tf.contrib.data.shuffle_and_repeat
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Shuffles and repeats a Dataset returning a new permutation for each epoch. (deprecated)
tf.contrib.data.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.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.data.shuffle_and_repeat\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/data/python/ops/shuffle_ops.py#L24-L54) |\n\nShuffles and repeats a Dataset returning a new permutation for each epoch. (deprecated) \n\n tf.contrib.data.shuffle_and_repeat(\n buffer_size, count=None, seed=None\n )\n\n| **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use [`tf.data.experimental.shuffle_and_repeat(...)`](../../../tf/data/experimental/shuffle_and_repeat).\n\n`dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size, count))`\n\nis equivalent to\n\n`dataset.shuffle(buffer_size, reshuffle_each_iteration=True).repeat(count)`\n\nThe difference is that the latter dataset is not serializable. So,\nif you need to checkpoint an input pipeline with reshuffling you must use\nthis implementation.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `buffer_size` | A [`tf.int64`](../../../tf#int64) scalar [`tf.Tensor`](../../../tf/Tensor), representing the maximum number elements that will be buffered when prefetching. |\n| `count` | (Optional.) A [`tf.int64`](../../../tf#int64) scalar [`tf.Tensor`](../../../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. |\n| `seed` | (Optional.) A [`tf.int64`](../../../tf#int64) scalar [`tf.Tensor`](../../../tf/Tensor), representing the random seed that will be used to create the distribution. See [`tf.compat.v1.set_random_seed`](../../../tf/random/set_random_seed) for behavior. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Dataset` transformation function, which can be passed to [`tf.data.Dataset.apply`](../../../tf/data/Dataset#apply). ||\n\n\u003cbr /\u003e"]]
View source on GitHub