jupyter Resnet网络

ResNet是一个非常流行的深度学习模型，用于图像分类，目标检测和其他计算机视觉任务中。在jupyter中使用ResNet网络，可以通过以下步骤实现：

1. 安装PyTorch和torchvision

!pip install torch torchvision

2. 导入所需的库

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

3. 定义ResNet模型

class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.resnet = torchvision.models.resnet18(pretrained=True)
        self.fc = nn.Linear(1000, 10)

    def forward(self, x):
        x = self.resnet(x)
        x = self.fc(x)
        return x

4. 加载数据集并进行数据预处理

transform = 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)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,
                                          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=32,
                                         shuffle=False, num_workers=2)

5. 定义损失函数和优化器

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

6. 训练模型

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(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

7. 测试模型

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))

这样就可以在jupyter中使用ResNet网络进行图像分类任务了。