Pytorch文本分类入门
🍨 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
一、前期准备
1. 环境安装
确保已经安装torchtext与portalocker库
2. 加载数据
#加载数据
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms,datasets
import os,PIL,pathlib,warnings
warnings.filterwarnings("ignore") #忽略警告信息
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
输出：
device(type='cpu')
from torchtext.datasets import AG_NEWS
train_iter = AG_NEWS(split='train') #加载AG NEWS数据集
3. 构建词典
#构建词典
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
tokenizer = get_tokenizer('basic_english') #返回分词器函数
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(['here','is','an','example'])
输出：
[475, 21, 30, 5297]
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
text_pipeline('here is the an example')
输出：
[475, 21, 2, 30, 5297]
4. 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 DataLoader
def collate_batch(batch):
label_list, text_list, offsets = [],[],[0]
for(_label, _text) 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
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 label_list.to(device), text_list.to(device), offsets.to(device)
#数据加载器
dataloader = DataLoader(
train_iter,
batch_size = 8,
shuffle = False,
collate_fn = collate_batch
)
二、准备模型
1. 定义模型
定义TextClassificationModel模型，首先对文本进行嵌入，然后对句子嵌入后的结果进行均值聚合
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)
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)
2. 定义实例
#定义实例
num_class = len(set([label for (label,text) in train_iter]))
vocab_size = len(vocab)
em_size = 64
model = TextClassificationModel(vocab_size, em_size, num_class).to(device)
3. 定义训练函数与评估函数
#定义训练函数与评估函数
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 = 500
start_time = time.time()
for idx, (label, text, 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
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{: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 = 0,0,0
staet_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,(label,text,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
三、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
# 超参数设定
EPOCHS = 10
LR = 5
BATCH_SIZE = 64
#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
# 加载数据
train_iter, test_iter = AG_NEWS()
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
num_train = int(len(train_dataset) * 0.95)
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)
test_dataloader = DataLoader(test_dataset, 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)
if total_accu is not None and total_accu > val_acc:
scheduler.step()
else:
total_accu = val_acc
print('-' * 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)
输出：
|epoch1| 500/1782 batches|train_acc0.909 train_loss0.00420
|epoch1|1000/1782 batches|train_acc0.909 train_loss0.00431
|epoch1|1500/1782 batches|train_acc0.910 train_loss0.00415
---------------------------------------------------------------------
| epoch 1 | time:17.55s | valid_acc 0.913 valid_loss 0.004
---------------------------------------------------------------------
|epoch2| 500/1782 batches|train_acc0.924 train_loss0.00355
|epoch2|1000/1782 batches|train_acc0.922 train_loss0.00366
|epoch2|1500/1782 batches|train_acc0.917 train_loss0.00376
---------------------------------------------------------------------
| epoch 2 | time:17.58s | valid_acc 0.914 valid_loss 0.004
---------------------------------------------------------------------
|epoch3| 500/1782 batches|train_acc0.929 train_loss0.00329
|epoch3|1000/1782 batches|train_acc0.929 train_loss0.00332
|epoch3|1500/1782 batches|train_acc0.929 train_loss0.00337
---------------------------------------------------------------------
| epoch 3 | time:19.67s | valid_acc 0.892 valid_loss 0.005
---------------------------------------------------------------------
|epoch4| 500/1782 batches|train_acc0.947 train_loss0.00258
|epoch4|1000/1782 batches|train_acc0.946 train_loss0.00257
|epoch4|1500/1782 batches|train_acc0.946 train_loss0.00266
---------------------------------------------------------------------
| epoch 4 | time:18.36s | valid_acc 0.915 valid_loss 0.004
---------------------------------------------------------------------
|epoch5| 500/1782 batches|train_acc0.951 train_loss0.00243
|epoch5|1000/1782 batches|train_acc0.949 train_loss0.00252
|epoch5|1500/1782 batches|train_acc0.947 train_loss0.00256
---------------------------------------------------------------------
| epoch 5 | time:17.92s | valid_acc 0.918 valid_loss 0.004
---------------------------------------------------------------------
|epoch6| 500/1782 batches|train_acc0.950 train_loss0.00245
|epoch6|1000/1782 batches|train_acc0.950 train_loss0.00246
|epoch6|1500/1782 batches|train_acc0.950 train_loss0.00245
---------------------------------------------------------------------
| epoch 6 | time:18.10s | valid_acc 0.918 valid_loss 0.004
---------------------------------------------------------------------
|epoch7| 500/1782 batches|train_acc0.950 train_loss0.00245
|epoch7|1000/1782 batches|train_acc0.951 train_loss0.00242
|epoch7|1500/1782 batches|train_acc0.951 train_loss0.00239
---------------------------------------------------------------------
| epoch 7 | time:18.08s | valid_acc 0.917 valid_loss 0.004
---------------------------------------------------------------------
|epoch8| 500/1782 batches|train_acc0.951 train_loss0.00238
|epoch8|1000/1782 batches|train_acc0.951 train_loss0.00241
|epoch8|1500/1782 batches|train_acc0.955 train_loss0.00228
---------------------------------------------------------------------
| epoch 8 | time:18.75s | valid_acc 0.918 valid_loss 0.004
---------------------------------------------------------------------
|epoch9| 500/1782 batches|train_acc0.952 train_loss0.00234
|epoch9|1000/1782 batches|train_acc0.953 train_loss0.00235
|epoch9|1500/1782 batches|train_acc0.951 train_loss0.00237
---------------------------------------------------------------------
| epoch 9 | time:18.50s | valid_acc 0.917 valid_loss 0.004
---------------------------------------------------------------------
|epoch10| 500/1782 batches|train_acc0.951 train_loss0.00234
|epoch10|1000/1782 batches|train_acc0.954 train_loss0.00231
|epoch10|1500/1782 batches|train_acc0.954 train_loss0.00234
---------------------------------------------------------------------
| epoch 10 | time:17.82s | valid_acc 0.917 valid_loss 0.004
---------------------------------------------------------------------
2. 使用测试数据集评估模型
print('Checking the results of test dataset.')
test_acc,test_loss = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(test_acc))
输出：
Checking the results of test dataset.
test accuracy 0.908
