Answer - Keras models accept three types of inputs: Firstly, NumPy arrays, just like Scikit-Learn and many other Python-based libraries. This is a good option if your data fits in memory. Secondly, TensorFlow Dataset objects. This is a high-performance option that is more suitable for datasets that do not fit in memory and that are streamed from a disk or from a distributed filesystem. Lastly, Python generators that yield batches of data (such as custom subclasses of the keras.utils.Sequence class).