[人工智能]VGG16实现Cifar10分类（PyTorch）

VGG16实现Cifar10分类（PyTorch）

import os
import ssl
import torch
import torchvision
import torchvision.transforms as transforms
import math
import torch
import torch.nn as nn

if __name__ == '__main__':

    ssl._create_default_https_context = ssl._create_unverified_context
    ########################################
    #第1步：载入数据
    ########################################

    #使用torchvision可以很方便地下载cifar10数据集，而torchvision下载的数据集为[0, 1]的PILImage格式，我们需要将张量Tensor归一化到[-1, 1]

    transform = transforms.Compose(
        [transforms.ToTensor(), #将PILImage转换为张量
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] #将[0, 1]归一化到[-1, 1]
         )

    trainset = torchvision.datasets.CIFAR10(root='./book/classifier_cifar10/data', #root表示cifar10的数据存放目录，使用torchvision可直接下载cifar10数据集，也可直接在https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz这里下载（链接来自cifar10官网）
                                            train=True,
                                            download=True,
                                            transform=transform #按照上面定义的transform格式转换下载的数据
                                            )
    trainloader = torch.utils.data.DataLoader(trainset,
                                              batch_size=4, #每个batch载入的图片数量，默认为1
                                              shuffle=True,
                                              num_workers=2 #载入训练数据所需的子任务数
                                              )

    testset = torchvision.datasets.CIFAR10(root='./book/classifier_cifar10/data',
                                           train=False,
                                           download=True,
                                           transform=transform)
    testloader = torch.utils.data.DataLoader(testset,
                                             batch_size=4,
                                             shuffle=False,
                                             num_workers=2)

    cifar10_classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
    #
    # ########################################
    # #查看训练数据
    # #备注：该部分代码可以不放入主函数
    # ########################################
    import numpy as np

    dataiter = iter(trainloader) #随机从训练数据中取一些数据
    print(trainloader)
    images, labels = dataiter.next()
    images.shape #(4L, 3L, 32L, 32L)
    #我们可以看到images的shape是4*3*32*32，原因是上面载入训练数据trainloader时一个batch里面有4张图片

    torchvision.utils.save_image(images[1],"test.jpg") #我们仅随机保存images中的一张图片看看
    print( cifar10_classes[labels[3]] )#打印label
########################################
# 第2步：构建卷积神经网络
########################################

    cfg = {'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']}


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

            # 构建网络的卷积层和池化层，最终输出命名features，原因是通常认为经过这些操作的输出为包含图像空间信息的特征层
            self.features = self._make_layers(cfg[net_name])

            # 构建卷积层之后的全连接层以及分类器
            self.classifier = nn.Sequential(
                nn.Dropout(),
                nn.Linear(512, 512),  # fc1
                nn.ReLU(True),
                nn.Dropout(),
                nn.Linear(512, 512),  # fc2
                nn.ReLU(True),
                nn.Linear(512, 10),  # fc3，最终cifar10的输出是10类
            )
            # 初始化权重
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                    m.weight.data.normal_(0, math.sqrt(2. / n))
                    m.bias.data.zero_()

        def forward(self, x):
            x = self.features(x)  # 前向传播的时候先经过卷积层和池化层
            x = x.view(x.size(0), -1)
            x = self.classifier(x)  # 再将features（得到网络输出的特征层）的结果拼接到分类器上
            return x

        def _make_layers(self, cfg):
            layers = []
            in_channels = 3
            for v in cfg:
                if v == 'M':
                    layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
                else:
                    # conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
                    # layers += [conv2d, nn.ReLU(inplace=True)]
                    layers += [nn.Conv2d(in_channels, v, kernel_size=3, padding=1),
                               nn.BatchNorm2d(v),
                               nn.ReLU(inplace=True)]
                    in_channels = v
            return nn.Sequential(*layers)


    net = VGG('VGG16')

    ########################################
    # 第3步：定义损失函数和优化方法
    ########################################
    import torch.optim as optim

    # x = torch.randn(2,3,32,32)
    # y = net(x)
    # print(y.size())
    criterion = nn.CrossEntropyLoss()  # 定义损失函数：交叉熵
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  # 定义优化方法：随机梯度下降

    ########################################
    # 第4步：卷积神经网络的训练
    ########################################
    for epoch in range(5):  # 训练数据集的迭代次数，这里cifar10数据集将迭代2次
        train_loss = 0.0
        for batch_idx, data in enumerate(trainloader, 0):
            # 初始化
            inputs, labels = data  # 获取数据
            optimizer.zero_grad()  # 先将梯度置为0

            # 优化过程
            outputs = net(inputs)  # 将数据输入到网络，得到第一轮网络前向传播的预测结果outputs
            loss = criterion(outputs, labels)  # 预测结果outputs和labels通过之前定义的交叉熵计算损失
            loss.backward()  # 误差反向传播
            optimizer.step()  # 随机梯度下降方法（之前定义）优化权重

            # 查看网络训练状态
            train_loss += loss.item()
            if batch_idx % 2000 == 1999:  # 每迭代2000个batch打印看一次当前网络收敛情况
                print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, train_loss / 2000))
                train_loss = 0.0

        print('Saving epoch %d model ...' % (epoch + 1))
        state = {
            'net': net.state_dict(),
            'epoch': epoch + 1,
        }
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(state, './checkpoint/cifar10_epoch_%d.ckpt' % (epoch + 1))

    print('Finished Training')
    ########################################
    # 第5步：批量计算整个测试集预测效果
    ########################################
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()  # 当标记的label种类和预测的种类一致时认为正确，并计数

    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

    # 结果打印：Accuracy of the network on the 10000 test images: 73 %

    ########################################
    # 分别查看每个类的预测效果
    ########################################
    class_correct = list(0. for i in range(10))
    class_total = 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 = (predicted == labels).squeeze()
            for i in range(4):
                label = labels[i]
                class_correct[label] += c[i].item()
                class_total[label] += 1

    for i in range(10):
        print('Accuracy of %5s : %2d %%' % (
            cifar10_classes[i], 100 * class_correct[i] / class_total[i]))

训练打印结果

训练模型

训练模型下载地址

训练时间比较长，如果不想训练，可以直接下载
将下载的jar包解压，并放在根目录下面
将代码中的步骤四注释即可