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VGG11由8個卷積，三個全連接組成，注意池化只改變特征圖大小，不改變通道數

給定x查看最后結果

x = torch.rand(128,3,224,224)
net = MyNet()
out = net(x)
print(out.shape)
#torch.Size([128, 1000])

class MyNet(nn.Module):
def __init__(self) -> None:
super().__init__()
#（1）conv3-64
self.conv1 = nn.Conv2d(
in_channels=3,
out_channels=64,
kernel_size=3,
stride=1,
padding=1 #! 不改變特征圖的大小
)
#! 池化只改變特征圖大小，不改變通道數
self.max_pool_1 = nn.MaxPool2d(2)
#（2）conv3-128
self.conv2 = nn.Conv2d(
in_channels=64,
out_channels=128,
kernel_size=3,
stride=1,
padding=1
)
self.max_pool_2 = nn.MaxPool2d(2)
#（3） conv3-256,conv3-256
self.conv3_1 = nn.Conv2d(
in_channels=128,
out_channels=256,
kernel_size=3,
stride=1,
padding=1)
self.conv3_2 = nn.Conv2d(
in_channels=256,
out_channels=256,
kernel_size=3,
stride=1,
padding=1
)
self.max_pool_3 = nn.MaxPool2d(2)
#（4）conv3-512,conv3-512
self.conv4_1 = nn.Conv2d(
in_channels=256,
out_channels=512,
kernel_size=3,
stride=1,
padding=1
)
self.conv4_2 = nn.Conv2d(
in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=1
)
self.max_pool_4 = nn.MaxPool2d(2)
#（5）conv3-512,conv3-512
self.conv5_1 = nn.Conv2d(
in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=1
)
self.conv5_2 = nn.Conv2d(
in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=1
)
self.max_pool_5 = nn.MaxPool2d(2)
#（6）
self.fc1 = nn.Linear(25088,4096)
self.fc2 = nn.Linear(4096,4096)
self.fc3 = nn.Linear(4096,1000)
def forward(self,x):
x = self.conv1(x)
print(x.shape)
x = self.max_pool_1(x)
print(x.shape)
x = self.conv2(x)
print(x.shape)
x = self.max_pool_2(x)
print(x.shape)
x = self.conv3_1(x)
print(x.shape)
x = self.conv3_2(x)
print(x.shape)
x = self.max_pool_3(x)
print(x.shape)
x = self.conv4_1(x)
print(x.shape)
x = self.conv4_2(x)
print(x.shape)
x = self.max_pool_4(x)
print(x.shape)
x = self.conv5_1(x)
print(x.shape)
x = self.conv5_2(x)
print(x.shape)
x = self.max_pool_5(x)
print(x.shape)
x = torch.flatten(x,1)
print(x.shape)
x = self.fc1(x)
print(x.shape)
x = self.fc2(x)
print(x.shape)
out = self.fc3(x)
return out

Import torch
from torch import nn