Practical Deep Learning for Cloud, Mobile, and Edge

Chapter 2. What’s in the Picture: Image Classification with Keras

If you have skimmed through deep learning literature, you might have come across a barrage of academic explanations laced with intimidating mathematics. Don’t worry. We will ease you into practical deep learning with an example of classifying images with just a few lines of code.

In this chapter, we take a closer look at the Keras framework, discuss its place in the deep learning landscape, and then use it to classify a few images using existing state-of-the-art classifiers. We visually investigate how these classifiers operate by using heatmaps. With these heatmaps, we make a fun project in which we classify objects in videos.

Recall from the “Recipe for the Perfect Deep Learning Solution” that we need four ingredients to create our deep learning recipe: hardware, dataset, framework, and model. Let’s see how each of these comes into play in this chapter:

• We begin with the easy one: hardware. Even an inexpensive laptop would suffice for what we we’re doing in this chapter. Alternatively, you can run the code in this chapter by opening the GitHub notebook (see http://PracticalDeepLearning.ai) in Colab. This is just a matter of a few mouse clicks.

• Because we won’t be training a neural network just yet, we don’t need a dataset (other than a handful of sample photos to test with).

• Next, we come to the framework. This chapter’s title has Keras in it, so that is what we will be using for now. In fact, we use Keras ...