Vgg梳理

Author：张一极 2020-0323-00:27

结构：

多个卷积多尺度的堆叠。

Code：

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class vgg(nn.Module):
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    def __init__(self,input_channel,num_classes):
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        super(vgg,self).__init__()
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        self.head_layer = conv_(3,64,kernel_size = 1,stride =1,padding=0)
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        self.layer_2 = conv_(64,64,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_3 = nn.MaxPool2d(2,stride = 2,padding = 0)
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        self.layer_4 = conv_(64,128,kernel_size= 3,stride= 1,padding = 1)
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        self.layer_5 = conv_(128,128,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_6 = nn.MaxPool2d(2,stride = 2)
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        self.layer_7 = conv_(128,256,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_8 = conv_(256,256,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_9 = conv_(256,256,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_10 = nn.MaxPool2d(2,stride = 2)
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        self.layer_11 = conv_(256,512,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_12 = conv_(512,512,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_13 = conv_(512,512,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_14 = nn.MaxPool2d(2,stride = 2)
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        self.layer_15 = conv_(512,512,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_16 = conv_(512,512,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_17 = conv_(512,512,kernel_size = 3,stride = 1,padding = 1)
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        self.layer_18 = nn.MaxPool2d(2,stride = 2)
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        self.dense = nn.Sequential(
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        nn.Linear(25088,4096),
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        nn.ReLU(True),
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        nn.Dropout(0.5),
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        nn.Linear(4096,4096),
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        nn.ReLU(True),
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        nn.Dropout(0.5),
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        nn.Linear(4096,10)
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        )
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    def forward(self,x):
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        x = self.head_layer(x)
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        x = self.layer_2(x)
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        x = self.layer_3(x)
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        x = self.layer_4(x)
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        x = self.layer_5(x)
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        x = self.layer_6(x)
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        x = self.layer_7(x)
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        x = self.layer_8(x)
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        x = self.layer_9(x)
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        x = self.layer_10(x)
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        x = self.layer_11(x)
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        x = self.layer_12(x)
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        x = self.layer_13(x)
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        x = self.layer_14(x)
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        x = self.layer_15(x)
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        x = self.layer_16(x)
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        x = self.layer_17(x)
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        x = self.layer_18(x)
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        x = x.view(x.shape[0], -1)
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        print(x.shape)
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        logits = self.dense(x)
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        probas = F.softmax(x, dim=1)
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        return logits,probas

实验🧪going

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