tf.data.experimental.shuffle_and_repeat

Shuffles and repeats a Dataset, reshuffling with each repetition. (deprecated)

tf.data.experimental.shuffle_and_repeat(
    buffer_size, count=None, seed=None
)

d = tf.data.Dataset.from_tensor_slices([1, 2, 3])
d = d.apply(tf.data.experimental.shuffle_and_repeat(2, count=2))
[elem.numpy() for elem in d] # doctest: +SKIP
[2, 3, 1, 1, 3, 2]
 
dataset.apply(
  tf.data.experimental.shuffle_and_repeat(buffer_size, count, seed))

produces the same output as

dataset.shuffle(
  buffer_size, seed=seed, reshuffle_each_iteration=True).repeat(count)

In each repetition, this dataset fills a buffer with buffer_size elements, then randomly samples elements from this buffer, replacing the selected elements with new elements. For perfect shuffling, set the buffer size equal to the full size of the dataset.

For instance, if your dataset contains 10,000 elements but buffer_size is set to 1,000, then shuffle will initially select a random element from only the first 1,000 elements in the buffer. Once an element is selected, its space in the buffer is replaced by the next (i.e. 1,001-st) element, maintaining the 1,000 element buffer.

buffer_size A tf.int64 scalar tf.Tensor, representing the maximum number elements that will be buffered when prefetching.
count (Optional.) A tf.int64 scalar tf.Tensor, representing the number of times the dataset should be repeated. The default behavior (if count is None or -1) is for the dataset be repeated indefinitely.
seed (Optional.) A tf.int64 scalar tf.Tensor, representing the random seed that will be used to create the distribution. See tf.random.set_seed for behavior.

A Dataset transformation function, which can be passed to tf.data.Dataset.apply.