Pytorch深度学习【十四】

批量归一化

• 归一化
  • 损失出现在最后，后面的层(高级语义层)训练较快
  • 数据输入在最底部
    • 底部的层训练慢
    • 底部层一变化，所有高级语义层都得跟着变
    • 最后的那些层需要重新学习多次—收敛速度变慢
  • 问题—是否可以在学习底部层的时候避免变化顶部层
• 批量归一化
  • 固定小批量里面每一层的均值和方差
    $${\mu }_B=\frac{1}{｜B｜}\sum _{i\in B}{x}_{i}and{\sigma }_B^2=\frac{1}{｜B｜}\sum _{i\in B}(x_i-{\mu }_B{)}^2+ϵ$$
  • 进行可学习参数的归一化
    $$x_{i+1}=\gamma \frac{x_i-{\mu }_B}{{\sigma }_B}+\beta$$
  • 意思是先进行小批量的均值方差计算，而后对每个输入数据进行处理
• 批量归一化层
  • 可以学习的参数为$\gamma \beta$
  • 批量归一化层作用位置
    • 全连接层和卷积层输出上，激活层之前
    • 全连接和卷积层输入上
  • 对全连接层，作用在特征维度上
  • 对于卷积层，作用在通道维上
• 批量归一化的作用
  • 加入噪音来控制模型复杂度
    $$x_{i+1}=\gamma \frac{x_i-{\mu }_B}{{\sigma }_B}+\beta$$
  • 没必要跟丢弃法混合使用
• 总结
  • 批量归一化固定小批量中的均值和方差，然后学习出适合的偏移和缩放
  • 可以加速收敛速度，因此学习率可以比较大，但一般不改变模型精确度
• 代码实现
  • 从零实现

    def batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum):
      if not torch.is_grad_enabled():
          X_hat = (X - moving_mean) / torch.sqrt(moving_var + eps)
      else:
          assert len(X.shape) in (2, 4)
          if len(X.shape) == 2:
              mean = X.mean(dim=0)
              var = ((X - mean)**2).mean(dim=0)
          else:
              mean = X.mean(dim=(0, 2, 3), keepdim=True)
              var = ((X - mean)**2).mean(dim=(0, 2, 3), keepdim=True)
          X_hat = (X - mean) / torch.sqrt(var + eps)
          moving_mean = momentum * moving_mean + (1.0 - momentum) * mean
          moving_var = momentum * moving_var + (1.0 - momentum) * var
      Y = gamma * X_hat + beta
      return Y, moving_mean.data, moving_var.data
    class BatchNorm(nn.Module):
        def __init__(self, num_features, num_dims):
            super().__init__()
            if num_dims == 2:
                shape = (1, num_features)
            else:
                shape = (1, num_features, 1, 1)
            self.gamma = nn.Parameter(torch.ones(shape))
            self.beta = nn.Parameter(torch.zeros(shape))
            self.moving_mean = torch.zeros(shape)
            self.moving_var = torch.ones(shape)
    
        def forward(self, X):
            if self.moving_mean.device != X.device:
                self.moving_mean = self.moving_mean.to(X.device)
                self.moving_var = self.moving_var.to(X.device)
            Y, self.moving_mean, self.moving_var = batch_norm(
                X, self.gamma, self.beta, self.moving_mean, self.moving_var,
                eps=1e-5, momentum=0.9)
            return Y
    

  • 快速实现

    net = nn.Sequential(
      nn.Conv2d(1, 6, kernel_size=5), nn.BatchNorm2d(6), # 6就是上一层的输出通道个数
      nn.Sigmoid(), nn.MaxPool2d(kernel_size=2, stride=2)
    )
    

残差神经网络

• 加深神经网络一直会带来好处么

  • 如果模型的训练方向有问题—模型反而变差
  • 因此，我们要保证后续的模型包含前向模型，要保证学到本该学到的东西

• 残差块

  • 串联一个层改变函数类，我们希望能扩大函数类
  • 残差块加入快速通道(右边)来得到$f(x)=x+g(x)$
  • 解读—即使不通过g(x)，我依然能直接跳过本层获得上层特征结果

• 残差网络解决的问题—梯度消失

  • 每层梯度都是本层梯度加上层梯度，保证梯度不会太小导致模型参数更新出现问题

• 结构图
  

• 细节实现—引入1*1的目的—修改通道数
  

• 总结

  • 残差块使得很深的网络更加容易训练
    • 因为其包含了前向模型，可以逐步训练保证稳定性
  • 残差网络对随后的深层神经网络设计产生了深远影响

• 代码实现

import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

class Residual(nn.Module):  
    def __init__(self, input_channels, num_channels, use_1x1conv=False,
                 strides=1):
        super().__init__()
        self.conv1 = nn.Conv2d(input_channels, num_channels, kernel_size=3,
                               padding=1, stride=strides)
        self.conv2 = nn.Conv2d(num_channels, num_channels, kernel_size=3,
                               padding=1)
        if use_1x1conv:
            self.conv3 = nn.Conv2d(input_channels, num_channels,
                                   kernel_size=1, stride=strides)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(num_channels)
        self.bn2 = nn.BatchNorm2d(num_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, X):
        Y = F.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
          X = self.conv3(X)
        Y += X
        return F.relu(Y)
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
                   nn.BatchNorm2d(64), nn.ReLU(),
                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

def resnet_block(input_channels, num_channels, num_residuals,
                 first_block=False):
    blk = []
    for i in range(num_residuals):
        if i == 0 and not first_block:
            blk.append(
                Residual(input_channels, num_channels, use_1x1conv=True,
                         strides=2))
        else:
            blk.append(Residual(num_channels, num_channels))
    return blk

b2 = nn.Sequential(*resnet_block(64, 64, 2, first_block=True))
b3 = nn.Sequential(*resnet_block(64, 128, 2))
b4 = nn.Sequential(*resnet_block(128, 256, 2))
b5 = nn.Sequential(*resnet_block(256, 512, 2))

net = nn.Sequential(b1, b2, b3, b4, b5, nn.AdaptiveAvgPool2d((1, 1)),
                    nn.Flatten(), nn.Linear(512, 10))
X = torch.rand(size=(1, 1, 224, 224))
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__, 'output shape:\t', X.shape)

