PyTorch入门

• LeNet-5
• AlexNet
• VGG-16
• GoogleNet
• ResNet
• 其他
• 参考链接

LeNet-5
LeNet-5 可以进行MNIST手写数字识别

1. 网络基本结构
   input (1)? \longrightarrow ? conv1 (6)? \longrightarrow ?pool1 (6)? \longrightarrow ? conv2 (16)? \longrightarrow ? pool2 (16)? \longrightarrow ? fc3? \longrightarrow ? fc4? \longrightarrow ? fc5? \longrightarrow ? softmax
   括号内的数字代表该层的通道数
   
   文章图片
   
   
2. 特点
   
   1. 卷积层-池化层被认为能够提取输入图像的平移不变特征
3. 损失函数的曲线图如下：
   

文章图片
上代码

#CNN进行MNIST手写数字集的识别 import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.data as data import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt#Hyper parameters LR = 0.01 EPOCH = 2 BATCH_SIZE = 50 DOWNLOAD_MNIST = False transforms = transforms.ToTensor() #准备训练集和测试集 train_set = torchvision.datasets.MNIST( root='./data/MNIST', train=True, transform=transforms, download=DOWNLOAD_MNIST ) train_loader = data.DataLoader( dataset=train_set, batch_size=BATCH_SIZE, shuffle=True ) #测试集只取2000个数据 test_set = torchvision.datasets.MNIST( root = './data/MNIST', train=False, transform=transforms, download=DOWNLOAD_MNIST)test_loader = data.DataLoader( dataset=test_set, batch_size= 2000, shuffle=True ) test_loader = iter(test_loader).next()#搭建网络 class Net(nn.Module): def __init__(self): super(Net,self).__init__() self.conv1 = nn.Sequential(#input shape: (1,28,28)(C,H,W) nn.Conv2d(1,16,5,1,2),# ->(16,28,28) nn.ReLU(), nn.MaxPool2d(2,2)# ->(16,14,14) ) self.conv2 = nn.Sequential( nn.Conv2d(16,32,5,1,2),# ->(32,14,14) nn.ReLU(), nn.MaxPool2d(2,2)# ->(32,7,7) ) # self.fc1 = nn.Linear(32*7*7,400)# ->(1,400) # self.fc2 = nn.Linear(400,120)# ->(1,120) # self.fc3 = nn.Linear(120,10)# ->(1,10) self.fc = nn.Linear(32*7*7,10)def forward(self,x): # x = self.conv1(x) # x = self.conv2(x) # x = x.view(-1,32*7*7) # x = F.relu(self.fc1(x)) # x = F.relu(self.fc2(x)) # x = self.fc3(x) # return x x = self.conv1(x) x = self.conv2(x) x = x.view(-1,32*7*7) x = self.fc(x) return xnet = Net()#定义损失函数和优化器criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(net.parameters(),lr=LR,betas=(0.9,0.99))#训练网络 loss_his = [] for epoch in range(EPOCH): for i,(inputs,labels) in enumerate(train_loader): #inputs: 图像 Tensor(50,1,28,28) #labels: 标签 Tensor(50)optimizer.zero_grad() outputs = net(inputs)#Tensor(50,10) #loss是一个批次内所有样本loss的平均值 #loss是一个值（1维的Tensor） loss = criterion(outputs,labels) loss.backward() optimizer.step() loss_his.append(loss)if (i % 50) == 0: #每50批次计算一次loss和accuracy并输出 inputs,labels = test_loader outputs = net(inputs) _, predicted = torch.max(outputs,1) accuracy = (predicted == labels).sum().item()/labels.size(0)*100 print('Epoch: ',epoch,'|loss: %.3f'%(loss.data.numpy()),'|Accuracy: %.2f%%'%(accuracy))print('Training Finished !')#显示预测示例 images,labels = test_loader images = images[:10] labels = labels[:10] outputs = net(images) _,predicted = torch.max(outputs,1) print('GroundTruth:\n',labels.numpy()) print('Predicted Types:\n',predicted.numpy())plt.plot(loss_his) plt.xlabel('iteration') plt.ylabel('Loss') plt.show()

