给出一个包含dropout层的卷积神经网络示例
时间: 2023-06-16 15:06:43 浏览: 47
这里有一个包含dropout层的卷积神经网络示例,可以用于图像分类任务:
```python
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self, num_classes=10):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.dropout = nn.Dropout(p=0.5)
self.fc1 = nn.Linear(8*8*32, 256)
self.fc2 = nn.Linear(256, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.dropout(out)
out = self.fc1(out)
out = self.fc2(out)
return out
```
这个网络包含两个卷积层和两个全连接层,中间使用了dropout层进行正则化,可以提高模型的泛化性能。其中,dropout的概率设置为0.5,表示在训练过程中,每个神经元有50%的概率被随机丢弃。