A Gentle Introduction to tensorflow.data API

Before we see how the tf.data API works, let's review how we usually train a Keras model. First, we need a dataset. An example is the fashion MNIST dataset that comes with the Keras API, which we have 60,000 training samples and 10,000 test samples of 28 28 pixels in grayscale and the corresponding classification label is encoded with integers 0 to 9. The dataset is a NumPy array. Then we can build a Keras model for classification, and with the model's fit() function, we provide the NumPy array as data.