基于卷积神经网络的CIFAR10分类识别

今天带来一个小小的PyTorch项目，利用PyTorch搭建卷积神经网络完成对CIFAR10数据集的分类

 

 

CIFAR10：由 10 个类中的 60000 张 32x32 彩色图像组成，每类 6000 张图像。有50000个训练图像和10000个测试图像。数据集分为五个培训批次和一个测试批次，每个测试批次有 10000 张
图像。测试批次包含来自每个类的 1000 个随机选择的图像。培训批次随机包含剩余图像，但某些培训批次可能包含来自一个班级的图像。培训批次包含每节课的 5000 张图片

十个类分别是：【飞机，汽车，鸟，猫，鹿，狗，青蛙，马，船，卡车】

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下面正式开始

首先我们先明确一下总体步骤：Load data->Build Model ->Train->Test

 1.Load data

在这里还是利用torchvision.datasets直接完成CIFAR10数据集的下载

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
train_data = torchvision.datasets.CIFAR10(root='./CIFAR10data',train=True,
            download=True,transform=transform )
train_loader = torch.utils.data.DataLoader(train_data,batch_size = 4,
            shuffle = True,num_workers=2)
test_data = torchvision.datasets.CIFAR10(root='./CIFAR10data',train=False,
            download=True,transform=transform)
test_loader = torch.utils.data.DataLoader(test_data,batch_size = 4,
            shuffle = False,num_workers=2)

 在这里我们将对图片的预处理直接整合好，对于shuffle和num_workers这两个属性在说明下：

shuffle：用于打乱数据集，每次以不同数据返回（这里好像还有不少坑，但是我还没亲自掉里面                  过，不过迟早的事，关于shuffle的坑以后有机会在进行详细阐述）

num_workers:当dataloader加载数据时，一次性创建num_workers个工作进程，并用                                    batch_sampler将指定batch分配给指定worker，worker将它负责的batch加载进RAM。

(1)num_workers设置的很大：好处是处理速度快，寻找速度快，可以需要的数据之前已经加载过了；坏处就是内存开销大，占用很多内存空间，加重CPU负担

(2)num_workers设置为0：意味着每一轮迭代时，dataloader不再有自主加载数据到RAM这一步骤，而是在RAM中找batch，找不到时再加载相应的batch：缺点是速度降下来了

2.Build Model

定义卷积神经网络模型

class Net(nn.Module):
    def __init__(self):
        super(Net, 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(16*5*5,120)
        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)))
        x = x.view(-1,16*5*5)  #拉成向量
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        return x

关于Conv2d()，参考了nn.Conv2d卷积_落地生根-CSDN博客

二维卷积可以处理二维数据

nn.Conv2d(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True))
参数：
  in_channel:　输入数据的通道数，例RGB图片通道数为3；
  out_channel: 输出数据的通道数，这个根据模型调整；
  kennel_size: 卷积核大小，可以是int，或tuple；kennel_size=2,意味着卷积大小(2,2)，                 kennel_size=（2,3），意味着卷积大小（2，3）即非正方形卷积
  stride：步长，默认为1，与kennel_size类似，stride=2,意味着步长上下左右扫描皆为2，                 stride=（2,3），左右扫描步长为2，上下为3；
  padding：对图像矩阵周边零填充

3.Train

Train之前定义一下优化器和损失函数

net  = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr = 0.001,momentum=0.9) #SGD(传入参数，学习率，动量）

start Training~

 for epoch in range(1):
        running_loss = 0.0
        # 0 用于指定索引起始值
        for i, data in enumerate(train_loader, 0):
            input, target = data
            input, target = Variable(input), Variable(target)
            optimizer.zero_grad()
            output = net(input)
            loss = criterion(output, target)  # out 和target的交叉熵损失
            loss.backward()
            optimizer.step()
            running_loss += loss.data

            if i % 2000 == 1999:  ## print every 2000 mini_batches,1999,because of index from 0 on
                print('[%d,%5d]loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0
        print('Finished Training')

训练完毕：

 

 4.Test

dataier = iter(test_loader)
        images, labels = dataier.next()
        imshow(torchvision.utils.make_grid(images))
        print('GroundTruth:', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
        outputs = net(Variable(images))
        _, pred = torch.max(outputs.data, 1)
        print('Predicted: ', ' '.join('%5s' % classes[pred[j]] for j in range(4)))

        correct = 0.0
        total = 0
        for data in test_loader:
            images, labels = data
            outputs = net(Variable(images))
            _, pred = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (pred == labels).sum()
        print('Accuracy of the network on the 10000 test images :%d %%' % (100 * correct / total))

