Python Tutorial: Classifying images

Want to learn more? Take the full course at https://learn.datacamp.com/courses/image-processing-with-keras-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Next, let's consider what to do in order to classify images. We have images of three different classes: dresses, t-shirts and shoes. We'd like to build an algorithm that can distinguish between these classes. In machine learning, this is called a classification task. In the training phase, we present the algorithm with samples from these three classes, together with the class labels for each image. Over the course of training, the algorithm adjusts its parameters to learn the patterns in the data that distinguish between the three different classes of clothing. At the end of training, we would like to know how well our classifier does. To avoid an estimate that is overly optimistic because of overfitting, we evaluate it by testing it on a portion of the data that has been set aside in advance for this purpose. In this case, the classifier is able to correctly classify some of the images, but incorrectly classifies an image of a dress as a t-shirt, and an image of a t-shirt as a shoe. How do we represent data for classification? Consider the following series of labels. One mathematically convenient way of representing this data is called one-hot encoding. In this one-hot encoding array, each row represents one sample, and each column corresponds to one of the classes. In each row, all of the values are set to 0, except in the column corresponding to the class from which this image is taken. For example, here is a one-hot encoding of three images, a t-shirt, a dress, and a shoe. To generate a one-hot encoding of these samples we generate an array of categories. We initialize an array of zeros, and then we iterate over the list of labels. For each sample, we find the index into the categories array that corresponds to the current sampl