河北建设执业资格注册中心网站,一个ip地址上可以做几个网站,国外网站国内做二维码,怎么注册网自己的网站吗目录 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
#x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客 … 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,warningswarnings.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, headerNone)
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, ytrain_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.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
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 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)# 偏移量即语句的总词汇量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)# 数据加载器调用示例
dataloader DataLoader(train_iter,batch_size8,shuffle False,collate_fncollate_batch)三、模型构建
1. 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) # 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 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 trainingcriterion torch.nn.CrossEntropyLoss()
optimizer torch.optim.SGD(model.parameters(), lrLR)
scheduler torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma0.1)
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_sizeBATCH_SIZE,shuffleTrue, collate_fncollate_batch)valid_dataloader DataLoader(split_valid_, batch_sizeBATCH_SIZE,shuffleTrue, collate_fncollate_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)
