根据提示,补充代码,构建出满足打印输出要求结当实例化结果打印时结构如下,即为通过测试: LeNet( (conv): Sequential( (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) (1): Sigmoid() (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1)) (4): Sigmoid() (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (fc): Sequential( (0): Linear(in_features=256, out_features=120, bias=True) (1): Sigmoid() (2): Linear(in_features=120, out_features=84, bias=True) (3): Sigmoid() (4): Linear(in_features=84, out_features=10, bias=True) ) )构的 LeNet 模型。
时间: 2023-09-04 17:10:03 浏览: 294
可以使用以下代码构建 LeNet 模型:
```python
import torch.nn as nn
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5),
nn.Sigmoid(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5),
nn.Sigmoid(),
nn.MaxPool2d(kernel_size=2)
)
self.fc = nn.Sequential(
nn.Linear(in_features=256, out_features=120),
nn.Sigmoid(),
nn.Linear(in_features=120, out_features=84),
nn.Sigmoid(),
nn.Linear(in_features=84, out_features=10)
)
def forward(self, x):
x = self.conv(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
lenet = LeNet()
print(lenet)
```
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