输出结果：

Epoch:0 |loss: 2.310 |Accuracy: 9.55% Epoch:0 |loss: 0.372 |Accuracy: 87.20% Epoch:0 |loss: 0.260 |Accuracy: 94.40% Epoch:0 |loss: 0.037 |Accuracy: 95.90% Epoch:0 |loss: 0.077 |Accuracy: 96.75% Epoch:0 |loss: 0.072 |Accuracy: 95.80% Epoch:0 |loss: 0.049 |Accuracy: 96.75% Epoch:0 |loss: 0.087 |Accuracy: 96.25% Epoch:0 |loss: 0.104 |Accuracy: 97.10% Epoch:0 |loss: 0.197 |Accuracy: 96.65% Epoch:0 |loss: 0.033 |Accuracy: 97.20% Epoch:0 |loss: 0.196 |Accuracy: 97.10% Epoch:0 |loss: 0.015 |Accuracy: 96.95% Epoch:0 |loss: 0.047 |Accuracy: 96.95% Epoch:0 |loss: 0.119 |Accuracy: 97.30% Epoch:0 |loss: 0.075 |Accuracy: 96.60% Epoch:0 |loss: 0.081 |Accuracy: 97.35% Epoch:0 |loss: 0.048 |Accuracy: 96.50% Epoch:0 |loss: 0.025 |Accuracy: 97.20% Epoch:0 |loss: 0.024 |Accuracy: 96.65% Epoch:0 |loss: 0.004 |Accuracy: 97.30% Epoch:0 |loss: 0.023 |Accuracy: 97.25% Epoch:0 |loss: 0.004 |Accuracy: 97.50% Epoch:0 |loss: 0.170 |Accuracy: 97.90% Epoch:1 |loss: 0.024 |Accuracy: 96.95% Epoch:1 |loss: 0.181 |Accuracy: 96.95% Epoch:1 |loss: 0.036 |Accuracy: 97.65% Epoch:1 |loss: 0.082 |Accuracy: 97.75% Epoch:1 |loss: 0.026 |Accuracy: 97.30% Epoch:1 |loss: 0.127 |Accuracy: 97.75% Epoch:1 |loss: 0.220 |Accuracy: 96.80% Epoch:1 |loss: 0.412 |Accuracy: 97.55% Epoch:1 |loss: 0.166 |Accuracy: 97.70% Epoch:1 |loss: 0.022 |Accuracy: 97.40% Epoch:1 |loss: 0.047 |Accuracy: 97.75% Epoch:1 |loss: 0.002 |Accuracy: 97.55% Epoch:1 |loss: 0.023 |Accuracy: 97.45% Epoch:1 |loss: 0.046 |Accuracy: 97.80% Epoch:1 |loss: 0.084 |Accuracy: 97.35% Epoch:1 |loss: 0.090 |Accuracy: 97.75% Epoch:1 |loss: 0.005 |Accuracy: 97.85% Epoch:1 |loss: 0.188 |Accuracy: 97.40% Epoch:1 |loss: 0.068 |Accuracy: 97.80% Epoch:1 |loss: 0.023 |Accuracy: 97.40% Epoch:1 |loss: 0.114 |Accuracy: 97.45% Epoch:1 |loss: 0.040 |Accuracy: 97.80% Epoch:1 |loss: 0.072 |Accuracy: 97.40% Epoch:1 |loss: 0.004 |Accuracy: 98.05% Training Finished ! GroundTruth: [0 4 1 0 5 9 3 8 5 6] Predicted Types: [0 4 1 0 5 9 3 8 5 6]

AlexNet

1. 网络基本结构：
   input (3,224,224)? \longrightarrow ? conv1 (96,55,55)? \longrightarrow ?pool1 (96,27,27)? \longrightarrow ? conv2 (256,27,27)? \longrightarrow ? pool2 (256,13,13)? \longrightarrow ? conv3 (384,13,13)? \longrightarrow ? conv4 (384,13,13)? \longrightarrow ? conv5(256,13,13)? \longrightarrow ? pool (256,6,6)? \longrightarrow ? dropout? \longrightarrow ? fc6 (4096)? \longrightarrow ? dropout? \longrightarrow ? fc7 (4096)? \longrightarrow ? fc8 (1000)? \longrightarrow ? softmax
   括号中表示每层激活值的shape，即（C, H, W）
   
