In this section, we're going to look at pulling in training and testing data. We'll be looking at loading the actual data, then we'll revisit normalization and one-hot encoding, and then we'll have a quick discussion about why we actually use training and testing datasets.
In this section, we'll be taking what we learned in the previous chapter about preparing image data and condensing it into just a few lines of code, as shown in the following screenshot:
We load the training and testing data along with the training and testing outputs. Then, we normalize, which just means dividing by the maximum value, which we know is going to be 255. Then, we break down the output variables into categorical, or one-hot, encodings. We do these two things (normalization and one-hot encoding) in the exact same fashion for both our...