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How to have different learning rates for the parts of a network in Pytorch?

I've got a segmentation network, which contains an encoder and multiple decoders. I want to have different learning rates for each part. How can I make it happen?

Computer VisionAI Development/Engineering
learning-ratepytorch
3 votesJW297.00
1 Answers
JO266.00
3

First, you need to define those parts in different classes to have different names. After that, you just filter and collect those parts by their layer-names.

encoder = []
decoder = []
for name, param in net.named_parameters():
  if 'encoder' in name:
    encoder.append(param)
  elif 'decoder' in name:
    decoder.append(param)

Then you pass those params to the optimizer separately, where you can treat them separately too.

optimizer = torch.optim.SGD([{'params':encoder}, {'params':decoder}], lr=DEFOULT_LRATE, nesterov=True)
...
optimizer.param_groups[0]['lr'] = L_RATE1
optimizer.param_groups[1]['lr'] = L_RATE2
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