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
```