【Pytorch学习笔记】4.深度学习策略

以你之姓@ 2022-09-04 04:55 317阅读 0赞

### 文章目录 ###

*  25. Visdom可视化
 *  26.过拟合&欠拟合
 *  27.Train-Val-Test划分
 *  28.正则化
 *  29.动量与学习率衰减
 *  30.early-stopping, dropout, sgd
 *  31.贝叶斯定理

根据龙良曲Pytorch学习视频整理，视频链接：  
[【计算机-AI】PyTorch学这个就够了！][-AI_PyTorch]  
[(好课推荐)深度学习与PyTorch入门实战——主讲人龙良曲][PyTorch]

# 25. Visdom可视化 #

*  Tensorboard: gogle出品的tensorflow可视化工具
 *  TensorboardX: 对应的pytorch可视化工具  
    数据必须搬到cpu上转化为numpy数据才能可视化
 *  Visdom: from Facebook 效率高、美观

visdom安装

*  不建议直接使用pip安装，会出现error
 *  推荐使用源码安装visdom

源码安装步骤

1.  安装包地址：[fossasia/visdom][fossasia_visdom]
2.  解压后cmd进入visdom-master目录执行 `pip install -e .`
3.  安装完成要将visdom-master文件下py目录下的文件夹复制到依赖包地址
4.  cmd执行命令`python -m visdom.server`打开服务连接，找到网址复制到浏览器
5.  如果执行命令时`Checking for scripts`加载时间太慢，找出visdom的安装文件注释掉server.py中的`download_scripts()`
6.  如果运行程序时visdom浏览器显示蓝屏，说明visdom文件被qiang了，需要再重新手动覆盖  
    ![在这里插入图片描述][3fdc3ab6e1e949ea806dc511b276c3a3.png]

可视化代码

from visdom import Visdom
    
    # lines: single trace
    viz = Visdom()
    viz.line([0.], [0.], win='train_loss', opts=dict(title='train_loss'))
    viz.line([loss.item()], [global_step], win='train_loss', update='append')
    
    # lines: multi-traces
    viz.line([[0., 0.]], [0.], win='test', opts=dict(title='test loss&acc.', legend=['loss', 'acc.']))
    viz.line([[test_loss, correct / len(test_loader.dataset)]], [global_step], win='test', update='append')
    
    # visual X
    viz.images(data.view(-1, 1, 28, 28), win='x')
    viz.text(str(pred.detach().cpu().numpy()), win='pred', opts=dict(title='pred'))

可视化结果（没有手写数字的数据集？？？）  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70]

# 26.过拟合&欠拟合 #

*  underfitting: Estimated < Ground-truth  
    e.g. WGAN  
    train acc. is bad  
    test acc. is bad as well
 *  overfitting: Estimated > Ground-truth  
    how to detect  
    how to reduce

# 27.Train-Val-Test划分 #

不能根据Test set的performance调整参数，只能通过Validation Set的结果调整，否则会造成数据污染

k-fold cross validation

*  merge train/val sets
 *  randomly sample 1/k as val set
 *  尽可能多的数据实行backward调参，且防止死记硬背

# load data
    train_db = datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.01307, ), (0.3081, ))
                       ]))
    train_loader = torch.utils.data.DataLoader(
        train_db,
        batch_size=batch_size, shuffle=True
    )
    
    test_db = datasets.MNIST('../data', train=False,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.01307, ), (0.3081, ))
                       ]))
    test_loader = torch.utils.data.DataLoader(
        test_db,
        batch_size=batch_size, shuffle=True
    )
    
    print('train', len(train_db), 'test', len(test_db))
    train_db, val_db = torch.utils.data.random_split(train_db, [50000, 10000])
    print('db1', len(train_db), 'db2', len(val_db))
    train_loader = torch.utils.data.DataLoader(
        train_db,
        batch_size=batch_size, shuffle=True
    )
    val_loader = torch.utils.data.DataLoader(
        val_db,
        batch_size=batch_size, shuffle=True
    )

# 28.正则化 #

Occam’s Razor

*  More thing should not be used than are necessary

Reduce Overfitting

*  More data
 *  Constraint model complexity  
    shallow  
    regularization
 *  Dropout
 *  Data argumentation
 *  Early Stopping