   文章图片
   
   
2. 特点
   (1) 使用ReLU激活函数，解决梯度消失的问题
   (2) 使用Dropout层来进行正则化，防止网络的过拟合
   (3) 采用两个GPU进行并行运算
   (4) 采用Local Response Normalization (LRN) 局部响应归一化层
   

上代码

#AlexNet网络结构import torch import torchvision import torch.nn as nn import torchvision.transforms as tranforms import numpy as np#搭建网络 class Net(nn.Module): def __init__(self,num_classes): super(Net,self).__init__() self.conv1 = nn.Sequential(# -> (224,224,3) nn.Conv2d(3,96,11,4),# -> (55,55,96) nn.ReLU(), nn.MaxPool2d(3,2)# -> (27,27,96) ) self.conv2 = nn.Sequential( nn.Conv2d(96,256,5,1,2),# -> (27,27,256) nn.ReLU(), nn.MaxPool2d(3,2)# -> (13,13,256) ) self.conv3 = nn.Sequential( nn.Conv2d(256,384,3,1,1),# -> (13,13,384) nn.ReLU() ) self.conv4 = nn.Sequential( nn.Conv2d(384,384,3,1,1),# -> (13,13,384) nn.ReLU() ) self.conv5 = nn.Sequential( nn.Conv2d(384,256,3,1,1),# -> (13,13,256) nn.ReLU(), nn.MaxPool2d(3,2),# -> (6,6,256) nn.Dropout() ) self.fc6 = nn.Sequential( nn.Linear(256*6*6,4096),# -> (4096) nn.ReLU(), nn.Dropout() ) self.fc7 = nn.Sequential( nn.Linear(4096,4096),# -> (4096) nn.ReLU() ) self.fc8 = nn.Linear(4096,num_classes)def forward(self,x): x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) x = self.conv5#ssss(x) x = x.view(x.size(0),6*6*256) x = self.fc6(x) x = self.fc7(x) x = self.fc8(x) return xnet = Net(1000) print(net)

VGG-16

1. 网络基本结构
   
   文章图片
   
   
   文章图片
   

2.特点
（1）卷积核采用小卷积核：3 x 3 卷积 2 x 2 池化
（2）卷积层都是same convolution，不改变图片的高和宽，只将通道数进行成倍增加
（3）池化层的参数设置： f = 2 ，s = 2，p = 0，所以图片经过池化后高和宽都减半
（4）参数量大：138milion 个参数，大部分集中在全连接层
GoogleNet
GoogleNet又叫InceptionNet，网络主要由Inception 模块组成
Inception 模块： 由若干个不同尺寸的过滤器组成，运用这些过滤器对输入进行卷积和池化，然后将输出结果按照深度拼接在一起形成输出层

1. 网络基本结构
   如下图
   
   文章图片
   
   
   
2. 特点
   （1）采用多个过滤器： 有1x1、3x3、5x5三种尺寸的卷积和3x3的池化，不需要人为的去设计过滤器的尺寸，相当于让网络自己去学习过滤器的种类和尺寸
   （2）采用1x1卷积： 1x1卷积在不改变图片高和宽的前提下，可以压缩通道的数目，从而达到减少参数的数量和计算过程中的乘法次数
   
   文章图片
   
   

上代码

#GoogleNet网络结构 # GoogleNet也叫InceptionNet，整个网络有多个Inception 模块组成#这里只搭建Inception模块 import torch import torch.nn as nnclass InceptionNet(nn.Module): def __init__(self,in_channels): super(InceptionNet,self).__init__() self.branch1x1 = nn.Sequential( nn.Conv2d(in_channels,64,1), nn.BatchNorm2d(64,eps = 0.001), nn.ReLU() ) self.branch3x3 = nn.Sequential( nn.Conv2d(in_channels,96,1), nn.BatchNorm2d(96, eps = 0.001), nn.ReLU(), nn.Conv2d(96, 128, 3, 1, 1), nn.BatchNorm2d(128, eps=0.001), nn.ReLU() ) self.branch5x5 = nn.Sequential( nn.Conv2d(in_channels,16,1), nn.BatchNorm2d(16, eps = 0.001), nn.ReLU(), nn.Conv2d(16,32,5,1,2), nn.BatchNorm2d(32, eps=0.001), nn.ReLU() ) self.branch_pool = nn.Sequential( nn.MaxPool2d(3,1,1), nn.BatchNorm2d(in_channels, eps=0.001), nn.ReLU(), nn.Conv2d(in_channels,32,1), nn.BatchNorm2d(32, eps=0.001), nn.ReLU() )def forward(self,x): branch_1x1 = self.branch1x1(x) branch_3x3 = self.branch3x3(x) branch_5x5 = self.branch5x5(x) branch_pool= self.branch_pool(x) branch = [branch_1x1,branch_3x3,branch_5x5,branch_pool] x = torch.cat(branch,1) return xnet = InceptionNet(3) print(net)

