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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.
# ==============================================================================
"""Datasets for random number generators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import random_seed
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.util.tf_export import tf_export
@tf_export("data.experimental.RandomDataset")
class RandomDataset(dataset_ops.DatasetSource):
"""A `Dataset` of pseudorandom values."""
def __init__(self, seed=None):
"""A `Dataset` of pseudorandom values."""
super(RandomDataset, self).__init__()
# NOTE(mrry): We generate the seed-pair once per graph in which the dataset
# is iterated over, and cache it in `self._graph_seed_map`. This supports
# two features: iterating over the same `ShuffleDataset` twice in the same
# pipeline and observing the same order (by tying the seeds together with
# a randomly-generated seed), and using `Dataset.make_one_shot_iterator()`,
# which requires the stateful RNG op to be created inside the same graph as
# the dataset.
self._original_seed = seed
self._graph_seed_map = {}
def _as_variant_tensor(self):
try:
seed, seed2 = self._graph_seed_map[ops.get_default_graph()]
except KeyError:
seed, seed2 = random_seed.get_seed(self._original_seed)
self._graph_seed_map[ops.get_default_graph()] = (seed, seed2)
return gen_dataset_ops.random_dataset(
seed=seed, seed2=seed2, **dataset_ops.flat_structure(self))
@property
def output_classes(self):
return ops.Tensor
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.int64
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