VGG16和集成模型（LeNet，CNN，Net均匀池化）比较

 1 #导入需要的模块
 2 import torch
 3 import torch.nn as nn
 4 import torch.optim as optim
 5 import torch.nn.functional as F
 6 import torch.backends.cudnn as cudnn
 7 import numpy as np
 8 import torchvision
 9 import torchvision.transforms as transforms
10 from torch.utils.data import DataLoader
11 from collections import Counter
12 
13 #定义一些超参数 
14 BATCHSIZE=100
15 DOWNLOAD_MNIST=False
16 EPOCHES=20
17 LR=0.001

 1 #定义相关模型结构，这三个网络结构比较接近
 2 class CNNNet(nn.Module):
 3     def __init__(self):
 4         super(CNNNet,self).__init__()
 5         self.conv1 = nn.Conv2d(in_channels=3,out_channels=16,kernel_size=5,stride=1)
 6         self.pool1 = nn.MaxPool2d(kernel_size=2,stride=2)
 7         self.conv2 = nn.Conv2d(in_channels=16,out_channels=36,kernel_size=3,stride=1)
 8         self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
 9         self.fc1 = nn.Linear(1296,128)
10         self.fc2 = nn.Linear(128,10)      
11 
12     def forward(self,x):
13         x=self.pool1(F.relu(self.conv1(x)))
14         x=self.pool2(F.relu(self.conv2(x)))
15         #print(x.shape)
16         x=x.view(-1,36*6*6)
17         x=F.relu(self.fc2(F.relu(self.fc1(x))))
18         return x
19 
20 class Net(nn.Module):
21     def __init__(self):
22         super(Net, self).__init__()
23         self.conv1 = nn.Conv2d(3, 16, 5)
24         self.pool1 = nn.MaxPool2d(2, 2)
25         self.conv2 = nn.Conv2d(16, 36, 5)
26         #self.fc1 = nn.Linear(16 * 5 * 5, 120)
27         self.pool2 = nn.MaxPool2d(2, 2)
28         self.aap=nn.AdaptiveAvgPool2d(1)
29         #self.fc2 = nn.Linear(120, 84)
30         self.fc3 = nn.Linear(36, 10)
31 
32     def forward(self, x):
33         x = self.pool1(F.relu(self.conv1(x)))
34         x = self.pool2(F.relu(self.conv2(x)))
35         #print(x.shape)
36         #x = x.view(-1, 16 * 5 * 5)
37         x = self.aap(x)
38         #print(x.shape)
39         #x = F.relu(self.fc2(x))
40         x = x.view(x.shape[0], -1)
41         #print(x.shape)
42         x = self.fc3(x)
43         return x
44 
45 class LeNet(nn.Module):
46     def __init__(self):
47         super(LeNet, self).__init__()
48         self.conv1 = nn.Conv2d(3, 6, 5)
49         self.conv2 = nn.Conv2d(6, 16, 5)
50         self.fc1   = nn.Linear(16*5*5, 120)
51         self.fc2   = nn.Linear(120, 84)
52         self.fc3   = nn.Linear(84, 10)
53 
54     def forward(self, x):
55         out = F.relu(self.conv1(x))
56         out = F.max_pool2d(out, 2)
57         out = F.relu(self.conv2(out))
58         out = F.max_pool2d(out, 2)
59         out = out.view(out.size(0), -1)
60         out = F.relu(self.fc1(out))
61         out = F.relu(self.fc2(out))
62         out = self.fc3(out)
63         return out

 1 cfg = {
 2     ‘VGG16‘: [64, 64, ‘M‘, 128, 128, ‘M‘, 256, 256, 256, ‘M‘, 512, 512, 512, ‘M‘, 512, 512, 512, ‘M‘],
 3     ‘VGG19‘: [64, 64, ‘M‘, 128, 128, ‘M‘, 256, 256, 256, 256, ‘M‘, 512, 512, 512, 512, ‘M‘, 512, 512, 512, 512, ‘M‘],
 4 }
 5 
 6 
 7 class VGG(nn.Module):
 8     def __init__(self, vgg_name):
 9         super(VGG, self).__init__()
10         self.features = self._make_layers(cfg[vgg_name])
11         self.classifier = nn.Linear(512, 10)
12 
13     def forward(self, x):
14         out = self.features(x)
15         out = out.view(out.size(0), -1)
16         out = self.classifier(out)
17         return out
18 
19     def _make_layers(self, cfg):
20         layers = []
21         in_channels = 3
22         for x in cfg:
23             if x == ‘M‘:
24                 layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
25             else:
26                 layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
27                            nn.BatchNorm2d(x),
28                            nn.ReLU(inplace=True)]
29                 in_channels = x
30         layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
31         return nn.Sequential(*layers)

