1. ResNet网络¶
① 这样直接加保证了最优解“至少不会变差”，g(x)=0是和以前一样的。假设没有学到任何东西，则g(x)为0。
② 这个x实际上是f0(x)，就是上幅图小的部分，f(x)是f1(x)，新函数包含原函数。
① g(x)高宽减半的同时，1x1 conv的strides=2，也把x高宽减半了。
2. 总结¶
1. ResNet网络（使用自定义）¶
In [1]:
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): # num_channels为输出channel数
super().__init__()
self.conv1 = nn.Conv2d(input_channels, num_channels, kernel_size=3, padding=1, stride=strides) # 可以使用传入进来的strides
self.conv2 = nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1) # 使用nn.Conv2d默认的strides=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) # inplace原地操作，不创建新变量，对原变量操作，节约内存
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)
In [2]:
# 输入和输出形状一致
blk = Residual(3,3) # 输入三通道，输出三通道
X = torch.rand(4,3,6,6)
Y = blk(X) # stride用的默认的1，所以宽高没有变化。如果strides用2，则宽高减半
Y.shape
Out[2]:
torch.Size([4, 3, 6, 6])
In [3]:
# 增加输出通道数的同时，减半输出的高和宽
blk = Residual(3,6,use_1x1conv=True,strides=2) # 由3变为6，通道数加倍
blk(X).shape
Out[3]:
torch.Size([4, 6, 3, 3])
In [4]:
# ResNet的第一个stage
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))
# class Residual为小block，resnet_block 为大block，为Resnet网络的一个stage
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: # stage中不是第一个block则高宽减半
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)) # 因为b1做了两次宽高减半，nn.Conv2d、nn.MaxPool2d，所以b2中的首次就不减半了
b3 = nn.Sequential(*resnet_block(64,128,2)) # b3、b4、b5的首次卷积层都减半
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))
In [5]:
# 观察一下ReNet中不同模块的输入形状是如何变化的
X = torch.rand(size=(1,1,224,224))
for layer in net:
X = layer(X)
print(layer.__class__.__name__,'output shape:\t',X.shape) # 通道数翻倍、模型减半
Sequential output shape: torch.Size([1, 64, 56, 56]) Sequential output shape: torch.Size([1, 64, 56, 56]) Sequential output shape: torch.Size([1, 128, 28, 28]) Sequential output shape: torch.Size([1, 256, 14, 14]) Sequential output shape: torch.Size([1, 512, 7, 7]) AdaptiveAvgPool2d output shape: torch.Size([1, 512, 1, 1]) Flatten output shape: torch.Size([1, 512]) Linear output shape: torch.Size([1, 10])
In [6]:
# 训练模型
lr, num_epochs, batch_size = 0.05, 10, 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
loss 0.009, train acc 0.998, test acc 0.921 2166.7 examples/sec on cuda:0
