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Pytorch优化器-Optimizer

要使用PyTorch训练CIFAR-10数据集,可以按照以下步骤进行操作: 1. 导入所需的PyTorch库: ```python import torch import torchvision import torchvision.transforms as transforms import torch.nn as nn import torch.optim as optim ``` 2. 定义训练和测试数据集的转换方式: ```python transform=transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) ``` 3. 加载CIFAR-10数据集并创建数据加载器: ```python trainset=torchvision.datasets.CIFAR10(root='https://blog.csdn.net/weixin_43869493/article/details/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='https://blog.csdn.net/weixin_43869493/article/details/data', train=False, download=True, transform=transform) testloader=torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) ``` 4. 定义神经网络模型: ```python 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) x=F.relu(self.fc1(x)) x=F.relu(self.fc2(x)) x=self.fc3(x) return x net=Net() ``` 5. 定义损失函数和优化器: ```python criterion=nn.CrossEntropyLoss() optimizer=optim.SGD(net.parameters(), lr=0.001, momentum=0.9) ``` 6. 进行模型的训练和测试: ```python for epoch in range(2): running_loss=0.0 for i, data in enumerate(trainloader, 0): # 获取输入 inputs, labels=data # 梯度清零 optimizer.zero_grad() # 正向传播、反向传播、优化 outputs=net(inputs) loss=criterion(outputs, labels) loss.backward() optimizer.step() # 统计损失 running_loss +=loss.item() if i % 2000==1999: print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss=0.0 print('Finished Training') # 在测试集上测试模型 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: %.2f %%' % (100 * correct / total)) ``` 通过以上步骤,我们可以使用PyTorch训练CIFAR-10数据集,并获得模型的准确率。

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