Welcome to Part 2 of a blog series that introduces TensorFlow Datasets and Estimators. We’re devoting this article to feature columns—a data structure describing the features that an Estimator requires for training and inference. As you’ll see, feature columns are very rich, enabling you to represent a diverse range of data.
In Part 1, we used the pre-made Estimator
DNNClassifier to train a model to predict different types of Iris flowers from four input features. That example created only numerical feature columns (of type
tf.feature_column.numeric_column). Although those feature columns were sufficient to model the lengths of petals and sepals, real world data sets contain all kinds of non-numerical features. For example: