Hi I want to train this network and then add 2 convolutional layer after conv layer 5. I trained this network and code another network with same architecture but with 2 more conv layer and at first I copy the weights of similar layers to the new network and then freeze the same layers but new network does not work at all!
I initialized the 2 new conv layers that be the same as identity( model2.conv6[0].weight.data=torch.zeros((1,1,7,7))     model2.conv6[0].weight.data[0,0,3,3]=1     model2.conv6[0].bias.data=torch.zeros((1))     model2.conv7[0].weight.data=torch.zeros((1,1,3,3))     model2.conv7[0].weight.data[0,0,1,1]=1     model2.conv7[0].bias.data=torch.zeros((1)))
the network:
class Net(nn.Module):
    def __init__(self,SR,block_size,phi):
        super(MHCSResNet,self).__init__()
        self.conv1= nn.Sequential(
            nn.Conv2d(1,64, kernel_size=(9,9), stride=1,padding=4),
            nn.BatchNorm2d(64),
            nn.ReLU()
        )
        self.conv2= nn.Sequential(
            nn.Conv2d(64,32, kernel_size=(7,7), stride=1,padding=3),
            nn.BatchNorm2d(32),
            nn.ReLU()
        )
        self.conv3= nn.Sequential(
            nn.Conv2d(32,16, kernel_size=(5,5), stride=1,padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU()
        )
        self.conv4= nn.Sequential(
            nn.Conv2d(16, 8, kernel_size=(3, 3), stride=1,padding=1),
            nn.BatchNorm2d(8),
            nn.ReLU()
        )
        self.conv5= nn.Sequential(
            nn.Conv2d(8,1,kernel_size=(1,1),stride=1,padding=0),
            
        )
###########
        #I want to add two conv layer here:
  
        ############
        self.fc=nn.Linear(1600,64)

    def forward(self,kr,y,phi):
        out_conv1=self.conv1(kr)
        out_conv2=self.conv2(out_conv1)
        out_conv3=self.conv3(out_conv2)
        out_conv4=self.conv4(out_conv3)
        out_conv5=self.conv5(out_conv4)
        ###########
        #I want to add two conv layer here:
  
        ############
        out_feedback=kr+out_conv5
        out_linear=self.fc(out_feedback.flatten(2))

        return out_linear