| View source on GitHub |
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. |
| Returns | |
|---|---|
A Dataset transformation function, which can be passed to tf.data.Dataset.apply. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/data/experimental/shuffle_and_repeat