My model looks like this:

```
def conv_layer(ni,nf,kernel_size=3,stride=1):
return nn.Sequential(
nn.Conv2d(ni,nf,kernel_size=kernel_size,bias=False,stride=stride,padding=kernel_size//2),
nn.BatchNorm2d(nf,momentum=0.01),
nn.LeakyReLU(negative_slope=0.1,inplace=True)
)
class block1(nn.Module):
def __init__(self,ni):
super().__init__()
self.conv1 = conv_layer(ni,ni//2,kernel_size=1)
self.conv2 = conv_layer(ni//2,ni,kernel_size=3)
self.classifier = nn.Linear(ni*8*4,751)
def forward(self,x):
x = self.conv2(self.conv1(x))
x = x.view(x.size(0),-1)
return self.classifier(x)
class block2(nn.Module):
def __init__(self,ni):
super().__init__()
self.conv1 = conv_layer(ni,ni//2,kernel_size=1)
self.conv2 = conv_layer(ni//2,ni,kernel_size=3)
self.classifier = nn.Linear(ni*8*4,1360)
def forward(self,x):
x = self.conv2(self.conv1(x))
x = x.view(x.size(0),-1)
return self.classifier(x)
```

I want to take features from conv2 layer of both block1 and block2 and apply forbenius norm loss like this:

X =

where Cs denotes features from conv2 layer of block2 and Ct denotes features from conv2 layer of block1.

Covariance is

Xi,Xj are the features from different images.

I have to take covariance of positive samples only. Then using Hinge loss = max(0,X) I want to add this along my cross entropy loss function.