 1 device = torch.device(‘cuda‘ if torch.cuda.is_available() else ‘cpu‘)
 2 
 3 
 4 # Data
 5 print(‘==> Preparing data..‘)
 6 transform_train = transforms.Compose([
 7     transforms.RandomCrop(32, padding=4),
 8     transforms.RandomHorizontalFlip(),
 9     transforms.ToTensor(),
10     transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
11 ])
12 
13 transform_test = transforms.Compose([
14     transforms.ToTensor(),
15     transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
16 ])
17 
18 trainset = torchvision.datasets.CIFAR10(root=r‘D:\JupyterNotebook\PyTorch深度学习代码\PyTorch深度学习代码及数据\pytorch-04\data‘, train=True, download=False, transform=transform_train)
19 trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
20 
21 testset = torchvision.datasets.CIFAR10(root=r‘D:\JupyterNotebook\PyTorch深度学习代码\PyTorch深度学习代码及数据\pytorch-04\data‘, train=False, download=False, transform=transform_test)
22 testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
23 
24 classes = (‘plane‘, ‘car‘, ‘bird‘, ‘cat‘, ‘deer‘, ‘dog‘, ‘frog‘, ‘horse‘, ‘ship‘, ‘truck‘)
25 
26 # Model
27 print(‘==> Building model..‘)
28 net1 = CNNNet()
29 net2=Net()
30 net3=LeNet()
31 net4 = VGG(‘VGG16‘)

#把3个网络模型放在一个列表里
mlps=[net1.to(device),net2.to(device),net3.to(device)]

optimizer=torch.optim.Adam([{"params":mlp.parameters()} for mlp in mlps],lr=LR)
  
loss_function=nn.CrossEntropyLoss()
 
for ep in range(EPOCHES):
    for img,label in trainloader:
        
        img,label=img.to(device),label.to(device)
        optimizer.zero_grad()#10个网络清除梯度
        for mlp in mlps:
            mlp.train()
            out=mlp(img)
            loss=loss_function(out,label)
            loss.backward()#网络们获得梯度
        optimizer.step()
 
    pre=[]
    vote_correct=0
    mlps_correct=[0 for i in range(len(mlps))]
    for img,label in testloader:
        img,label=img.to(device),label.to(device)
        for i, mlp in  enumerate( mlps):
            mlp.eval()
            out=mlp(img)
 
            _,prediction=torch.max(out,1) #按行取最大值
            pre_num=prediction.cpu().numpy()
            mlps_correct[i]+=(pre_num==label.cpu().numpy()).sum()
 
            pre.append(pre_num)
        arr=np.array(pre)
        pre.clear()
        result=[Counter(arr[:,i]).most_common(1)[0][0] for i in range(BATCHSIZE)]
        vote_correct+=(result == label.cpu().numpy()).sum()
    print("epoch:" + str(ep)+"集成模型的正确率"+str(vote_correct/len(testloader)))
 
    for idx, coreect in enumerate( mlps_correct):
        print("模型"+str(idx)+"的正确率为："+str(coreect/len(testloader)))

mlps=[net4.to(device)]

optimizer=torch.optim.Adam([{"params":mlp.parameters()} for mlp in mlps],lr=LR)
  
loss_function=nn.CrossEntropyLoss()
 
for ep in range(EPOCHES):
    for img,label in trainloader:
        img,label=img.to(device),label.to(device)
        optimizer.zero_grad()#10个网络清除梯度
        for mlp in mlps:
            mlp.train()
            out=mlp(img)
            loss=loss_function(out,label)
            loss.backward()#网络们获得梯度
        optimizer.step()
 
    pre=[]
    vote_correct=0
    mlps_correct=[0 for i in range(len(mlps))]
    for img,label in testloader:
        img,label=img.to(device),label.to(device)
        for i, mlp in  enumerate( mlps):
            mlp.eval()
            out=mlp(img)
 
            _,prediction=torch.max(out,1) #按行取最大值
            pre_num=prediction.cpu().numpy()
            mlps_correct[i]+=(pre_num==label.cpu().numpy()).sum()
 
            pre.append(pre_num)
        arr=np.array(pre)
        pre.clear()
        result=[Counter(arr[:,i]).most_common(1)[0][0] for i in range(BATCHSIZE)]
        vote_correct+=(result == label.cpu().numpy()).sum()
    #print("epoch:" + str(ep)+"集成模型的正确率"+str(vote_correct/len(testloader)))
 
    for idx, coreect in enumerate( mlps_correct):
        print("VGG16模型迭代"+str(ep)+"次的正确率为："+str(coreect/len(testloader)))

VGG16模型迭代0次的正确率为：49.72
VGG16模型迭代1次的正确率为：64.76
VGG16模型迭代2次的正确率为：72.55
VGG16模型迭代3次的正确率为：75.42
VGG16模型迭代4次的正确率为：79.24
VGG16模型迭代5次的正确率为：79.82
VGG16模型迭代6次的正确率为：82.19
VGG16模型迭代7次的正确率为：82.14
VGG16模型迭代8次的正确率为：84.04
VGG16模型迭代9次的正确率为：84.61
VGG16模型迭代10次的正确率为：87.26
VGG16模型迭代11次的正确率为：85.57
VGG16模型迭代12次的正确率为：85.55
VGG16模型迭代13次的正确率为：86.79
VGG16模型迭代14次的正确率为：88.49
VGG16模型迭代15次的正确率为：87.19
VGG16模型迭代16次的正确率为：88.86
VGG16模型迭代17次的正确率为：88.56
VGG16模型迭代18次的正确率为：88.84
VGG16模型迭代19次的正确率为：88.19

from tensorboardX import SummaryWriter
input1 = torch.rand(128,3,32,32)
with SummaryWriter(log_dir=‘logs‘,comment=‘Net‘) as w:
    w.add_graph(net4,(input1,))

tensorboard --logdir=logs --port 6006