def _as_variant_tensor(self):
# pylint: disable=protected-access
input_resource = self._input_dataset._as_variant_tensor()
return gen_dataset_ops.shuffle_and_repeat_dataset(
input_resource,
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
def _as_variant_tensor(self):
# pylint: disable=protected-access
input_resource = self._input_dataset._as_variant_tensor()
return gen_dataset_ops.shuffle_and_repeat_dataset(
input_resource,
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
def _as_variant_tensor(self):
# pylint: disable=protected-access
input_resource = self._input_dataset._as_variant_tensor()
return gen_dataset_ops.shuffle_and_repeat_dataset(
input_resource,
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)),
output_shapes=nest.flatten(
sparse.as_dense_shapes(self.output_shapes, self.output_classes)))
def _as_variant_tensor(self):
# pylint: disable=protected-access
input_resource = self._input_dataset._as_variant_tensor()
return gen_dataset_ops.shuffle_and_repeat_dataset(
input_resource,
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)),
output_shapes=nest.flatten(
sparse.as_dense_shapes(self.output_shapes,
self.output_classes)))
def __init__(self, input_dataset, buffer_size, count=None, seed=None):
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(
count, dtype=dtypes.int64, name="count")
self._seed, self._seed2 = random_seed.get_seed(seed)
variant_tensor = gen_dataset_ops.shuffle_and_repeat_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
super(_ShuffleAndRepeatDataset, self).__init__(input_dataset,
variant_tensor)
def __init__(self, input_dataset, buffer_size, count=None, seed=None):
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(
count, dtype=dtypes.int64, name="count")
self._seed, self._seed2 = random_seed.get_seed(seed)
variant_tensor = gen_dataset_ops.shuffle_and_repeat_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
super(_ShuffleAndRepeatDataset, self).__init__(input_dataset,
variant_tensor)
