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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Experimental shuffle ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.experimental.ops import shuffle_ops
from tensorflow.python.util import deprecation
@deprecation.deprecated(None,
"Use `tf.data.experimental.shuffle_and_repeat(...)`.")
def shuffle_and_repeat(buffer_size, count=None, seed=None):
"""Shuffles and repeats a Dataset returning a new permutation for each epoch.
`dataset.apply(tf.contrib.data.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.set_random_seed` for behavior.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
return shuffle_ops.shuffle_and_repeat(buffer_size, count, seed)
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