I have the following network
resnet18 = models.resnet18(pretrained=True)
fc_ftrs = resnet18.fc.in_features
resnet18.fc = nn.Linear(fc_ftrs,self.numClasses)

I want to use small learning rate at for the base of my network (finetuning) and different for the fully connected.
If my optimizer is defined as follows :
RMSprop([{resnet18.parameters(), 'lr': 1e-6}, {resnet18.fc.parameters(), 'lr': 5e-4}])

will both learning rates added up for the fully connected layer or will the second override?