def _make_reduce_func(self, reduce_func, input_dataset):
"""Make wrapping Defun for reduce_func."""
nested_dataset = dataset_ops._NestedDatasetComponent(input_dataset) # pylint: disable=protected-access
wrapped_func = dataset_ops.StructuredFunctionWrapper(
reduce_func, "tf.contrib.data.reduce_by_window()",
input_classes=(ops.Tensor, nested_dataset),
input_shapes=(tensor_shape.scalar(), nested_dataset),
input_types=(dtypes.int64, nested_dataset),
experimental_nested_dataset_support=True)
if not isinstance(
wrapped_func.output_classes, dataset_ops._NestedDatasetComponent): # pylint: disable=protected-access
raise TypeError("`reduce_func` must return a `Dataset` object.")
self._output_classes = wrapped_func.output_classes.output_classes
self._output_types = wrapped_func.output_types.output_types
self._output_shapes = wrapped_func.output_shapes.output_shapes
self._reduce_func = wrapped_func.function
def _make_reduce_func(self, reduce_func, input_dataset):
"""Make wrapping defun for reduce_func."""
nested_dataset = dataset_ops._NestedDatasetComponent(input_dataset) # pylint: disable=protected-access
wrapped_func = dataset_ops.StructuredFunctionWrapper(
reduce_func,
self._transformation_name(),
input_classes=(ops.Tensor, nested_dataset),
input_shapes=(tensor_shape.scalar(), nested_dataset),
input_types=(dtypes.int64, nested_dataset))
if not isinstance(
wrapped_func.output_classes, dataset_ops._NestedDatasetComponent): # pylint: disable=protected-access
raise TypeError("`reduce_func` must return a `Dataset` object.")
self._output_classes = wrapped_func.output_classes.output_classes
self._output_types = wrapped_func.output_types.output_types
self._output_shapes = wrapped_func.output_shapes.output_shapes
self._reduce_func = wrapped_func.function
def _make_reduce_func(self, reduce_func, input_dataset):
"""Make wrapping defun for reduce_func."""
nested_dataset = dataset_ops._NestedDatasetComponent(input_dataset) # pylint: disable=protected-access
wrapped_func = dataset_ops.StructuredFunctionWrapper(
reduce_func,
self._transformation_name(),
input_classes=(ops.Tensor, nested_dataset),
input_shapes=(tensor_shape.scalar(), nested_dataset),
input_types=(dtypes.int64, nested_dataset))
if not isinstance(wrapped_func.output_classes,
dataset_ops._NestedDatasetComponent): # pylint: disable=protected-access
raise TypeError("`reduce_func` must return a `Dataset` object.")
self._output_classes = wrapped_func.output_classes.output_classes
self._output_types = wrapped_func.output_types.output_types
self._output_shapes = wrapped_func.output_shapes.output_shapes
self._reduce_func = wrapped_func.function
def _make_reduce_func(self, reduce_func, input_dataset):
"""Make wrapping Defun for reduce_func."""
nested_dataset = dataset_ops._NestedDatasetComponent(input_dataset) # pylint: disable=protected-access
wrapped_func = dataset_ops.StructuredFunctionWrapper(
reduce_func,
"tf.contrib.data.reduce_by_window()",
input_classes=(ops.Tensor, nested_dataset),
input_shapes=(tensor_shape.scalar(), nested_dataset),
input_types=(dtypes.int64, nested_dataset),
experimental_nested_dataset_support=True)
if not isinstance(wrapped_func.output_classes,
dataset_ops._NestedDatasetComponent): # pylint: disable=protected-access
raise TypeError("`reduce_func` must return a `Dataset` object.")
self._output_classes = wrapped_func.output_classes.output_classes
self._output_types = wrapped_func.output_types.output_types
self._output_shapes = wrapped_func.output_shapes.output_shapes
self._reduce_func = wrapped_func.function
def __init__(self, input_dataset, window_size):
"""See `window_dataset()` for more details."""
super(_WindowDataset, self).__init__()
self._input_dataset = input_dataset
self._window_size = ops.convert_to_tensor(
window_size, dtype=dtypes.int64, name="window_size")
self._output_classes = nest.pack_sequence_as(
input_dataset.output_classes,
[
dataset_ops._NestedDatasetComponent( # pylint: disable=protected-access
output_classes=output_class,
output_shapes=output_shape,
output_types=output_type)
for output_class, output_shape, output_type in zip(
nest.flatten(input_dataset.output_classes),
nest.flatten(input_dataset.output_shapes),
nest.flatten(input_dataset.output_types))
])
self._output_shapes = self._output_classes
self._output_types = self._output_classes
def __init__(self, input_dataset, window_size):
"""See `window_dataset()` for more details."""
super(_WindowDataset, self).__init__()
self._input_dataset = input_dataset
self._window_size = ops.convert_to_tensor(window_size,
dtype=dtypes.int64,
name="window_size")
self._output_classes = nest.pack_sequence_as(
input_dataset.output_classes,
[
dataset_ops._NestedDatasetComponent( # pylint: disable=protected-access
output_classes=output_class,
output_shapes=output_shape,
output_types=output_type)
for output_class, output_shape, output_type in zip(
nest.flatten(input_dataset.output_classes),
nest.flatten(input_dataset.output_shapes),
nest.flatten(input_dataset.output_types))
])
self._output_shapes = self._output_classes
self._output_types = self._output_classes