These attributes can be used to do neat things, like quickly creating a model that extracts the outputs of all intermediate layers in a Sequential model:

Here's a similar example that only extract features from one layer:


Transfer learning & fine-tuning
Transfer learning & fine-tuning
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem.
For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.
The most common incarnation of transfer learning in the context of deep learning is the following worfklow:
Take layers from a previously trained model.
Freeze them, so as to avoid destroying any of the information they contain during future training rounds.
Add some new, trainable layers on top of the frozen layers