### VGG-11
class MyNet(nn.Module):
def __init__(self) -> None:
super().__init__() # 继承用法
self.conv1 = nn.Conv2d(
in_channels=1,
out_channels=6,
stride=1,
kernel_size=5,
padding=0
)
self.avg_pool_1 = nn.AvgPool2d(
stride=2,
kernel_size=2
)
self.conv2 = nn.Conv2d(
in_channels=6,
out_channels=16,
kernel_size=5,
stride=1,
padding=0
)
self.avg_pool_2 = nn.AvgPool2d(
kernel_size=2,
stride=2
)
self.fc1 = nn.Linear(in_features=16*4*4,out_features=120)
self.fc2 = nn.Linear(in_features=120,out_features=84)
self.fc3 = nn.Linear(in_features=84,out_features=10)
def forward(self,x):
x = self.conv1(x)
x = self.avg_pool_1(x)
x = self.conv2(x)
x = self.avg_pool_2(x)
# 此时格式为 [-,5,5,16]
# 卷积后必须flatten 才能全连接，经过flatten
x = torch.flatten(x,1) # [B,C,H,W] --> [-,C,H,W]
x = self.fc1(x)
x = self.fc2(x)
out = self.fc3(x)
return out
### LeNet-5
class MyNet(nn.Module):
def __init__(self) -> None:
super().__init__() # 继承用法
self.conv1 = nn.Conv2d(
in_channels=1,
out_channels=6,
stride=1,
kernel_size=5,
padding=0
)
self.avg_pool_1 = nn.AvgPool2d(
stride=2,
kernel_size=2
)
self.conv2 = nn.Conv2d(
in_channels=6,
out_channels=16,
kernel_size=5,
stride=1,
padding=0
)
self.avg_pool_2 = nn.AvgPool2d(
kernel_size=2,
stride=2
)
self.fc1 = nn.Linear(in_features=16*4*4,out_features=120)
self.fc2 = nn.Linear(in_features=120,out_features=84)
self.fc3 = nn.Linear(in_features=84,out_features=10)
def forward(self,x):
x = self.conv1(x)
x = self.avg_pool_1(x)
x = self.conv2(x)
x = self.avg_pool_2(x)
# 此时格式为 [-,5,5,16]
# 卷积后必须flatten 才能全连接，经过flatten
x = torch.flatten(x,1) # [B,C,H,W] --> [-,C,H,W]
x = self.fc1(x)
x = self.fc2(x)
out = self.fc3(x)
return out |
