最近在学习廖老师的pytorch教程，学到Resnet 这部分着实的烧脑，这个模型都捣鼓了好长时间才弄懂，附上我学习过程中最为不解的网络的具体结构连接（网上一直没有找到对应网络结构，对与一个自学的学渣般的我，很是无奈，所以搞懂后我就...分享给有需要的的你了）
我们先大致了解一下残差模型
ResNet在2015年被提出，在ImageNet比赛classification任务上获得第一名，因为它“简单与实用”并存，之后很多方法都建立在ResNet50或者ResNet101的基础上完成的，检测，分割，识别等领域都纷纷使用ResNet，Alpha zero也使用了ResNet，所以可见ResNet确实很好用。
下面我们从实用的角度去看看ResNet。
1.ResNet意义
随着网络的加深，出现了训练集准确率下降的现象，我们可以确定这不是由于Overfit过拟合造成的(过拟合的情况训练集应该准确率很高)；所以作者针对这个问题提出了一种全新的网络，叫深度残差网络，它允许网络尽可能的加深，其中引入了全新的结构如图1；
这里问大家一个问题
残差指的是什么？
其中ResNet提出了两种mapping：一种是identity mapping，指的就是图1中”弯弯的曲线”，另一种residual mapping，指的就是除了”弯弯的曲线“那部分，所以最后的输出是 y=F(x)+x
identity mapping顾名思义，就是指本身，也就是公式中的x，而residual mapping指的是“差”，也就是y−x，所以残差指的就是F(x)部分。
我们可以看到一个“弯弯的弧线“这个就是所谓的”shortcut connection“，也是文中提到identity mapping，这张图也诠释了ResNet的真谛,当然残差的结构可不会像图中这样单一，
下面是对通过Resnet 对cafir10数据的训练代码 以及网络结构图
import torch
import torch.nn as nn
import torchvision.datasets as normal_datasets
import torchvision.transforms as transforms
from torch.autograd import Variable
num_epochs = 2
lr = 0.001
def get_variable(x):
x = Variable(x)
return x.cuda() if torch.cuda.is_available() else x
# 图像预处理
transform = transforms.Compose([
transforms.Scale(40),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32),
transforms.ToTensor()])
# 加载CIFAR-10
train_dataset = normal_datasets.CIFAR10(root='./data/',
train=True,
transform=transform,
download=False)
test_dataset = normal_datasets.CIFAR10(root='./data/',
train=False,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=100,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=100,
shuffle=False)
# 3x3 卷积
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
# Residual Block
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(out_channels, out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 16
self.conv = conv3x3(3, 16)
self.bn = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self.make_layer(block, 16, layers[0])
self.layer2 = self.make_layer(block, 32, layers[0], 2)
self.layer3 = self.make_layer(block, 64, layers[1], 2)
self.avg_pool = nn.AvgPool2d(8)
self.fc = nn.Linear(64, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1,mm=0):
#print(out_channels,blocks,'****')
downsample = None
if (stride != 1) or (self.in_channels != out_channels):
downsample = nn.Sequential(
conv3x3(self.in_channels, out_channels, stride=stride),
nn.BatchNorm2d(out_channels))
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
mm+=1
self.in_channels = out_channels
for i in range(1, blocks):
layers.append(block(out_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv(x)
out = self.bn(out)
out = self.relu(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
resnet = ResNet(ResidualBlock, [2,2 ,2,2]) #blocks
print(resnet)
if torch.cuda.is_available():
resnet = resnet.cuda()
loss_func = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
# 训练
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = get_variable(images)
labels = get_variable(labels)
outputs = resnet(images)
loss = loss_func(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
print("Epoch [%d/%d], Iter [%d/%d] Loss: %.4f" % (epoch + 1, num_epochs, i + 1, 500, loss.data[0]))
# 衰减学习率
if (epoch + 1) % 20 == 0:
lr /= 3
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
# 测试
correct = 0
total = 0
for images, labels in test_loader:
images = get_variable(images)
labels = get_variable(labels)
outputs = resnet(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels.data).sum()
print(' 测试 准确率: %d %%' % (100 * correct / total))
# 保存模型参数
torch.save(resnet.state_dict(), 'resnet.pkl')网络结构
ResNet(
(conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(layer1): Sequential(
(0): ResidualBlock(
(conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): ResidualBlock(
(conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): ResidualBlock(
(conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResidualBlock(
(conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): ResidualBlock(
(conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): ResidualBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avg_pool): AvgPool2d(kernel_size=8, stride=8, padding=0)
(fc): Linear(in_features=64, out_features=10, bias=True)
)网络结构的图
虽然我是个学渣但是不妨碍我学习啊，希望这个图能帮助到有希望看具体网络连接图的你，