笔记小结：现代卷积神经网络之批量归一化

本文为李沐老师《动手学深度学习》笔记小结，用于个人复习并记录学习历程，适用于初学者

训练深层神经网络是十分困难的，特别是在较短的时间内使他们收敛更加棘手。本节将介绍批量规范化（batch normalization），这是一种流行且有效的技术，可持续加速深层网络的收敛速度。

从零开始实现

张量的批量规范化函数

import torch
from torch import nndef batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum):# 通过is_grad_enabled来判断当前模式是训练模式还是预测模式if not torch.is_grad_enabled():# 如果是在预测模式下，直接使用传入的移动平均所得的均值和方差X_hat = (X - moving_mean) / torch.sqrt(moving_var + eps)else:assert len(X.shape) in (2, 4)if len(X.shape) == 2:# 使用全连接层的情况，计算特征维上的均值和方差mean = X.mean(dim=0)var = ((X - mean) ** 2).mean(dim=0)else:# 使用二维卷积层的情况，计算通道维上（axis=1）的均值和方差。# 这里我们需要保持X的形状以便后面可以做广播运算mean = X.mean(dim=(0, 2, 3), keepdim=True)var = ((X - mean) ** 2).mean(dim=(0, 2, 3), keepdim=True)# 训练模式下，用当前的均值和方差做标准化X_hat = (X - mean) / torch.sqrt(var + eps)# 更新移动平均的均值和方差moving_mean = momentum * moving_mean + (1.0 - momentum) * meanmoving_var = momentum * moving_var + (1.0 - momentum) * varY = gamma * X_hat + beta  # 缩放和移位return Y, moving_mean.data, moving_var.data

批量规范化层

class BatchNorm(nn.Module):# num_features：完全连接层的输出数量或卷积层的输出通道数。# num_dims：2表示完全连接层，4表示卷积层def __init__(self, num_features, num_dims):super().__init__()if num_dims == 2:shape = (1, num_features)else:shape = (1, num_features, 1, 1)# 参与求梯度和迭代的拉伸和偏移参数，分别初始化成1和0self.gamma = nn.Parameter(torch.ones(shape))self.beta = nn.Parameter(torch.zeros(shape))# 非模型参数的变量初始化为0和1self.moving_mean = torch.zeros(shape)self.moving_var = torch.ones(shape)def forward(self, X):# 如果X不在内存上，将moving_mean和moving_var# 复制到X所在显存上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)# 保存更新过的moving_mean和moving_varY, 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

使用批量规范化层作用于LeNet

批量规范化是在卷积层或全连接层之后、相应的激活函数之前应用的。

net = nn.Sequential(nn.Conv2d(1, 6, kernel_size=5), BatchNorm(6, num_dims=4), nn.Sigmoid(),nn.AvgPool2d(kernel_size=2, stride=2),nn.Conv2d(6, 16, kernel_size=5), BatchNorm(16, num_dims=4), nn.Sigmoid(),nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),nn.Linear(16*4*4, 120), BatchNorm(120, num_dims=2), nn.Sigmoid(),nn.Linear(120, 84), BatchNorm(84, num_dims=2), nn.Sigmoid(),nn.Linear(84, 10))

