resnet18 cifar10

ResNet 是一种卷积神经网络，它通过使用残差块和shortcut连接来解决深度网络中的模型退化问题。与传统的卷积神经网络相比，ResNet 可以训练更深的网络，并且在准确度上取得了显著的提升。

在对 Cifar10 数据集进行学习训练时，可以按照以下步骤进行操作：

1. 数据集的处理：首先，加载 Cifar10 数据集，并将图像数据进行预处理，例如对图像进行归一化、裁剪等操作，以便于后续的训练过程。

2. 构建 ResNet 模型：使用 PyTorch 或其他深度学习框架，可以选择使用预训练的 ResNet18 模型或自行构建 ResNet18 模型。ResNet18 模型由多个残差块组成，每个残差块包含卷积层和恒等映射（identity mapping）层。通过堆叠这些残差块，可以构建深度为18的 ResNet 模型。

3. Loss 计算：选择适当的损失函数，例如交叉熵损失函数，用于度量模型的输出与真实标签之间的差异。在每个训练批次中，根据模型的输出和真实标签计算损失值。

4. 准确度计算：使用准确度指标（accuracy）来评估模型的性能。在每个训练批次中，将模型的预测结果与真实标签进行比较，并计算正确预测的比例。

5. 数据保存：在训练过程中，可以选择定期保存模型的参数和优化器的状态，以便于在训练结束后进行模型的评估和使用。

相关问题

pytorch resnet18 cifar10

ResNet-18是一个经典的深度残差网络，特别用于解决深层网络中的梯度消失和梯度爆炸问题。它通过引入残差块和跳跃连接来实现，使得梯度能够更容易地在网络中传播，从而有助于训练更深的网络。这个网络在深度学习领域取得了很大的成功。在PyTorch中，可以使用以下代码实现ResNet-18对CIFAR-10数据集进行分类：

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

# 数据导入
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=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')
class_nums = 10

# 定义ResNet-18网络
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != self.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * out_channels)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=class_nums):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

# 创建ResNet-18模型
net = ResNet(BasicBlock, [2, 2, 2, 2])

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 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

print('Finished Training')

# 在测试集上评估模型
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 on the test set: %d %%' % (100 * correct / total))

resnet18 cifar100 pytorch

ResNet18是一种深度卷积神经网络模型，适用于图像分类任务。CIFAR100是一个包含100个类别的图像分类数据集。在PyTorch中，可以使用ResNet18模型对CIFAR100数据集进行训练和测试。
使用ResNet18对CIFAR100数据集进行训练的参数和代码如下：

# 导入必要的库
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

# 加载数据集
train_dataset = datasets.CIFAR100(root='./data', train=True, download=True, transform=transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]))
test_dataset = datasets.CIFAR100(root='./data', train=False, download=True, transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]))
train_loader = DataLoader(train_dataset, batch_size=1024, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False)

# 定义模型
class ResNet18(nn.Module):
    def __init__(self, num_classes=100):
        super(ResNet18, self).__init__()
        self.resnet18 = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=False)
        self.resnet18.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.resnet18(x)
        return x

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ResNet18().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.002, momentum=0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[80, 120], gamma=0.1)
num_epochs = 200
best_acc = 0.0
for epoch in range(num_epochs):
    model.train()
    train_loss = 0.0
    train_acc = 0.0
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)
    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_acc / len(train_loader.dataset)
    model.eval()
    test_loss = 0.0
    test_acc = 0.0
    for i, (images, labels) in enumerate(test_loader):
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        loss = criterion(outputs, labels)
        test_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        test_acc += torch.sum(preds == labels.data)
    test_loss = test_loss / len(test_loader.dataset)
    test_acc = test_acc / len(test_loader.dataset)
    if test_acc > best_acc:
        best_acc = test_acc
        torch.save(model.state_dict(), './res/best_model.pth')
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(
        epoch + 1, num_epochs, train_loss, train_acc, test_loss, test_acc))
    scheduler.step()

# 测试模型
model.load_state_dict(torch.load('./res/best_model.pth'))
model.eval()
test_acc = 0.0
for i, (images, labels) in enumerate(test_loader):
    images = images.to(device)
    labels = labels.to(device)
    outputs = model(images)
    _, preds = torch.max(outputs, 1)
    test_acc += torch.sum(preds == labels.data)
test_acc = test_acc / len(test_loader.dataset)
print('Test Acc: {:.4f}'.format(test_acc))