)
split_train,split_valid = random_split(train_dataset,[num_train,len(train_dataset)-num_train])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)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)

def predict(text):with torch.no_grad():text = torch.tensor(text_pipeline(text)).to(device)output = model(text,torch.tensor([0]).to(device))return output.argmax(1).item()
# ex_text_str = "還有南昌到哈爾濱西的火車票嗎？"
ex_text_str = "我想聽TWICE的新曲"
print("該文本的類別是：%s" %label_name[predict(ex_text_str)])

總結

本次學習對中文文本實現了分類，主要代碼和N1周基本一致。