Building input pipelines using tf.data – the TensorFlow Dataset API
When we are training a deep NN model, we usually train the model incrementally using an iterative optimization algorithm such as stochastic gradient descent, as we have seen in previous chapters.
As mentioned at the beginning of this chapter, the Keras API is a wrapper around TensorFlow for building NN models. The Keras API provides a method, .fit(), for training the models. In cases where the training dataset is rather small and can be loaded as a tensor into the memory, TensorFlow models (that are built with the Keras API) can directly use this tensor via their .fit() method for training. In typical use cases, however, when the dataset is too large to fit into the computer memory, we will need to load the data from the main storage device (for example, the hard drive or solid-state drive) in chunks, that is, batch by batch (note the use of the term "batch" instead of "mini-batch" in...
