I came across the following function in Tensorflow's tutorial on Machine Translation:
BUFFER_SIZE = 32000 BATCH_SIZE = 64 data_size = 30000 train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
I went through several blogs to understand
.shuffle(BUFFER_SIZE), but what puzzles me is the fact that a BUFFER_SIZE > DATA_SIZE results in a perfectly uniform shuffling. Neither do I understand what they mean by 'uniform shuffling', nor do I understand how a BUFFER_SIZE> DATA_SIZE is even possible.
From what I understand, tensorflow keeps a BUFFER_SIZE of elements, selects a random element and adds the next input element into the buffer. This makes sense if the BUFFER_SIZE is <= DATA_SIZE. But, what happens to the buffer in case we have more number of elements than the size of the dataset? Do we not have some NULL elements? How does it result in uniform shuffling?
Could anyone please explain to me with an example of how BUFFER_SIZE > DATA_SIZE results in a uniform shuffling? And, what exactly do we mean by uniform shuffling?