Deep Inception V3 for transfer learning

Transfer learning is a very powerful deep learning technique that has applications in a number of different domains. The idea behind transfer learning is very simple and can be explained with an analogy. Suppose you want to learn a new language, say Spanish. Then it could be useful to start from what you already know in a different language, say English.

Following this line of thinking, computer vision researchers now commonly use pretrained CNNs to generate representations for novel tasks [1], where the dataset may not be large enough to train an entire CNN from scratch. Another common tactic is to take the pretrained ImageNet network and then fine-tune the entire network to the novel task. For instance, we can take a network trained to recognize 10 categories of music and fine-tune it to recognize 20 categories of movies.

Inception V3 is a very deep ConvNet developed by Google [2]. tf.Keras implements the full network, as described...