def _as_variant_tensor(self):
return gen_dataset_ops.dense_to_sparse_batch_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._batch_size,
row_shape=dataset_ops._partial_shape_to_tensor(self._row_shape), # pylint: disable=protected-access
output_shapes=nest.flatten(
sparse.as_dense_shapes(self.output_shapes, self.output_classes)),
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)))
def _as_variant_tensor(self):
return gen_dataset_ops.dense_to_sparse_batch_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._batch_size,
row_shape=dataset_ops._partial_shape_to_tensor(self._row_shape), # pylint: disable=protected-access
output_shapes=nest.flatten(
sparse.as_dense_shapes(self.output_shapes, self.output_classes)),
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)))
def __init__(self, input_dataset, batch_size, row_shape):
"""See `Dataset.dense_to_sparse_batch()` for more details."""
super(DenseToSparseBatchDataset, self).__init__()
if not isinstance(input_dataset.output_types, dtypes.DType):
raise TypeError("DenseToSparseDataset requires an input whose elements "
"have a single component, whereas the input has %r." %
input_dataset.output_types)
self._input_dataset = input_dataset
self._batch_size = batch_size
# pylint: disable=protected-access
self._row_shape = dataset_ops._partial_shape_to_tensor(row_shape)
def __init__(self, input_dataset, batch_size, row_shape):
"""See `Dataset.dense_to_sparse_batch()` for more details."""
super(DenseToSparseBatchDataset, self).__init__()
if not isinstance(input_dataset.output_types, dtypes.DType):
raise TypeError("DenseToSparseDataset requires an input whose elements "
"have a single component, whereas the input has %r." %
input_dataset.output_types)
self._input_dataset = input_dataset
self._batch_size = batch_size
# pylint: disable=protected-access
self._row_shape = dataset_ops._partial_shape_to_tensor(row_shape)
