Pytorc搭建AlexNet实现CAFAR10数据集分类（学习记录)

1.加载数据集：torchvision.dataset.CIFAR10

　　

• transform设置：transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5),(0.5, 0.5, 0.5))])将图像作为tensor输入，同时进行归一化处理，均值为0.5， 方差为0.5，三个分量分别为RGB值。
• DataLoader是pytorch常用数据加载函数，进行批次数据传送，batch_size的设定指明每次输入数据量，因此预测时与之对应

 

transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(0.5,0.5,0.5)])
trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
trainloader=torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=0)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=True, num_workers=0)

 

2.定义AlexNet网络结构

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()


        self.conv1=nn.Conv2d(3,6,5)
        self.pool1=nn.MaxPool2d(2,2)
        self.conv2=nn.Conv2d(6,16,5)
        self.fc1=nn.Linear(16*5*5,120)
        self.fc2=nn.Linear(120,84)
        self.fc3=nn.Linear(84,10)

    def forward(self,x):
        out=self.pool1(F.relu(self.conv1(x)))
        out=self.pool1(F.relu(self.conv2(out)))
        out=out.view(-1,16*5*5)
        out=F.relu(self.fc1(out))
        out=F.relu(self.fc2(out))
        out=self.fc3(out)
        return out
net=Net()
print(net)

3、定义损失函数和优化器

 

cost=nn.CrossEntropyLoss()

optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9)

4.训练网络

 

for epoch in range(2):
    running_loss=0.0
    for i,data in enumerate(trainloader,0):
        inputs,labels=data
        inputs,labels=Variable(inputs),Variable(labels)
        optimizer.zero_grad()
        outputs=net(inputs)
        loss=cost(outputs,labels)
        loss.backward()
        optimizer.step()

        running_loss+=loss.item()
        if i % 2000==1999:
            print('[%d, %5d]  loss: %.3f'%(epoch + 1, i + 1, running_loss / 2000))
            running_loss=0.001
print('done')

 

 5、测试整个预测结果

correct=0.0
total=0
for data in testloader:
    images,labels=data
    outputs=net(Variable(images))
    _,pred=torch.max(outputs.data,1)
    total+=labels.size(0)
    correct+=(pred==labels).sum()
print('average Accuracy: %d %%' %(100*correct / total))

6、展示各类别预测结果

class_correct =list(0. for i in range(10))
class_total=list(0. for i in range(10))
for data in testloader:
    images,labels=data
    outputs=net(Variable(images))
    _,pred=torch.max(outputs.data,1)
    c=(pred==labels).squeeze()

    for i in range(4):
        label=labels[i]
        class_correct[label]+=float(c[i])
        class_total[label]+=1
print('each class accuracy: \n')

7、实验结果

 

 

 8、完整代码

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms

import matplotlib.pyplot as plt
import numpy as np

def imshow(img):
    img=img/2+0.5
    np_img=img.numpy()
    plt.imshow(np.transpose(np_img,(1,2,0)))

transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize(0.5,0.5,0.5)])
trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
trainloader=torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=0)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=True, num_workers=0)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()


        self.conv1=nn.Conv2d(3,6,5)
        self.pool1=nn.MaxPool2d(2,2)
        self.conv2=nn.Conv2d(6,16,5)
        self.fc1=nn.Linear(16*5*5,120)
        self.fc2=nn.Linear(120,84)
        self.fc3=nn.Linear(84,10)

    def forward(self,x):
        out=self.pool1(F.relu(self.conv1(x)))
        out=self.pool1(F.relu(self.conv2(out)))
        out=out.view(-1,16*5*5)
        out=F.relu(self.fc1(out))
        out=F.relu(self.fc2(out))
        out=self.fc3(out)
        return out
net=Net()
print(net)

cost=nn.CrossEntropyLoss()

optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9)


for epoch in range(2):
    running_loss=0.0
    for i,data in enumerate(trainloader,0):
        inputs,labels=data
        inputs,labels=Variable(inputs),Variable(labels)
        optimizer.zero_grad()
        outputs=net(inputs)
        loss=cost(outputs,labels)
        loss.backward()
        optimizer.step()

        running_loss+=loss.item()
        if i % 2000==1999:
            print('[%d, %5d]  loss: %.3f'%(epoch + 1, i + 1, running_loss / 2000))
            running_loss=0.001
print('done')

correct=0.0
total=0
for data in testloader:
    images,labels=data
    outputs=net(Variable(images))
    _,pred=torch.max(outputs.data,1)
    total+=labels.size(0)
    correct+=(pred==labels).sum()
print('average Accuracy: %d %%' %(100*correct / total))

class_correct =list(0. for i in range(10))
class_total=list(0. for i in range(10))
for data in testloader:
    images,labels=data
    outputs=net(Variable(images))
    _,pred=torch.max(outputs.data,1)
    c=(pred==labels).squeeze()

    for i in range(4):
        label=labels[i]
        class_correct[label]+=float(c[i])
        class_total[label]+=1
print('each class accuracy: \n')

for i in range(10):
    print('Accuracy: %6s %2d %%' %(classes[i], 100 * class_correct[i] / class_total[i]))