Fine Tuning the Neural Nets
It is a good practice to freeze the early layers when you finetune the model. There are two reasons why you want to do this :


Your new layers is initialized randomly and will always start with very big loss. Big loss means big gradient and if the weights are not frozen this will be propagated . Your model will become less stable and might fail to converge. To put it simply you don't want to put the blame of false prediction on the earlier layers.
You are saving a lot of computation resources. Training a neural network, is not a small issue, and training only a small section of the model will give you decent enough accuracy without expending too much computational resource.


Unfreezing the earlier layer is up to you on the later stage of training (Some people do some people don't) but if you feel you might need to do this to push for better performance, then just try it.