Author: fchollet Date created: 2020/04/15 Last modified: 2020/05/12 Description: Complete guide to transfer learning & fine-tuning in Keras. 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. 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.
Aug-21-2020, 22:35:20 GMT