The first thing to have is a function to create minibatches of training data. In fact, at each training iteration, we'd need to insert a minibatch of samples extracted from the training set. Here, we'll build a function that takes the observations, labels, and batch size as arguments and returns a minibatch generator. Furthermore, to introduce some variability in the training data, let's add another argument to the function, the possibility to shuffle the data to have different minibatches of data for each generator. Having different minibatches of data in each generator will force the model to learn the in-out connection and not memorize the sequence:
def minibatcher(X, y, batch_size, shuffle):assert ...