Regularization(weight decay)：Loss后加入正则化项

*  L1-regularization  λ ∑ i = 1 n ∣ θ i ∣ \\lambda \\sum^n\_\{i=1\}|\\theta \_i| λ∑i=1n∣θi∣
 *  L2-regularization  1 2 λ ∣ ∣ W ∣ ∣ 2 \\frac\{1\}\{2\}\\lambda ||W||^2 21λ∣∣W∣∣2

L2正则化

optimizer = optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.01)

L1正则化（只能手动实现）

regularization_loss = 0
    for param in model.parameters():
        regularization_loss += torch.sum(torch.abs(param))
        
    classify_loss = criteon(logits, target)
    loss = classify_loss + 0.01 * regularization_loss
    
    torch.optim.optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# 29.动量与学习率衰减 #

momentum

*  考虑上一次历史数据
 *  原梯度更新函数： w k + 1 = w k − α ▽ f ( w k ) w^\{k+1\}=w^k-\\alpha\\bigtriangledown f(w^k) wk\+1=wk−α▽f(wk)
 *  加入动量后： w k + 1 = w k − α ▽ f ( w k ) − β z k w^\{k+1\}=w^k-\\alpha\\bigtriangledown f(w^k)-\\beta z^k wk\+1=wk−α▽f(wk)−βzk  
     w k + 1 = w k − α z k + 1 w^\{k+1\}=w^k-\\alpha z^\{k+1\} wk\+1=wk−αzk\+1，其中 z k + 1 = β z k + α ▽ f ( w k ) z^\{k+1\}=\\beta z^k+\\alpha\\bigtriangledown f(w^k) zk\+1=βzk\+α▽f(wk)
 *  pytorch的Adam优化器内置动量

optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.78, weight_decay=0.01)

learning-rate decay  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 1]

# Assuming optimizer uses lr = 0.05 for all groups
    # lr = 0.05 if epoch < 30
    # lr = 0.005 if 30 <= epoch < 60
    # lr = 0.0005 if 60 <= epoch < 90
    # ...`在这里插入代码片`
    scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
    for epoch in range(100):
        scheduler.step()
        train(...)
        validata(...)

# 30.early-stopping, dropout, sgd #

early-stopping

*  Validation set to select parameters
 *  Monitor validation performance
 *  Stop at the highest val pref.

dropout

*  Learning less to learn better
 *  Each connection has p=\[0, 1\] to lose
 *  test时不dropout
 *  `torch.nn.Dropout(p=dropout_prob)`  
    `tensorflow.nn.drropout(keep_prob)`

net_dropped = nn.Sequential(
        nn.Linear(784, 200),
        nn.Dropout(0.5),
        nn.LeakyReLU(inplace=True),
        nn.Linear(200, 200),
        nn.Dropout(0.5),
        nn.LeakyReLU(inplace=True),
        nn.Linear(200, 10),
        nn.LeakyReLU(inplace=True),
    )
    
    for epoch in range(epochs):
    	# train
    	net_dropped.train()
    	for batch_idx, (data, target) in enumerate(train_loader):
    		...
    	net_dropped.eval()
    	test_loss = 0
    	correct = 0
    	for data, target in test_loader:
    		...

Stochastic Gradient Descent(sgd)

*  Stochastic: not random 不是一次加载所有数据而是batch
 *  Deterministic

# 31.贝叶斯定理 #

P ( A ∣ B ) = P ( B ∣ A ) × P ( A ) P ( B ) P(A|B)=\\frac\{P(B|A)×P(A)\}\{P(B)\} P(A∣B)=P(B)P(B∣A)×P(A)

找了全网没有这一节的教程：(

[-AI_PyTorch]: https://www.bilibili.com/video/BV1Rv411y7oE?p=1
[PyTorch]: https://www.bilibili.com/video/BV1mh41167e3?p=1
[fossasia_visdom]: https://github.com/fossasia/visdom
[3fdc3ab6e1e949ea806dc511b276c3a3.png]: /images/20220829/ba5e587f18c24405bda775ebec3d57ec.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70]: /images/20220829/c1954b7e2f734a7fba3da5a9b52d0786.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2tlaXZlbl8_size_16_color_FFFFFF_t_70 1]: /images/20220829/ff91c4ddd707426680e913b6722beb1f.png