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)