Hi, I’m creating a splitter in my learner to group the different layers, and later look at training each group with a different learning rate. I’ve got a simple learner being built as shown below. But how can I verify that there are actually 3 groups created in my learner model? What’s the function or variable to look at?
learn.summary doesn’t tell you directly, right? (Unless you’re super familiar with the network architecture and can tell by looking at the trainable attribute).
I found that sequentially freezing the layers using learn.freeze_to(i), where i iterates from 1 to 3, and then looking at the non-trainable parameters value from learn.summary helps to verify this. Is there an easier way?
That seems like the right approach to me to verify where they are… I wonder if a modification could be made to it to provide a separate frozen section…
Just an observation for anyone else looking at the same: learn.summary only tells you the number of groups in the model if you run learn.freeze_to(x), learn.summary() where x is >= number of groups (and it’s told as a warning before the tabular output, since it’ll be freezing all groups then).
In case x is less than the number of groups, the only information you get is a Model frozen up to parameter group number x