@ptrblck  the values that I transmitted to you, are just the results of the convolution layers.
I want to substruct the results of the convolutional layers of x and x1.
 out = self.cnn1(x)
        out = self.batchnorm1(out)
        out = self.relu(out)
        out = self.maxpool1(out)
        out = self.cnn2(out)
        out = self.batchnorm2(out)
        out = self.relu(out)
        out = self.maxpool2(out)
        out = self.cnn3(out)
        out = self.batchnorm3(out)
        out = self.relu(out)
        out = self.cnn4(out)
        out = self.batchnorm4(out)
        out = self.relu(out)
        out = self.cnn5(out)
        out = self.batchnorm5(out)
        out = self.relu(out)
       
        out1 = self.cnn1(x2)
        out1 = self.batchnorm1(out1)
        out1 = self.relu(out1)
        out1 = self.maxpool1(out1)
        out1 = self.cnn2(out1)
        out1 = self.batchnorm2(out1)
        out1 = self.relu(out1)
        out1 = self.maxpool2(out1)
        out1 = self.cnn3(out1)
        out1 = self.batchnorm3(out1)
        out1 = self.relu(out1)
        out1 = self.cnn4(out1)
        out1 = self.batchnorm4(out1)
        out1 = self.relu(out1)
        out1 = self.cnn5(out1)
        out1 = self.batchnorm5(out1)
        out1 = self.relu(out1)
     
        out2 = out - out1
       print(out2)