Machine Learning in Elixir

Fine-Tuning Your Model

In the previous section, you made use of a pre-trained model for feature extraction. You attached a classification head on top of a frozen pre-trained model—taking advantage of the general features extracted from the pre-trained model for your specific problem. Remember, freezing the model initially was important because the early stages of training are unstable and your model was at risk of losing all of its prior knowledge. But now that you have a trained model, you can unfreeze some of the layers of the pre-trained model and force them to learn features specific to your problem. This process is called fine-tuning.

Rather than freeze the entire pre-trained model, during fine-tuning you unfreeze the top-most layers of ...