They will learn to turn the old features into predictions on a new dataset.
Train the new layers on your dataset.
A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate.
This can potentially achieve meaningful improvements, by incrementally adapting the pretrained features to the new data.


Freezing layers: understanding the trainable attribute
Freezing layers: understanding the trainable attribute
Layers & models have three weight attributes:
weights is the list of all weights variables of the layer.
trainable_weights is the list of those that are meant to be updated (via gradient descent) to minimize the loss during training.
non_trainable_weights is the list of those that aren't meant to be trained