ResNet
ResNet 提出 残差模块（Residual Block）, 解决网络加深后训练难度增大的现象，有效缓解反向传播时由于深度过深导致的梯度消失现象，这使得网络加深之后性能不会变差。

1. 基本结构
   
   文章图片
   
   
2. 特点
   （1）Residual Block： 随着深度的增加，模型在训练集上的正确率不会下降，而误差会一直减小，从而使得可以训练更深层的神经网络
   （2）大量使用批处理化： Batch Normalization
   
3. 准确率
   使用ResNet去进行CIFAR10数据集的图片分类
   

• 训练集上的accuracy：90.00 %
• 测试集上的accuracy：78.68 %

上代码
ResNet.py

#ResNet网络#接口：ResNet(layers, num_classes) # layers: 每个残差层中残差模块的数目 # num_classes:分类的类别数import torch import torch.nn as nn import torch.nn.functional as F#先搭建残差模块 class ResModule(nn.Module): def __init__(self,in_channels,out_channels,stride = 1): super(ResModule,self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(in_channels,out_channels,3,stride,1), nn.BatchNorm2d(out_channels, eps = 0.001), nn.ReLU() ) self.conv2 = nn.Sequential( nn.Conv2d(out_channels,out_channels,3,1,1), nn.BatchNorm2d(out_channels, eps = 0.001)) #对residual进行卷积，保证residual和x 在各个维度上的值相等 self.downsample = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, stride, 1), nn.BatchNorm2d(out_channels, eps=0.001) )def forward(self,x): residual = x x = self.conv1(x) x = self.conv2(x) if(x.size(1) != residual.size(1)) or (x.size(2) != residual.size(2)): # x的在各个维度上的值发生变化时 residual = self.downsample(residual) x = F.relu(x+residual) returnx#再搭建残差网络 class ResNet(nn.Module): def __init__(self,layers,num_classes): #block: 残差模块类 # layers: 每个残差层中残差模块的数目 # num_classes:分类的类别数 super(ResNet,self).__init__() self.in_channels = 16 self.conv = nn.Sequential(# -> (3,32,32) nn.Conv2d(3,self.in_channels,3,1,1),# -> (16,32,32) nn.BatchNorm2d(self.in_channels, eps = 0.001), nn.ReLU() ) self.resLayer1 = self.make_layer(ResModule,layers[0],32,1)# -> (32,32,32) self.resLayer2 = self.make_layer(ResModule, layers[1], 64,2)# -> (64,16,16) self.resLayer3 = self.make_layer(ResModule, layers[2], 128,2)# -> (128,8,8) self.maxPool = nn.MaxPool2d(2,2)# -> (128,4,4) self.fc1 = nn.Linear(128*4*4,500)# -> (500) self.fc2 = nn.Linear(500,120)# -> (120) self.fc3 = nn.Linear(120,num_classes)# -> (num_classes)def make_layer(self,block,num_modules,out_channels,stride = 1): #block: 残差模块类 #num_modules: 需要搭建的残差模块的数目resLayer = [] resLayer.append( block(self.in_channels,out_channels,stride) ) for i in range(num_modules): resLayer.append( block(out_channels,out_channels) ) self.in_channels = out_channelsreturn nn.Sequential( *resLayer )def forward(self,x): x = self.conv(x) x = self.resLayer1(x) x = self.resLayer2(x) x = self.resLayer3(x) x = self.maxPool(x) x = x.view(x.size(0),-1) x = self.fc1(x) x = self.fc2(x) x = self.fc3(x) return x

