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Chapter 5
And we can evaluate the trained model:
Figure 15: An evaluation of the trained model
We have successfully created an estimator. In the next section, we will discuss a new
deep learning technique called transfer learning.
Deep Inception-v3 Net used 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 to fine-tune the entire
network to the novel task. For instance, we can take a network trained to recognize
10 categories in music and fine-tune it to recognize 20 categories in movies.
Inception-v3 Net is a very deep CNN developed by Google [2]. tf.keras
implements the full network described in Figure 16 and it comes pretrained on
ImageNet. The default input size for this model is 299×299 on three channels:
Figure 16: Inception-v3 deep learning model
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