ResNet神经网络
- [定义ResNet Block](#定义ResNet Block)
- 定义ResNet18
- 加载数据集并训练、测试
定义ResNet Block
ResNet Block 的作用：
是一个残差块，用于构建ResNet
主要是为了解决神经网络中的梯度爆炸和梯度消失问题，以及缓解训练过程中的退化问题。
在传统的神经网络中，每层的输出会直接作为下一层的输入，可能会导致梯度在反向传播过程中逐渐减小，当层数比较深时，就可能导致梯度消失。故引入了跳跃连接，将每一层的输出与最初的x进行相加，当你对其进行求导，能发现比传统的多了一项对x的求导，也就是因为该项，避免了梯度消失的问题。
python
class ResBlk(nn.Module):
"""
resnet Block
"""
def __init__(self,ch_in,ch_out,stride):
super(ResBlk,self).__init__()
self.conv1 = nn.Conv2d(in_channels=ch_in,out_channels=ch_out,kernel_size=3,stride=stride,padding=1)
print(self.conv1)
self.bn1 = nn.BatchNorm2d(ch_out)
self.conv2 = nn.Conv2d(in_channels=ch_out, out_channels=ch_out, kernel_size=3, stride=1, padding=1)
print(self.conv2)
self.bn2 = nn.BatchNorm2d(ch_out)
self.extra =nn.Sequential()#当输入通道数并不等于输出通道数的时候，进行转换。
if ch_out != ch_in:
self.extra = nn.Sequential(
# [b,ch_in,h,w] =>[b,ch_out,h,w]
nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=stride),
nn.BatchNorm2d(ch_out)
)
def forward(self,x):
"""
:param x: [b,ch,h,w]
:return:
"""
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
#shor cut
# x :[b,ch_in,h,w] 而out [b,ch_out,h,w]
out = self.extra(x) +out #resNet的精髓所在，能够避免过拟合，梯度爆炸，梯度消失，
return out运行测试一下：
python
def main():
blk = ResBlk(64,128,stride=4)
tmp = torch.randn(2,64,32,32)
out = blk(tmp)
print(out.shape)
if __name__ == '__main__':
main()在这里说明一下其中的疑惑，在做该模块的时候
blk = ResBlk(64,128,stride=4) #64是输入通道数，128表示输出通道数。
tmp = torch.randn(2,64,32,32) # 2是样本数量，64是输入通道数，32是形状。
out = blk(tmp) #将其传入到ResBlok中，进行运算。
输出为torch.Size([2, 128, 8, 8])。
定义ResNet18
python
class ResNet18(nn.Module):
def __init__(self):
super(ResNet18,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3,64,kernel_size=3,stride=3,padding=0),
nn.BatchNorm2d(64)
)
# followed 4 blocks
# [b,64,h,w] => [b,128,h,w]
self.blk1 = ResBlk(64,128,stride=2)
# [b,128,h,w] => [b,256,h,w]
self.blk2 = ResBlk(128,256,stride=2)
# [b,256,h,w] => [b,512,h,w]
self.blk3 = ResBlk(256, 512,stride=2)
# [b,512,h,w] => [b,1024,h,w]
self.blk4 = ResBlk(512, 512,stride=2)
self.outlayer = nn.Linear(512,10)
def forward(self,x):
x = F.relu(self.conv1(x))
x = self.blk1(x)
x = self.blk2(x)
x = self.blk3(x)
x = self.blk4(x)
x = F.adaptive_avg_pool2d(x,[1,1])
x = x.view(x.size(0), -1)
x = self.outlayer(x)
return x加载数据集并训练、测试
python
import torch
import torchvision.transforms
from torch import nn, optim
from torchvision import datasets
from torch.utils.data import DataLoader
# from lenet5 import Lenet5
from learing_resnet import ResNet18
def main():
batchsz = 32
cifar_train= datasets.CIFAR10('data',train=True,transform=torchvision.transforms.Compose([
torchvision.transforms.Resize((32,32)),
torchvision.transforms.ToTensor()
]),download=True)
cifar_train = DataLoader(cifar_train,batch_size=batchsz,shuffle=True)
cifar_test= datasets.CIFAR10('data',train=False,transform=torchvision.transforms.Compose([
torchvision.transforms.Resize((32,32)),
torchvision.transforms.ToTensor()
]),download=True)
cifar_test = DataLoader(cifar_test,batch_size=batchsz,shuffle=True)
# x, label = iter(cifar_train)
# print("x:",x.shape,"label:",label.shape)
device = torch.device('cuda')
# model = Lenet5().to(device)
model = ResNet18().to(device)
criten = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(),lr=1e-3)
for epoch in range(1000):
for batchidx,(x,lable) in enumerate(cifar_train):
x,lable = x.to(device),lable.to(device)
logits = model(x)
loss = criten(logits,lable)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(epoch,loss.item())
total_correct = 0
total_num = 0
model.eval()
with torch.no_grad():
for x,label in cifar_test:
x,label = x.to(device),label.to(device)
logits = model(x)
pred = logits.argmax(dim=1)
total_correct += torch.eq(pred,label).float().sum().item()
total_num += x.size(0)
acc = total_correct /total_num
print(epoch,acc)
if __name__ == '__main__':
main()