训练

准备工作

和之前多篇文章中提到的一样，不再赘述，只给出代码

from IPython import display
import torchvision
from torch.utils import data
from torchvision import transforms
import matplotlib.pyplot as pltdef load_data_fashion_mnist(batch_size, resize=None): """下载Fashion-MNIST数据集，然后将其加载到内存中"""trans = [transforms.ToTensor()]if resize:trans.insert(0, transforms.Resize(resize))trans = transforms.Compose(trans)mnist_train = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=0)mnist_test = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=0)return (data.DataLoader(mnist_train, batch_size, shuffle=True,num_workers=get_dataloader_workers()),data.DataLoader(mnist_test, batch_size, shuffle=False,num_workers=get_dataloader_workers()))def get_dataloader_workers():  """使用4个进程来读取数据"""return 4batch_size = 128
train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=224)lr, num_epochs, batch_size = 1.0, 10, 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)def accuracy(y_hat, y):  #@save"""计算预测正确的数量"""if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:y_hat = y_hat.argmax(axis=1) #找出输入张量（tensor）中最大值的索引cmp = y_hat.type(y.dtype) == yreturn float(cmp.type(y.dtype).sum())
class Accumulator:  #@save"""在n个变量上累加"""def __init__(self, n):self.data = [0.0] * ndef add(self, *args):self.data = [a + float(b) for a, b in zip(self.data, args)]def reset(self):self.data = [0.0] * len(self.data)def __getitem__(self, idx):return self.data[idx]import matplotlib.pyplot as plt
from matplotlib_inline import backend_inlinedef use_svg_display(): """使⽤svg格式在Jupyter中显⽰绘图"""backend_inline.set_matplotlib_formats('svg')def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):"""设置matplotlib的轴"""axes.set_xlabel(xlabel)axes.set_ylabel(ylabel)axes.set_xscale(xscale)axes.set_yscale(yscale)axes.set_xlim(xlim)axes.set_ylim(ylim)if legend:axes.legend(legend)axes.grid()class Animator:  #@save"""在动画中绘制数据"""def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,ylim=None, xscale='linear', yscale='linear',fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,figsize=(3.5, 2.5)):# 增量地绘制多条线if legend is None:legend = []use_svg_display()self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)if nrows * ncols == 1:self.axes = [self.axes, ]# 使用lambda函数捕获参数self.config_axes = lambda: set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)self.X, self.Y, self.fmts = None, None, fmtsdef add(self, x, y):# 向图表中添加多个数据点if not hasattr(y, "__len__"):y = [y]n = len(y)if not hasattr(x, "__len__"):x = [x] * nif not self.X:self.X = [[] for _ in range(n)]if not self.Y:self.Y = [[] for _ in range(n)]for i, (a, b) in enumerate(zip(x, y)):if a is not None and b is not None:self.X[i].append(a)self.Y[i].append(b)self.axes[0].cla()for x, y, fmt in zip(self.X, self.Y, self.fmts):self.axes[0].plot(x, y, fmt)self.config_axes()display.display(self.fig)display.clear_output(wait=True)def evaluate_accuracy_gpu(net, data_iter, device=None): #@save"""使用GPU计算模型在数据集上的精度"""if isinstance(net, nn.Module):net.eval()  # 设置为评估模式if not device:device = next(iter(net.parameters())).device# 正确预测的数量，总预测的数量metric = Accumulator(2)with torch.no_grad():for X, y in data_iter:if isinstance(X, list):# BERT微调所需的（之后将介绍）X = [x.to(device) for x in X]else:X = X.to(device)y = y.to(device)metric.add(accuracy(net(X), y), y.numel())return metric[0] / metric[1]import time
class Timer:  #@save"""记录多次运行时间"""def __init__(self):self.times = []self.start()def start(self):"""启动计时器"""self.tik = time.time()def stop(self):"""停止计时器并将时间记录在列表中"""self.times.append(time.time() - self.tik)return self.times[-1]def avg(self):"""返回平均时间"""return sum(self.times) / len(self.times)def sum(self):"""返回时间总和"""return sum(self.times)def cumsum(self):"""返回累计时间"""return np.array(self.times).cumsum().tolist()def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):"""用GPU训练模型(在第六章定义)"""def init_weights(m):if type(m) == nn.Linear or type(m) == nn.Conv2d:nn.init.xavier_uniform_(m.weight)net.apply(init_weights)print('training on', device)net.to(device)optimizer = torch.optim.SGD(net.parameters(), lr=lr)loss = nn.CrossEntropyLoss()animator = Animator(xlabel='epoch', xlim=[1, num_epochs],legend=['train loss', 'train acc', 'test acc'])timer, num_batches = Timer(), len(train_iter)for epoch in range(num_epochs):# 训练损失之和，训练准确率之和，样本数metric = Accumulator(3)net.train()for i, (X, y) in enumerate(train_iter):timer.start()optimizer.zero_grad()X, y = X.to(device), y.to(device)y_hat = net(X)l = loss(y_hat, y)l.backward()optimizer.step()with torch.no_grad():metric.add(l * X.shape[0], accuracy(y_hat, y), X.shape[0])timer.stop()train_l = metric[0] / metric[2]train_acc = metric[1] / metric[2]if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:animator.add(epoch + (i + 1) / num_batches,(train_l, train_acc, None))test_acc = evaluate_accuracy_gpu(net, test_iter)animator.add(epoch + 1, (None, None, test_acc))print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, 'f'test acc {test_acc:.3f}')print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec 'f'on {str(device)}')def try_gpu(i=0):  #@save"""如果存在，则返回gpu(i)，否则返回cpu()"""if torch.cuda.device_count() >= i + 1:return torch.device(f'cuda:{i}')return torch.device('cpu')

训练

和以前一样，我们将在Fashion-MNIST数据集上训练网络。这个代码与我们第一次训练LeNet时几乎完全相同，主要区别在于学习率大得多。

begin = time.time()
train_ch6(net, train_iter, test_iter, num_epochs, lr, try_gpu())
end = time.time()
print(end - begin)

这个结果，对比当时不用批量归一化层的LeNet，训练的收敛速度快了许多，loss变小了，train acc提高了许多，但是test acc没有提高太多，出现了过拟合。

简洁实现

除了使用我们刚刚定义的BatchNorm，我们也可以直接使用深度学习框架中定义的BatchNorm。该代码看起来几乎与我们上面的代码相同。

net = nn.Sequential(nn.Conv2d(1, 6, kernel_size=5), nn.BatchNorm2d(6), nn.Sigmoid(),nn.AvgPool2d(kernel_size=2, stride=2),nn.Conv2d(6, 16, kernel_size=5), nn.BatchNorm2d(16), nn.Sigmoid(),nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),nn.Linear(256, 120), nn.BatchNorm1d(120), nn.Sigmoid(),nn.Linear(120, 84), nn.BatchNorm1d(84), nn.Sigmoid(),nn.Linear(84, 10))

下面，我们使用相同超参数来训练模型。请注意，通常高级API变体运行速度快得多，因为它的代码已编译为C++或CUDA，而我们的自定义代码由Python实现。

begin = time.time()
train_ch6(net, train_iter, test_iter, num_epochs, lr, try_gpu())
end = time.time()

从结果可以看到，运行速度快了，并且过拟合也小了许多。

小结

在模型训练过程中，批量规范化利用小批量的均值和标准差，不断调整神经网络的中间输出，使整个神经网络各层的中间输出值更加稳定。
批量规范化在全连接层和卷积层的使用略有不同。
批量规范化层和暂退层一样，在训练模式和预测模式下计算不同。
批量规范化有许多有益的副作用，主要是正则化。另一方面，”减少内部协变量偏移“的原始动机似乎不是一个有效的解释。