目录
- 一、前言
- 二、准备工作
- 三、数据预处理
- 1.加载数据
- 2.构建词典
- 3.生成数据批次和迭代器
- 三、模型构建
- 1. 搭建模型
- 2. 初始化模型
- 3. 定义训练与评估函数
- 四、训练模型
- 1. 拆分数据集并运行模型
一、前言
🍨 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
● 难度：夯实基础⭐⭐
● 语言：Python3、Pytorch3
● 时间：4月23日-4月28日
🍺要求：
1、熟悉NLP的基础知识
二、准备工作
环境搭建
Python 3.8
pytorch == 1.8.1
torchtext == 0.9.1
三、数据预处理
1.加载数据
import torch
import torch.nn as nn
import os,PIL,pathlib,warnings
warnings.filterwarnings("ignore") #忽略警告信息
# win10系统
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
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)
train_data.head()
# 构造数据集迭代器
def coustom_data_iter(texts, labels):
for x, y in zip(texts, labels):
yield x, y
train_iter = coustom_data_iter(train_data[0].values[:], train_data[1].values[:])
2.构建词典
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
# conda install jieba -y
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'))
3.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，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)
三、模型构建
1. 搭建模型
from torch import nn
class 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 = 0.5
self.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)
2. 初始化模型
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
import time
def train(dataloader):
model.train() # 切换为训练模式
total_acc, train_loss, total_count = 0, 0, 0
log_interval = 50
start_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loss
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()
def evaluate(dataloader):
model.eval() # 切换为测试模式
total_acc, train_loss, total_count = 0, 0, 0
with 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
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 training
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 = 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_acc
print('-' * 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)
