I've read this post, but I wanted more clarification for a broader question.
In Keras, there are now three types of regularizers for a layer:
I have read posts that explain the difference between L1 and L2 norm, but in an intuitive sense, I'd like to know how each regularizer will affect the aforementioned three types of regularizers and when to use what.
The motivation for my question is that my understanding is that regularizers are usually applied to the loss function. However, they're even being added to bias term. I'm not able to wrap my head around why one would think to do this, let alone be able to discern when to use L1 and L2 for the bias regularizer. Hence, I wanted to get an overall understanding of all three entities that regularizers are applied on and in general know how the 2 kinds of regularizers can affect each of those entities at a high level.