tf.data.experimental.shuffle_and_repeat

Shuffles and repeats a Dataset returning a new permutation for each epoch. (deprecated)

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.data.experimental.shuffle_and_repeat, `tf.compat.v2.data.experimental.shuffle_and_repeat`

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.

dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size, count))

is equivalent to

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

The difference is that the latter dataset is not serializable. So, if you need to checkpoint an input pipeline with reshuffling you must use this implementation.

Args
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.

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