用pyhon代码表示:将LeNet-5改进为单类识别的神经网络,修改全连接部分,改为只有1个输出
时间: 2024-05-01 17:18:44 浏览: 16
抱歉,我可以回答这个问题。以下是将LeNet-5改进为单类识别的神经网络的Python代码示例:
```python
import torch.nn as nn
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 1) # output layer with single binary output
def forward(self, x):
x = self.pool1(nn.functional.relu(self.conv1(x)))
x = self.pool2(nn.functional.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = nn.functional.sigmoid(self.fc3(x)) # output layer with single binary output
return x
```
这里的关键修改是将全连接层的输出单元个数从原来的10改为1,并添加一个sigmoid激活函数作为输出层,以输出二元分类结果。