Hi @stas
When we do freeze , unfreeze , we do it to ‘layer groups’. We know ‘layer groups’ are groups of layers of a model. Different models may have different number of layer groups, some has 2, some has more.
The natural question to follow is, why do we use ‘layer groups’ instead of individual layers? Of course, it is much simpler to deal with a few layer groups instead of dozens or hundreds of layers. But how do the model designer choose which layers to group together and how many groups to have? What purpose does it serve besides convenience?
One small thing I want to check is whether learn.layer_groups comes with the Resnet model itself or it is a feature of fastai.
I digged a little into vision.models.resnet34 and found out the model has 4 types of ‘layers’ (rather look like learn.layer_groups ), but when looking into learn.model for its layer groups they are not quite the same. Also, learn has 3 layer groups, but Resnet34 has 4 so-called 'layer’s. Is there a relationship between Resnet34’s layer1 , layer2 , layer3 , layer4 with learn.layer_groups ? If so, what is it?
The paragraphs and links above are from my kaggle kernel, you can see details there.
Thanks!