- 🍨 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、我的环境
1.语言环境：Python 3.8
2.编译器：Pycharm
3.深度学习环境：
- torch==1.12.1+cu113
- torchvision==0.13.1+cu113
二、导入数据
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")
from torchtext.datasets import AG_NEWS
train_iter = AG_NEWS(split='train') # 加载 AG News 数据集三、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
print(vocab(['here', 'is', 'an', 'example']))结果： [475, 21, 30, 5297]
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
print(text_pipeline('here is the an example'))
结果：[475, 21, 2, 30, 5297]print(label_pipeline('10'))
结果：10四、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，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 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)五、定义模型
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)
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)六、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)七、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为真实值
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 {: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, (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结果：
| epoch 1 | 500/1782 batches| train_acc 0.901 train_loss 0.00458
| epoch 1 | 1000/1782 batches| train_acc 0.905 train_loss 0.00438
| epoch 1 | 1500/1782 batches| train_acc 0.908 train_loss 0.00437
---------------------------------------------------------------------
| epoch 1 | time:6.30s |valid_acc 0.907 | valid_loss 0.004
---------------------------------------------------------------------
| epoch 2 | 500/1782 batches| train_acc 0.917 train_loss 0.00381
| epoch 2 | 1000/1782 batches| train_acc 0.917 train_loss 0.00383
| epoch 2 | 1500/1782 batches| train_acc 0.917 train_loss 0.00386
---------------------------------------------------------------------
| epoch 2 | time:6.26s |valid_acc 0.911 | valid_loss 0.004
---------------------------------------------------------------------
| epoch 3 | 500/1782 batches| train_acc 0.929 train_loss 0.00330
| epoch 3 | 1000/1782 batches| train_acc 0.927 train_loss 0.00340
| epoch 3 | 1500/1782 batches| train_acc 0.923 train_loss 0.00354
---------------------------------------------------------------------
| epoch 3 | time:6.21s |valid_acc 0.935 | valid_loss 0.003
---------------------------------------------------------------------
| epoch 4 | 500/1782 batches| train_acc 0.933 train_loss 0.00306
| epoch 4 | 1000/1782 batches| train_acc 0.932 train_loss 0.00311
| epoch 4 | 1500/1782 batches| train_acc 0.929 train_loss 0.00318
---------------------------------------------------------------------
| epoch 4 | time:6.22s |valid_acc 0.916 | valid_loss 0.003
---------------------------------------------------------------------
| epoch 5 | 500/1782 batches| train_acc 0.948 train_loss 0.00253
| epoch 5 | 1000/1782 batches| train_acc 0.949 train_loss 0.00242
| epoch 5 | 1500/1782 batches| train_acc 0.951 train_loss 0.00238
---------------------------------------------------------------------
| epoch 5 | time:6.23s |valid_acc 0.954 | valid_loss 0.002
---------------------------------------------------------------------
| epoch 6 | 500/1782 batches| train_acc 0.951 train_loss 0.00241
| epoch 6 | 1000/1782 batches| train_acc 0.952 train_loss 0.00236
| epoch 6 | 1500/1782 batches| train_acc 0.952 train_loss 0.00235
---------------------------------------------------------------------
| epoch 6 | time:6.26s |valid_acc 0.954 | valid_loss 0.002
---------------------------------------------------------------------
| epoch 7 | 500/1782 batches| train_acc 0.954 train_loss 0.00228
| epoch 7 | 1000/1782 batches| train_acc 0.951 train_loss 0.00238
| epoch 7 | 1500/1782 batches| train_acc 0.954 train_loss 0.00228
---------------------------------------------------------------------
| epoch 7 | time:6.26s |valid_acc 0.954 | valid_loss 0.002
---------------------------------------------------------------------
| epoch 8 | 500/1782 batches| train_acc 0.953 train_loss 0.00227
| epoch 8 | 1000/1782 batches| train_acc 0.955 train_loss 0.00224
| epoch 8 | 1500/1782 batches| train_acc 0.954 train_loss 0.00224
---------------------------------------------------------------------
| epoch 8 | time:6.32s |valid_acc 0.954 | valid_loss 0.002
---------------------------------------------------------------------
| epoch 9 | 500/1782 batches| train_acc 0.955 train_loss 0.00218
| epoch 9 | 1000/1782 batches| train_acc 0.953 train_loss 0.00227
| epoch 9 | 1500/1782 batches| train_acc 0.955 train_loss 0.00227
---------------------------------------------------------------------
| epoch 9 | time:6.24s |valid_acc 0.954 | valid_loss 0.002
---------------------------------------------------------------------
| epoch 10 | 500/1782 batches| train_acc 0.952 train_loss 0.00229
| epoch 10 | 1000/1782 batches| train_acc 0.955 train_loss 0.00220
| epoch 10 | 1500/1782 batches| train_acc 0.956 train_loss 0.00220
---------------------------------------------------------------------
| epoch 10 | time:6.29s |valid_acc 0.954 | valid_loss 0.002
---------------------------------------------------------------------八、定义训练函数与评估函数
print('Checking the results of test dataset.')
test_acc, test_loss = evaluate(test_dataloader)
print('test accuracy {:8.3f}'.format(test_acc))Checking the results of test dataset.
test accuracy 0.905总结：
预训练词向量：使用GloVe、FastText等预训练词向量能显著提升性能
正则化：合理使用dropout、权重衰减等技术防止过拟合
超参数调优：学习率、批大小、隐藏层维度等对模型性能影响很大
迁移学习：对于小数据集，考虑使用BERT等预训练模型进行微调