用python写一段5层的卷积神经网络代码,用于图像分类
时间: 2023-02-06 20:25:20 浏览: 112
下面是一段 Python 代码,它实现了一个 5 层的卷积神经网络 (CNN) 用于图像分类:
```python
import torch
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.layer3 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer4 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer5 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(256 * 7 * 7, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
model = ConvNet()
print(model)
```
这里的卷积神经网络由 5 个卷积层和 1 个全连接层组成,每个卷积层后面都