这里还定义了一个函数用于展示一下数据集识别的图像

def imshow(img):
    img = img / 2 + 0.5   # unnormalize
    npimg = img.numpy()
    # np.transpose :按需求转置
    plt.imshow(np.transpose(npimg,(1,2,0)))
    plt.show()

因为CIFAR10中的图像像素都是32*32的，很小，所以这个观赏效果很模糊，不过大概能看得出来

可以看得出来，识别的准确度还是有的，不过样板数据少，其实真实的识别精度还不是很高

下面是对十类不同物体的分类分析

class_correct = list(0. for i in range(10))
        class_total = list(0. for i in range(10))
        for data in test_loader:
            images, labels = data
            outputs = net(Variable(images))
            _, pred = torch.max(outputs.data, 1)
            c = (pred == labels).squeeze()  # 1*10000*10-->10*10000
            for i in range(4):
                label = labels[i]
            class_correct[label] += c[i]
            class_total[label] += 1
        for i in range(10):
            print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))

 识别的结果会打印在Console

确实不算是很高啊，对于bird的识别竟然是0% ！！！！

每次学习的结果都不一样，如何正确提高识别精度，我还需要再努力学习！！

最后贴一下完整代码：
 

import torch
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
#导入数据并及进行标准化处理，转换成需要的格式

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
#下载数据
train_data = torchvision.datasets.CIFAR10(root='./CIFAR10data',train=True,
            download=True,transform=transform )
train_loader = torch.utils.data.DataLoader(train_data,batch_size = 4,
            shuffle = True,num_workers=2)
test_data = torchvision.datasets.CIFAR10(root='./CIFAR10data',train=False,
            download=True,transform=transform)
test_loader = torch.utils.data.DataLoader(test_data,batch_size = 4,
            shuffle = False,num_workers=2)

classes = ('plane','car','bird','cat','deer','dog','frog','horse','ship','truck')
def imshow(img):
    img = img / 2 + 0.5   # unnormalize
    npimg = img.numpy()
    # np.transpose :按需求转置
    plt.imshow(np.transpose(npimg,(1,2,0)))
    plt.show()
#定义卷积神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, 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(16*5*5,120)
        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)))
        x = x.view(-1,16*5*5)  #拉成向量
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        return x
net  = Net()
#定义loss函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr = 0.001,momentum=0.9) #SGD(传入参数，学习率，动量）
#训练网络
if __name__ == '__main__':

    for epoch in range(1):
        running_loss = 0.0
        # 0 用于指定索引起始值
        for i, data in enumerate(train_loader, 0):
            input, target = data
            input, target = Variable(input), Variable(target)
            optimizer.zero_grad()
            output = net(input)
            loss = criterion(output, target)  # out 和target的交叉熵损失
            loss.backward()
            optimizer.step()
          
            running_loss += loss.data

            if i % 2000 == 1999:  ## print every 2000 mini_batches,1999,because of index from 0 on
                print('[%d,%5d]loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0
        print('Finished Training')

        dataier = iter(test_loader)
        images, labels = dataier.next()
        imshow(torchvision.utils.make_grid(images))
        print('GroundTruth:', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
        outputs = net(Variable(images))
        _, pred = torch.max(outputs.data, 1)
        print('Predicted: ', ' '.join('%5s' % classes[pred[j]] for j in range(4)))

        correct = 0.0
        total = 0
        for data in test_loader:
            images, labels = data
            outputs = net(Variable(images))
            _, pred = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (pred == labels).sum()
        print('Accuracy of the network on the 10000 test images :%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 test_loader:
            images, labels = data
            outputs = net(Variable(images))
            _, pred = torch.max(outputs.data, 1)
            c = (pred == labels).squeeze()  # 1*10000*10-->10*10000
            for i in range(4):
                label = labels[i]
            class_correct[label] += c[i]
            class_total[label] += 1
        for i in range(10):
            print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))

 

 本片只是一味的贴了一下这个分类识别过程中具体实现代码，对于卷积神经网络的理解以及测试相关说明还不够，尤其是对于卷积神经网络中卷积层Conv2d的数据说明还不够透彻且网络最后识别精度不够高，学习路漫漫，感觉这期不太含有什么营养，只是简单的搬运项目代码

但还是感谢观看，如果有帮助到您，点个赞再走吧！

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