怎么搭建ResNet模型

ResNet是一种非常流行的深度神经网络模型，其通过使用残差连接来解决了深层网络训练中梯度消失和梯度爆炸的问题，从而实现了更深的网络结构。

要搭建一个ResNet模型，你可以按照以下步骤进行：

1. 确定模型的深度和宽度，即网络的层数和每层的卷积核数量。
2. 搭建基础模块，即残差块。一个残差块包括两个卷积层和一个跨越连接。在跨越连接上，如果输入和输出形状不同，则需要进行相应的卷积或池化操作，以保证形状一致。
3. 将多个残差块按照一定的规则连接起来，形成完整的ResNet模型。在ResNet中，通常会在网络的前端加入一个较浅的卷积层和池化层，用于对输入图像进行初步处理。

除此之外，你还需要选择合适的损失函数、优化器和学习率等超参数，并对模型进行训练和调优。这些工作都需要一定的深度学习知识和实践经验。

相关问题

基于pytorch搭建resnet模型

好的，您可以参考下面的代码：

import torch.nn as nn

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

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


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 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.linear = nn.Linear(512*block.expansion, num_classes)

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

    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = nn.AvgPool2d(4)(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

这是一个基于PyTorch搭建ResNet18模型的例子。其中，BasicBlock是ResNet18的基本模块，ResNet是整个网络的结构体，包含多个BasicBlock，_make_layer函数用于构建多层BasicBlock，forward函数定义前向传播过程，ResNet18函数返回一个ResNet18模型的实例。您可以根据自己的需求修改或扩展该模型。

基于pytorch搭建resnet模型的步骤

好的，下面是基于PyTorch搭建ResNet模型的步骤：

1. 导入必要的库和模块

   import torch.nn as nn

2. 定义ResNet的基本模块

ResNet的基本模块是BasicBlock，包含两个卷积层和一个shortcut连接。其中，shortcut连接可以是一个恒等映射或者一个1×1卷积层。这里我们使用恒等映射作为shortcut连接。

   class BasicBlock(nn.Module):
       expansion = 1
   
       def __init__(self, in_planes, planes, stride=1):
           super(BasicBlock, self).__init__()
           self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
           self.bn1 = nn.BatchNorm2d(planes)
           self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
           self.bn2 = nn.BatchNorm2d(planes)
   
           self.shortcut = nn.Sequential()
           if stride != 1 or in_planes != self.expansion*planes:
               self.shortcut = nn.Sequential(
                   nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                   nn.BatchNorm2d(self.expansion*planes)
               )
   
       def forward(self, x):
           out = nn.ReLU()(self.bn1(self.conv1(x)))
           out = self.bn2(self.conv2(out))
           out += self.shortcut(x)
           out = nn.ReLU()(out)
           return out

3. 定义ResNet的整体结构

ResNet的整体结构由多个BasicBlock组成，包含一个卷积层、一个BatchNorm层、四个BasicBlock层、一个全局平均池化层和一个全连接层。

   class ResNet(nn.Module):
       def __init__(self, block, num_blocks, num_classes=10):
           super(ResNet, self).__init__()
           self.in_planes = 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.linear = nn.Linear(512*block.expansion, num_classes)
   
       def _make_layer(self, block, planes, num_blocks, stride):
           strides = [stride] + [1]*(num_blocks-1)
           layers = []
           for stride in strides:
               layers.append(block(self.in_planes, planes, stride))
               self.in_planes = planes * block.expansion
           return nn.Sequential(*layers)
   
       def forward(self, x):
           out = nn.ReLU()(self.bn1(self.conv1(x)))
           out = self.layer1(out)
           out = self.layer2(out)
           out = self.layer3(out)
           out = self.layer4(out)
           out = nn.AvgPool2d(4)(out)
           out = out.view(out.size(0), -1)
           out = self.linear(out)
           return out

4. 定义ResNet18模型

ResNet18模型是一个包含多个BasicBlock的ResNet模型，其中包括两个3×3卷积层和一个1×1卷积层。我们可以使用ResNet类来定义ResNet18模型。

   def ResNet18():
       return ResNet(BasicBlock, [2,2,2,2])

5. 实例化模型并进行训练

在实例化ResNet18模型之后，我们可以使用PyTorch提供的优化器和损失函数对模型进行训练，如下所示：

   import torch.optim as optim
   
   device = 'cuda' if torch.cuda.is_available() else 'cpu'
   
   net = ResNet18().to(device)
   
   criterion = nn.CrossEntropyLoss()
   optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
   
   for epoch in range(100):
       net.train()
       for batch_idx, (inputs, targets) in enumerate(trainloader):
           inputs, targets = inputs.to(device), targets.to(device)
           optimizer.zero_grad()
           outputs = net(inputs)
           loss = criterion(outputs, targets)
           loss.backward()
           optimizer.step()
   
       net.eval()
       test_loss = 0
       correct = 0
       with torch.no_grad():
           for batch_idx, (inputs, targets) in enumerate(testloader):
               inputs, targets = inputs.to(device), targets.to(device)
               outputs = net(inputs)
               test_loss += criterion(outputs, targets).item()
               _, predicted = outputs.max(1)
               correct += predicted.eq(targets).sum().item()
   
       test_loss /= len(testloader.dataset)
       print('Epoch: {} | Test Loss: {:.3f} | Test Acc: {:.3f}'.format(epoch, test_loss, 100.*correct/len(testloader.dataset)))

在训练过程中，我们首先将模型移动到GPU上，然后定义交叉熵损失函数和随机梯度下降优化器。在每个epoch中，我们先将模型设置为训练模式，然后对训练集进行迭代，并在每个batch上计算损失和梯度，并更新模型的参数。接着，我们将模型设置为评估模式，对测试集进行迭代，并在每个batch上计算损失和精度。最后，我们输出每个epoch的测试损失和测试精度。