本文主要是介绍李沐29_残差网络ResNet——自学笔记，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
残差网络
残差网络的核心思想是：每个附加层都应该更容易地包含原始函数作为其元素之一。
残差块
串联一个层改变函数类，我们希望扩大函数类，残差块加入快速通道来得到f(x)=x+g(x)的结果
ResNet块
1.高宽减半的ResNet块（步幅2）
2.后接多个高宽不变的ResNet块
ResNet架构
1.类似VGG和GoogLeNet总体架构
2.但替换成ResNet块
总结
残差块使得很深的网络更加容易训练，甚至可以训练一千层的网络
代码实现
ResNet沿用了VGG完整的3X3卷积层设计。 残差块里首先有2个有相同输出通道数的3X3卷积层。 每个卷积层后接一个批量规范化层和ReLU激活函数。 然后我们通过跨层数据通路，跳过这2个卷积运算，将输入直接加在最后的ReLU激活函数前。 这样的设计要求2个卷积层的输出与输入形状一样，从而使它们可以相加。 如果想改变通道数，就需要引入一个额外的
1X1卷积层来将输入变换成需要的形状后再做相加运算。
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2lclass Residual(nn.Module): #savedef __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 = Noneself.bn1 = nn.BatchNorm2d(num_channels)self.bn2 = nn.BatchNorm2d(num_channels)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 += Xreturn F.relu(Y)
一种是当use_1x1conv=False时，应用ReLU非线性函数之前，将输入添加到输出。 另一种是当use_1x1conv=True时，添加通过1X1卷积调整通道和分辨率。
输入与输出形状一致
blk = Residual(3,3)
X = torch.rand(4, 3, 6, 6)
Y = blk(X)
Y.shape
torch.Size([4, 3, 6, 6])
增加输出通道的同时，减半高和宽
blk = Residual(3,6, use_1x1conv=True, strides=2)
blk(X).shape # batch size, channel, h, w
torch.Size([4, 6, 3, 3])
ResNet模型
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))
观察ResNet的不同模块的输入形状是如何变化。
X = torch.rand(size=(1, 1, 224, 224))
for layer in net:X = layer(X)print(layer.__class__.__name__,'output shape:\t', X.shape)
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])
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 0.016, train acc 0.995, test acc 0.915
1553.6 examples/sec on cuda:0
这篇关于李沐29_残差网络ResNet——自学笔记的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
