Hands-On Convolutional Neural Networks with TensorFlow

One-hot vectors

After shuffling, we do some preprocessing on the data labels. The labels loaded with the dataset is just a 150-length vector of integers representing which target class each datapoint belongs to, either 1, 2, or 3 in this case. When creating machine learning models, we like to transform our labels into a new form that is easier to work with by doing something called one-hot encoding.

Rather than a single number being the label for each datapoint, we use vectors instead. Each vector will be as long as the number of different target classes you have. So for example, if you have 5 target classes then each vector will have 5 elements; if you have 1,000 target classes then each vector will have 1,000 elements. Each column in the ...