classification_CIFAR10.py

#使用残差结构来图像分类 #数据集：CIFAR10 import ResNet import torch import torch.nn as nn import torch.functional as F import torchvision import torchvision.transforms as transforms import torch.utils.data as data import numpy as np import matplotlib.pyplot as plt#超参数 EPOCH = 20 LR = 0.01 BATCH_SIZE = 60 DOWNLOAD = False#准备数据集CIFAR10 def get_data(): #数据转换器 train_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5]) # transforms.Scale(4), # transforms.RandomHorizontalFlip(), # transforms.RandomCrop(32) ] ) test_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5])] )train_set = torchvision.datasets.CIFAR10( root='./data/CIFAR10', train = True, download=DOWNLOAD, transform = train_transform ) train_loader = data.DataLoader( dataset=train_set, batch_size=BATCH_SIZE, shuffle=True ) test_set = torchvision.datasets.CIFAR10( root='./data/CIFAR10', train = False, download=DOWNLOAD, transform = test_transform ) test_loader = data.DataLoader( dataset=test_set, batch_size=BATCH_SIZE, shuffle=True ) return train_loader,test_loader#搭建网络 net = ResNet.ResNet([3,4,5],10) net = net.cuda() #定义优化器和损失函数 criterion = nn.CrossEntropyLoss() criterion = criterion.cuda() optimizer = torch.optim.Adam(net.parameters(), lr = LR, betas=(0.9,0.99))train_loader,test_loader = get_data() # #训练网络 # print('开始训练网络...\n') # print('网络是否在cuda上训练:\n') # print(next(net.parameters()).is_cuda) # losses_his = [] # for epoch in range(EPOCH): #for i, (images,labels) in enumerate(train_loader): #images = images.cuda() #labels = labels.cuda() #optimizer.zero_grad() #outputs = net(images) #loss = criterion(outputs,labels) #loss.backward() #optimizer.step() # #losses_his.append(loss.item()) # #if (i+1) % 50 == 0: ##每隔50批次输出一次loss和accuracy #_, predicted = torch.max(outputs,1) #correct = (predicted == labels).sum().item() #trian_accuracy = correct/labels.size(0)*100 # #print('Epoch: %d | Batch: [%d,%d] | Loss: %.4f | Accuracy: %.2f %% '%(epoch,i+1,1000,loss.data.item(),trian_accuracy) )# torch.save(net.state_dict(),'./models/ResNet_CIFAR10.pkl') net.load_state_dict(torch.load('./models/ResNet_CIFAR10.pkl'))#测试网络 num_correct = 0 total_test = 0 net.eval() print('开始测试网络...') with torch.no_grad(): for (images,labels) in test_loader: images = images.cuda() labels = labels.cuda() outputs = net(images) _, predicted = torch.max(outputs,1) num_correct += (predicted == labels).sum().item() total_test += labels.size(0) test_accuracy = num_correct/ total_test*100 print('Test set: Accuracy: %.2f %%'%(test_accuracy))#输出部分测试用例 classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truch') num_show = 10#展示的测试用例个数 #test_loader是个可迭代数据对象，每个元素包含两个张量。 # 一个是图像张量，其维度为：（60,3,32,32）; 一个是标签张量，其维度是：（60） data = https://www.it610.com/article/iter(test_loader).next() images,labels = data#images是图片张量，维度：(60,3,32,32)labels是标签张量，维度：(60) images = images[:num_show] labels = labels[:num_show] images = images.cuda() labels = labels.cuda() outputs = net(images) _, predicted = torch.max(outputs,1) print('Ground Truth:') print(' '.join('%5s'% classes[labels[i]] for i in range(num_show))) print('Predicted Types:') print(' '.join('%5s'% classes[predicted[i]] for i in range(num_show)))#可视化 plt.plot(losses_his) plt.title('Loss Function Curve') plt.xlabel('iteration') plt.ylabel('Loss') plt.show()

输出

Test set: Accuracy: 78.68 % Ground Truth: cat planedog planecardeerdog truchcardog Predicted Types: cat planedog planecardeerdog truchcarcat

