How to apply layer-wise learning rate in Pytorch?
I know that it is possible to freeze single layers in a network for example to train only the last layers of a pre-trained model. What I’m looking for is a way to apply certain learning rates to different layers. 

So for example a very low learning rate of 0.000001 for the first layer and then increasing the learning rate gradually for each of the following layers. So that the last layer then ends up with a learning rate of 0.01 or so.

Is this possible in pytorch? Any idea how I can archive this?

Here is the solution:

from torch.optim import Adam

model = Net()

optim = Adam(
    [
        {"params": model.fc.parameters(), "lr": 1e-3},
        {"params": model.agroupoflayer.parameters()},
        {"params": model.lastlayer.parameters(), "lr": 4e-2},
    ],
    lr=5e-4,
)


Other parameters that are didn't specify in optimizer will not optimize. So you should state all layers or groups(OR the layers you want to optimize). and if you didn't specify the learning rate it will take the global learning rate(5e-4).
The trick is when you create the model you should give names to the layers or you can group it. 
