使用pytorch搭建Lenet5模型,且在该模型的全连接层均使用dropout
时间: 2024-03-04 21:49:33 浏览: 13
好的,可以使用PyTorch搭建Lenet5模型,并在全连接层使用dropout。以下是代码示例:
```python
import torch.nn as nn
class Lenet5(nn.Module):
def __init__(self, dropout_rate):
super(Lenet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.dropout1 = nn.Dropout(p=dropout_rate)
self.fc2 = nn.Linear(120, 84)
self.dropout2 = nn.Dropout(p=dropout_rate)
self.fc3 = nn.Linear(84, 10)
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 = self.dropout1(x)
x = nn.functional.relu(self.fc2(x))
x = self.dropout2(x)
x = self.fc3(x)
return x
```
在这个Lenet5模型中,我们使用了`nn.Dropout`来添加dropout层。在构造函数中,我们通过`dropout_rate`参数来控制dropout层的丢弃概率。在前向传播中,我们使用`nn.functional.relu`来进行激活函数的计算。
希望这个代码示例能够对你有所帮助!