一维resnet18
时间: 2024-04-23 14:21:07 浏览: 23
一维ResNet18是一种基于残差网络(ResNet)结构的深度学习模型,用于处理一维序列数据的分类或回归任务。ResNet18是ResNet系列中的一个较小的模型,具有较少的参数和计算量,适用于中等规模的数据集和计算资源。
一维ResNet18的主要特点包括:
1. 卷积层:一维ResNet18使用一维卷积层来提取输入序列的局部特征。这些卷积层可以捕捉到不同尺度的特征,从而提高模型的表达能力。
2. 残差块:为了解决深层网络训练过程中的梯度消失和梯度爆炸问题,一维ResNet18引入了残差块。每个残差块由两个卷积层和一个跳跃连接组成,跳跃连接将输入直接添加到残差块的输出上,使得信息可以更容易地传递。
3. 池化层:为了减小特征图的尺寸并提取更高级别的特征,一维ResNet18使用最大池化层对特征图进行下采样。
4. 全局平均池化:在最后一个残差块之后,一维ResNet18使用全局平均池化层将特征图转换为固定长度的向量,用于最终的分类或回归任务。
相关问题
pytorch实现二维resnet18
以下是PyTorch实现二维ResNet18的示例代码:
```
import torch
import torch.nn as nn
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_planes)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != out_planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_planes)
)
def forward(self, x):
out = nn.functional.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = nn.functional.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, 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
return nn.Sequential(*layers)
def forward(self, x):
out = nn.functional.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = nn.functional.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def ResNet18():
return ResNet(BasicBlock, [2,2,2,2])
```
这个代码实现了一个简单的二维ResNet18,由基本块(BasicBlock)组成。块中包含了两个卷积层和一个残差连接(shortcut),并且使用批量归一化(BatchNorm)来提高模型的稳定性。整个ResNet18模型由四个块(layer)组成,其中每个块包含了若干个基本块。在最后一个块后面添加了一个平均池化层和全连接层。
一维resnet代码复现pytorch
一维 ResNet 的 PyTorch 代码可以如下实现:
```python
import torch
import torch.nn as nn
# 定义基本残差块
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm1d(out_channels)
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.stride != 1 or identity.shape[1] != out.shape[1]:
identity = nn.Conv1d(identity.shape[1], out.shape[1], kernel_size=1, stride=self.stride)(identity)
out += identity
out = self.relu(out)
return out
# 定义一维 ResNet
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv1d(3, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm1d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
self.layer1 = self.make_layer(block, 64, layers[0])
self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Linear(512, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
layers = []
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels
for _ in range(1, blocks):
layers.append(block(out_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.maxpool(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avgpool(out)
out = torch.flatten(out, 1)
out = self.fc(out)
return out
# 创建一维 ResNet 模型
model = ResNet(ResidualBlock, [2, 2, 2, 2])
# 输出模型结构
print(model)
```