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View source on GitHub
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Shuffles and repeats a Dataset, reshuffling with each repetition. (deprecated)
tf.data.experimental.shuffle_and_repeat(
buffer_size, count=None, seed=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use tf.data.Dataset.shuffle(buffer_size, seed) followed by tf.data.Dataset.repeat(count). Static tf.data optimizations will take care of using the fused implementation.
>>> 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.compat.v1.set_random_seed for behavior.A Dataset transformation function, which can be passed to
tf.data.Dataset.apply.