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Chapter 4
img_path = 'cat.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
# get the features from this block
features = model.predict(x)
print(features)
You might wonder why we want to extract the features from an intermediate layer
in a DCNN. The reasoning is that as the network learns to classify images into
categories, each layer learns to identify the features that are necessary to perform
the final classification. Lower layers identify lower-order features such as color
and edges, and higher layers compose these lower-order features into higher-order
features such as shapes or objects. Hence, the intermediate layer has the capability
to extract important features from an image, and these features are more likely to
help in different kinds of classification.
This has multiple advantages. First, we can rely on publicly available large-scale
training and transfer this learning to novel domains. Second, we can save time for
expensive large training. Third, we can provide reasonable solutions even when we
don't have a large number of training examples for our domain. We also get a good
starting network shape for the task at hand, instead of guessing it.
With this, we will conclude the overview of VGG-16 CNNs, the last deep learning
model defined in this chapter. You will see more examples of CNNs in the next
chapter.
Summary
In this chapter we have learned how to use deep learning convnets for recognizing
MNIST handwritten characters with high accuracy. We used the CIFAR-10 dataset
for building a deep learning classifier with 10 categories, and the ImageNet dataset
to build an accurate classifier with 1,000 categories. In addition, we investigated how
to use large deep learning networks such as VGG16 and very deep networks such
as InceptionV3. We concluded with a discussion on transfer learning; in the next
chapter we'll see how to adapt prebuilt models trained on large datasets so that they
can work well on a new domain.
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