动手学深度学习（Pytorch版）代码实践 -卷积神经网络-28批量规范化

28批量规范化

"""可持续加速深层网络的收敛速度"""
import torch
from torch import nn
import liliPytorch as lp
import matplotlib.pyplot as pltdef batch_norm(X, gamma, beta, moving_mean, moving_var, eps, momentum):"""实现一个具有张量的批量规范化层。"""# 如果启用了梯度计算，torch.is_grad_enabled() 返回 True；否则返回 False。if not torch.is_grad_enabled():# torch.no_grad() 是一个上下文管理器，用于临时禁用梯度计算# torch.enable_grad() 是一个上下文管理器，用于在禁用梯度计算的上下文中重新启用梯度计算。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) # 计算张量 X 沿着第 0 维的平均值# 维度 0 代表样本数量，即沿着每个特征计算所有样本的平均值。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) * var# gamma 和 beta 的更新是通过反向传播和优化器自动完成的Y = gamma * X_hat + beta # 缩放和移位return Y, moving_mean.data, moving_var.dataclass 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1# 经过归一化处理后的数据均值接近于零。因此，将滑动均值初始化为0，是对数据初始均值的一种合理假设。self.moving_mean = torch.zeros(shape)# 方差表示数据的离散程度。将滑动方差初始化为1，意味着假设数据的初始方差为1，# 即数据分布接近标准正态分布。这样初始化可以避免初始阶段的数值不稳定。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GPU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#使用批量规范化层的 LeNet
net = nn.Sequential(nn.Conv2d(1, 6,  kernel_size=5, padding=2), # 卷积层1：输入通道数1，输出通道数6，卷积核大小5x5，填充2BatchNorm(num_features=6, num_dims=4),nn.ReLU(), # 激活函数nn.AvgPool2d(kernel_size=2, stride=2), # 平均池化层1：池化窗口大小2x2，步幅2nn.Conv2d(6, 16, kernel_size=5), # 卷积层2：输入通道数6，输出通道数16，卷积核大小5x5BatchNorm(num_features=16, num_dims=4),nn.ReLU(), nn.AvgPool2d(kernel_size=2, stride=2), # 平均池化层2：池化窗口大小2x2，步幅2nn.Flatten(), # 展平层：将多维输入展平为1维nn.Linear(16 * 5 * 5, 120), # 全连接层1：输入节点数16*5*5，输出节点数120BatchNorm(num_features=120, num_dims=2),nn.ReLU(),nn.Linear(120, 84), # 全连接层2：输入节点数120，输出节点数84BatchNorm(num_features=84, num_dims=2),nn.ReLU(), nn.Linear(84, 10) # 全连接层3：输入节点数84，输出节点数10（对应10个分类）
)lr, num_epochs, batch_size = 1.0, 10, 256
train_iter, test_iter = lp.loda_data_fashion_mnist(batch_size)
# lp.train_ch6(net, train_iter, test_iter, num_epochs, lr, lp.try_gpu())
# plt.show()# loss 0.200, train acc 0.925, test acc 0.812
# 34957.3 examples/sec on cuda:0# loss 0.189, train acc 0.928, test acc 0.894
# 33471.2 examples/sec on cuda:0#简明实现
net = nn.Sequential(nn.Conv2d(1, 6, kernel_size=5), nn.BatchNorm2d(6), nn.ReLU(),nn.AvgPool2d(kernel_size=2, stride=2),nn.Conv2d(6, 16, kernel_size=5), nn.BatchNorm2d(16), nn.ReLU(),nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(),nn.Linear(256, 120), nn.BatchNorm1d(120), nn.ReLU(),nn.Linear(120, 84), nn.BatchNorm1d(84), nn.ReLU(),nn.Linear(84, 10)
)
lp.train_ch6(net, train_iter, test_iter, num_epochs, lr, lp.try_gpu())
plt.show()# nn.Sigmoid()
# loss 0.263, train acc 0.902, test acc 0.833
# 46935.0 examples/sec on cuda:0# nn.ReLU()
# loss 0.224, train acc 0.914, test acc 0.874
# 44479.2 examples/sec on cuda:0
"""
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。
"""