其他

#使用CNN进行图像分类 #数据集：CIFAR-10 import torch importtorch.nn as nn#构建网络的常用包 import torchvision#包含常用数据集包 import torchvision.transforms as transforms#数据格式转化包#加载和标准化CIFAR10训练集和测试集#先定义转换器 transforms = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))]) voc_rot = '../../Dataset/CIFAR10' #准备训练集和测试集 trainset = torchvision.datasets.CIFAR10(root=voc_rot, train = True,transform = transforms,download = False) trainloader = torch.utils.data.DataLoader(trainset,batch_size = 4,shuffle = True)testset = torchvision.datasets.CIFAR10(root=voc_rot, train = False,transform = transforms,download = False) testloader = torch.utils.data.DataLoader(testset,batch_size = 4,shuffle = True)#共有10中类别的图片 classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')#预先展示一些训练图像 import matplotlib.pyplot as plt#数据可视化 import numpy as np#展示图像 def imshow(img): img = img * 0.5 + 0.5#逆标准化 np_img = img.numpy()#转numpy()对象 plt.imshow(np.transpose(np_img,(1,2,0))) #维度转换#搭建网络 import torch.nn.functional as Fclass cnnNet(nn.Module): def __init__(self): #定义各个层，相当于一些处理函数 super(cnnNet, self).__init__() self.conv1 = nn.Conv2d(3,6,5) self.pool = nn.MaxPool2d(2,2) self.conv2 = nn.Conv2d(6,16,5) self.L1 = nn.Linear(16*5*5,120) self.L2 = nn.Linear(120,84) self.L3 = nn.Linear(84,10)def forward(self,x): #定义前馈函数，反向传播函数被自动通过autograd定义 x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1,16*5*5) x = F.relu(self.L1(x)) x = F.relu(self.L2(x)) x = self.L3(x) return xnet = cnnNet() # 定义Loss函数和优化器import torch.optim as optimcriterion = nn.CrossEntropyLoss()#Loss函数 opt_Momentum = optim.SGD(net.parameters(),lr = 0.001,momentum=0.9)#训练网络 for epoch in range(2): running_loss = 0.0 for i, data in enumerate(trainloader,0): inputs, labels = data #开始迭代 opt_Momentum.zero_grad() outputs = net(inputs) loss = criterion(outputs,labels) loss.backward() opt_Momentum.step() #显示每次训练结果 running_loss += loss.item() if i % 1000 == 999: running_loss = running_loss/1000 print('[%d, %d] loss: %.3f'%(epoch+1, i+1,running_loss)) running_loss = 0.0##print('网络模型训练完成') # torch.save(net.state_dict(),'CNN.pkl') #导入已经训练好的网络# net.load_state_dict(torch.load('./model/CNN.pkl'))#在测试数据上测试网络#Step 1: 个例测试，每组4张图片 dataIter = iter(testloader) images ,labels = dataIter.next() print('images的维度:\n',images.size()) print(labels) imshow(torchvision.utils.make_grid(images)) print('测试用例：') print('GroundTruth: ') print(' '.join('%5s' % classes[labels[j]] for j in range(4))) plt.show()outputs = net(images) _,predicted = torch.max(outputs.data,1) print('Predicted Types: ') print(' '.join('%5s' % classes[predicted[j]] for j in range(4)))#STEP 2: 计算正确率#总体的正确率 total = 0#总样本数 correct = 0#统计预测正确的个数 with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _,predicted = torch.max(outputs,1) total += labels.size(0) correct += (labels == predicted).sum().item()accuracy = correct/total *100 print('Total accuracy: ') print('Accuracy of CNN on the %s test images: %s'%(total,accuracy))#每类图片的预测的正确率 class_total = list(0.for i in range(10)) class_correct = list(0. for i in range(10))with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs,1) c = (labels == predicted) for i in range(4): class_total[labels[i]] += 1 if c[i].item() == 1: class_correct[labels[i]] += 1print('Class accuracy: ') for i in range(10): print('Accuracy of %5s on the %5s test images: %s'%(classes[i],class_total[i],100*class_correct[i]/class_total[i]))

参考链接

【PyTorch入门（二）卷积神经网络的案例分析 AlexNet、VGGNet、GoogLeNet、ResNet】https://zhuanlan.zhihu.com/p/94694563

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