![]() a machine with Keras, SciPy, PIL installed.Our setup: only 2000 training examples (1000 per class) ImageDataGenerator for real-time data augmentation.fit_generator for training Keras a model using Python data generators.This will lead us to cover the following Keras features: fine-tuning the top layers of a pre-trained network.using the bottleneck features of a pre-trained network.training a small network from scratch (as a baseline). ![]() In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples -just a few hundred or thousand pictures from each class you want to be able to recognize. Please seeįor an up-to-date alternative, or check out chapter 8 of my book "Deep Learning with Python (2nd edition)". ![]() Note: this post was originally written in June 2016. ![]()
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