根据你上述的结构,搭建基于pytorch的网络结构
时间: 2024-01-21 14:03:25 浏览: 20
好的,以下是基于PyTorch的网络结构代码,实现上述的网络结构:
```python
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 输入层
self.input_layer = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=0),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=0),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 卷积层1
self.conv1 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=0),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size=2, stride=2)
)
# 卷积层2
self.conv2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=0),
nn.ReLU(),
nn.BatchNorm2d(128)
)
# 全连接层
self.fc = nn.Sequential(
nn.Linear(128 * 3 * 3, 64),
nn.ReLU(),
nn.Linear(64, 20)
)
def forward(self, x):
x = self.input_layer(x)
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
这个网络总共有5层卷积层和2层全连接层,其中前3层为卷积层和池化层的组合,后面2层为全连接层。每个卷积层后都接有ReLU激活函数和BatchNormalization层。最后一个全连接层输出20个类别的概率分布。