tf.data.experimental.shuffle_and_repeat
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Shuffles and repeats a Dataset returning a new permutation for each epoch. (deprecated)
tf.data.experimental.shuffle_and_repeat(
buffer_size, count=None, seed=None
)
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.
|
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Last updated 2020-10-01 UTC.
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