Pytorch用LeNet在CIFAR10数据集上实现分类

Myth丶恋晨 2023-07-12 08:49 18阅读 0赞

# 本文主要讲解该算法的实现过程 #

### 实验环境 ###

python3.6+pytorch1.2+cuda10.1

### 数据集 ###

CIFAR10数据集包含6万训练图像与1万验证图像

## 下边是代码实现过程及讲解 ##

### 数据加载 ###

#载入数据
    def loadCIFAR10(batch_size):
        transform = transforms.Compose(
            [transforms.ToTensor(),
             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
    
        trainset = CIFAR10('./data',train=True,transform=transform,download=True)
        testset = CIFAR10('./data',train=False,transform=transform,download=True)
    
        trainloader = DataLoader(trainset,batch_size=batch_size,shuffle=True,num_workers=2)
        testloader = DataLoader(testset,batch_size=batch_size,shuffle=False,num_workers=2)
    
        return trainset,testset,trainloader,testloader

### 定义网络结构 ###

class Lenet(nn.Module):
        def __init__(self):
            super(Lenet,self).__init__()
            self.conv1 = nn.Conv2d(3,6,5)
            self.pool = nn.MaxPool2d(2,2)
            self.conv2 = nn.Conv2d(6,16,5)
            self.fc1 = nn.Linear(400,120)
            self.fc2 = nn.Linear(120, 84)
            self.fc3 = nn.Linear(84, 10)
    
        def forward(self,x):
            x = self.pool(F.relu(self.conv1(x)))
            x = self.pool(F.relu(self.conv2(x)))
            # print(2,x.size())
            x = x.view(-1,400)
            x = F.relu(self.fc1(x))
            # print(3,x.size())
            x = F.relu(self.fc2(x))
            x = self.fc3(x)
            return x

### 参数设定 ###

learning_rate = 1e-3
    batch_size = 10
    epoches = 50
    
    lenet = Lenet()
    lenet.cuda()
    
    trainset,testset,trainloader,testloader = loadCIFAR10(batch_size)
    print(len(trainset))
    criterian = nn.CrossEntropyLoss()
    optimizer = optim.SGD(lenet.parameters(),lr=learning_rate,momentum=0.9)

### 网络训练 ###

for epoch in range(epoches):
        running_loss = 0.0
        for i,data in enumerate(trainloader,0):
            inputs,labels = data
            inputs = inputs.cuda()
            labels = labels.cuda()
            optimizer.zero_grad()
            # print(0,inputs.size())
            outputs = lenet(inputs)
    
            loss = criterian(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 = 0.0

### 结果检验 ###

correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images,labels = data
            images = images.cuda()
            labels = labels.cuda()
            outputs = lenet(images)
            _,predict = torch.max(outputs.data,1)
            total += labels.size(0)
            correct += (predict==labels).sum().item()
    print('Acc: %.2f%%'%(100*correct/total))

### 结果展示 ###

![20200307204804155.jpg][]

迭代了50次最后结果也才刚过60%，主要原因是模型太简单了，如果要进一步提高正确率要考虑更换模型

### 完整代码 ###

import torch
    import torchvision
    from torch.utils.data import DataLoader
    from torchvision.datasets import CIFAR10
    from torchvision import transforms
    from torch.autograd import Variable
    from torch import optim
    import  torch.nn as nn
    import  torch.nn.functional as F
    import matplotlib.pyplot as plt
    import numpy as np
    import pylab
    
    classes = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
    
    
    #载入数据
    def loadCIFAR10(batch_size):
        transform = transforms.Compose(
            [transforms.ToTensor(),
             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
    
        trainset = CIFAR10('./data',train=True,transform=transform,download=True)
        testset = CIFAR10('./data',train=False,transform=transform,download=True)
    
        trainloader = DataLoader(trainset,batch_size=batch_size,shuffle=True,num_workers=2)
        testloader = DataLoader(testset,batch_size=batch_size,shuffle=False,num_workers=2)
    
        return trainset,testset,trainloader,testloader
    
    
    class Lenet(nn.Module):
        def __init__(self):
            super(Lenet,self).__init__()
            self.conv1 = nn.Conv2d(3,6,5)
            self.pool = nn.MaxPool2d(2,2)
            self.conv2 = nn.Conv2d(6,16,5)
            self.fc1 = nn.Linear(400,120)
            self.fc2 = nn.Linear(120, 84)
            self.fc3 = nn.Linear(84, 10)
    
        def forward(self,x):
            x = self.pool(F.relu(self.conv1(x)))
            x = self.pool(F.relu(self.conv2(x)))
            # print(2,x.size())
            x = x.view(-1,400)
            x = F.relu(self.fc1(x))
            # print(3,x.size())
            x = F.relu(self.fc2(x))
            x = self.fc3(x)
            return x
    
    learning_rate = 1e-3
    batch_size = 10
    epoches = 50
    
    lenet = Lenet()
    lenet.cuda()
    
    trainset,testset,trainloader,testloader = loadCIFAR10(batch_size)
    print(len(trainset))
    criterian = nn.CrossEntropyLoss()
    optimizer = optim.SGD(lenet.parameters(),lr=learning_rate,momentum=0.9)
    
    
    for epoch in range(epoches):
        running_loss = 0.0
        for i,data in enumerate(trainloader,0):
            inputs,labels = data
            inputs = inputs.cuda()
            labels = labels.cuda()
            optimizer.zero_grad()
            # print(0,inputs.size())
            outputs = lenet(inputs)
    
            loss = criterian(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 = 0.0
    
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images,labels = data
            images = images.cuda()
            labels = labels.cuda()
            outputs = lenet(images)
            _,predict = torch.max(outputs.data,1)
            total += labels.size(0)
            correct += (predict==labels).sum().item()
    print('Acc: %.2f%%'%(100*correct/total))

[20200307204804155.jpg]: https://img-blog.csdnimg.cn/20200307204804155.jpg