test=car[~car.index.isin(train.index)]

这段代码的作用是从数据集 `car` 中选择出不在训练集 `train` 中出现的样本，然后将这些样本赋值给变量 `test`。其中 `~` 表示取反，`car.index` 表示 `car` 数据集的索引，`train.index` 表示 `train` 数据集的索引，`isin()` 方法用于判断一个元素是否在另一个集合中出现。因此，`~car.index.isin(train.index)` 表示在 `car` 数据集的索引集合中但不在 `train` 数据集的索引集合中出现的元素。最后，使用这些元素对 `car` 数据集进行索引，即可得到 `test` 数据集。

相关问题

Pytorch中怎么使用vis.line 来画train_loss train_acc test_acc 这三条线在同一个图中 并给出一个在深度学习中以网络为resnet50 、数据集为cifar10、把输出的模型保存、输出的数据保存到csv文件中、把代码模块化的实战代码

首先，我们需要安装visdom库，可以使用以下命令：

!pip install visdom

接下来，需要在代码中引入相关库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import visdom
import csv

然后，定义一个函数来加载数据集：

def load_dataset():
    transform_train = transforms.Compose(
        [transforms.RandomCrop(32, padding=4),
         transforms.RandomHorizontalFlip(),
         transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    transform_test = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                            download=True, transform=transform_train)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                              shuffle=True, num_workers=2)

    testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                           download=True, transform=transform_test)
    testloader = torch.utils.data.DataLoader(testset, batch_size=128,
                                             shuffle=False, num_workers=2)

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

    return trainloader, testloader

接下来，定义一个函数来构建ResNet50网络：

class ResNet50(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet50, self).__init__()
        self.resnet50 = torchvision.models.resnet50(pretrained=False)
        num_ftrs = self.resnet50.fc.in_features
        self.resnet50.fc = nn.Linear(num_ftrs, num_classes)

    def forward(self, x):
        x = self.resnet50(x)
        return x

然后，定义一个函数来训练模型：

def train(model, trainloader, criterion, optimizer, epoch, device, vis):
    model.train()
    train_loss = 0.0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        if batch_idx % 10 == 0:
            vis.line(X=torch.Tensor([epoch+batch_idx/len(trainloader)]), Y=torch.Tensor([train_loss/len(trainloader)]), win='train_loss', update='append', name='train_loss')
            vis.line(X=torch.Tensor([epoch+batch_idx/len(trainloader)]), Y=torch.Tensor([100.*correct/total]), win='train_acc', update='append', name='train_acc')

    print('Train Epoch: {} Loss: {:.6f} Acc: {:.6f}'.format(
        epoch, train_loss/len(trainloader), 100.*correct/total))

接下来，定义一个函数来测试模型：

def test(model, testloader, criterion, epoch, device, vis):
    model.eval()
    test_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            if batch_idx % 10 == 0:
                vis.line(X=torch.Tensor([epoch+batch_idx/len(testloader)]), Y=torch.Tensor([100.*correct/total]), win='test_acc', update='append', name='test_acc')

    print('Test Epoch: {} Loss: {:.6f} Acc: {:.6f}'.format(
        epoch, test_loss/len(testloader), 100.*correct/total))

接下来，定义主函数来调用以上函数：

def main():
    vis = visdom.Visdom()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    trainloader, testloader = load_dataset()
    model = ResNet50().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

    for epoch in range(100):
        train(model, trainloader, criterion, optimizer, epoch, device, vis)
        test(model, testloader, criterion, epoch, device, vis)

    torch.save(model.state_dict(), 'resnet50_cifar10.pth')

    with open('output.csv', mode='w') as file:
        writer = csv.writer(file)
        writer.writerow(['epoch', 'train_loss', 'train_acc', 'test_acc'])
        for epoch in range(100):
            train_loss = vis.line(Y=torch.Tensor([0]), win='train_loss', name='train_loss', update='none')
            train_acc = vis.line(Y=torch.Tensor([0]), win='train_acc', name='train_acc', update='none')
            test_acc = vis.line(Y=torch.Tensor([0]), win='test_acc', name='test_acc', update='none')
            writer.writerow([epoch+1, train_loss['Y'][-1], train_acc['Y'][-1], test_acc['Y'][-1]])

最后，运行主函数即可进行训练、测试、模型保存和输出数据到csv文件的操作：

if __name__ == '__main__':
    main()

完整代码如下：

pytorchvggnet的实例

以下是一个使用PyTorch实现VGGNet的示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# Define the VGGNet architecture
class VGGNet(nn.Module):
    def __init__(self):
        super(VGGNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 1000),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# Load the CIFAR-10 dataset
transform = transforms.Compose(
    [transforms.Resize(256),
     transforms.CenterCrop(224),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (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=2)

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

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

# Define the optimizer and loss function
net = VGGNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Train the network
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

# Test the network on the test dataset
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()

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

此代码实现了一个VGGNet模型，用于分类CIFAR-10数据集的10个类别。该模型包含13个卷积层和3个全连接层，并使用交叉熵作为损失函数和随机梯度下降（SGD）作为优化器。在训练时，使用了两个epoch，并在每个mini-batch之后打印出运行损失。最后，测试了网络在测试数据集上的准确性。