数据增广

• 增加一个已有数据集，使得有更多的多样性
  • 在语言里面加入各种不同的背景噪声
  • 改变图片颜色和形状
• 流程
  • 读取数据—随机读取—数据增强
• 可以认为是一个正则项
• 常见办法
  • 翻转
    • 左右翻转
    • 上下翻转
  • 切割
    • 从图片中切割一块而后变形到固定形状
      • 随机高宽比
      • 随机大小
      • 随机位置
  • 颜色
    • 改变色调，饱和度，明亮度
• 总结
  • 数据增广通过变形数据来获得多样性从而使得模型泛化性能更好
  • 常见图片增广包括翻转、切割、变色
• 代码实现

#===============================基础操作部分===============================#
!pip install d2l
!pip install matplotlib_inline
%matplotlib inline
import torch
import torchvision
from torch import nn
from d2l import torch as d2l
d2l.set_figsize() # 设置后续画图的大小
!wget -O ./pic/test.jpg "https://cdn.pixabay.com/photo/2017/02/20/18/03/cat-2083492__480.jpg" # 下载照片
img = d2l.Image.open('./pic/test.jpg') # 读取照片
d2l.plt.imshow(img) # 查看当前照片
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
  """对img进行2*4张图的aug方法的增广"""
  Y = [aug(img) for _ in range(num_rows*num_cols)]
  d2l.show_images(Y, num_rows, num_cols, scale=scale)
# 图像左右翻转
apply(img, torchvision.transforms.RandomHorizontalFlip())
# 图像上下翻转
apply(img, torchvision.transforms.RandomVerticalFlip())
# 随机裁剪
shape_aug = torchvision.transforms.RandomResizedCrop(
    (200, 200), scale=(0.3, 0.8), ratio=(0.5, 2)
)
# (200, 200)---不论裁剪多大最后都要放大到200200
# scale---裁剪的范围比例
# 拉伸比例
apply(img, shape_aug)
# 随机修改亮度
apply(img, torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.2, saturation=0.6, hue=0.4))
# 增广方式的混合使用
color_aug = torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.2, saturation=0.6, hue=0.4)
augs = torchvision.transforms.Compose([torchvision.transforms.RandomHorizontalFlip(), shape_aug, color_aug])
apply(img, augs)

• 完整数据增强训练案例

all_images = torchvision.datasets.CIFAR10(
    train=True, root="./data", download=True
)
d2l.show_images([
    all_images[i][0] for i in range(32)], 4, 8, scale=0.8);
train_augs = torchvision.transforms.Compose([
    torchvision.transforms.RandomHorizontalFlip(),
    torchvision.transforms.ToTensor()])
test_augs = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])
# torchvision.transforms.ToTensor()转换增广使得其可以训练
# 测试不需要增广
# 定义辅助函数，便于读取图像和应用图像增广
def load_cifar10(is_train, augs, batch_size):
  dataset = torchvision.datasets.CIFAR10(
      root='./data', train=is_train, transform=augs, download=True
  )
  dataloader = torch.utils.data.DataLoader(
      dataset, batch_size=batch_size, shuffle=is_train, num_workers=4
  )
  return dataloader
# 定义一个函数，使用多GPU对模型进行训练和评估
def train_batch_ch13(net, X, y, loss, trainer, devices):
    if isinstance(X, list):
        X = [x.to(devices[0]) for x in X]
    else:
        X = X.to(devices[0])
    y = y.to(devices[0])
    net.train()
    trainer.zero_grad()
    pred = net(X)
    l = loss(pred, y)
    l.sum().backward()
    trainer.step()
    train_loss_sum = l.sum()
    train_acc_sum = d2l.accuracy(pred, y)
    return train_loss_sum, train_acc_sum

def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,
               devices=d2l.try_all_gpus()):
    timer, num_batches = d2l.Timer(), len(train_iter)
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
                            legend=['train loss', 'train acc', 'test acc'])
    net = nn.DataParallel(net, device_ids=devices).to(devices[0]) # 多GPU训练
    for epoch in range(num_epochs):
        metric = d2l.Accumulator(4)
        for i, (features, labels) in enumerate(train_iter):
            timer.start()
            l, acc = train_batch_ch13(net, features, labels, loss, trainer,
                                      devices)
            metric.add(l, acc, labels.shape[0], labels.numel())
# 定义 train_with_data_aug 函数，使用图像增广来训练模型
batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10, 3)

def init_weights(m):
    if type(m) in [nn.Linear, nn.Conv2d]:
        nn.init.xavier_uniform_(m.weight)

net.apply(init_weights)

def train_with_data_aug(train_augs, test_augs, net, lr=0.001):
    train_iter = load_cifar10(True, train_augs, batch_size)
    test_iter = load_cifar10(False, test_augs, batch_size)
    loss = nn.CrossEntropyLoss(reduction="none")
    trainer = torch.optim.Adam(net.parameters(), lr=lr)
    train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)
# 训练模型
train_with_data_aug(train_augs, test_augs, net)