def testFlattenAndPack(self):
structure = ((3, 4), 5, (6, 7, (9, 10), 8))
flat = ["a", "b", "c", "d", "e", "f", "g", "h"]
self.assertEqual(nest.flatten(structure), [3, 4, 5, 6, 7, 9, 10, 8])
self.assertEqual(
nest.pack_sequence_as(structure, flat), (("a", "b"), "c",
("d", "e", ("f", "g"), "h")))
point = collections.namedtuple("Point", ["x", "y"])
structure = (point(x=4, y=2), ((point(x=1, y=0),),))
flat = [4, 2, 1, 0]
self.assertEqual(nest.flatten(structure), flat)
restructured_from_flat = nest.pack_sequence_as(structure, flat)
self.assertEqual(restructured_from_flat, structure)
self.assertEqual(restructured_from_flat[0].x, 4)
self.assertEqual(restructured_from_flat[0].y, 2)
self.assertEqual(restructured_from_flat[1][0][0].x, 1)
self.assertEqual(restructured_from_flat[1][0][0].y, 0)
self.assertEqual([5], nest.flatten(5))
self.assertEqual([np.array([5])], nest.flatten(np.array([5])))
self.assertEqual("a", nest.pack_sequence_as(5, ["a"]))
self.assertEqual(
np.array([5]), nest.pack_sequence_as("scalar", [np.array([5])]))
with self.assertRaisesRegexp(ValueError, "Structure is a scalar"):
nest.pack_sequence_as("scalar", [4, 5])
with self.assertRaisesRegexp(TypeError, "flat_sequence"):
nest.pack_sequence_as([4, 5], "bad_sequence")
with self.assertRaises(ValueError):
nest.pack_sequence_as([5, 6, [7, 8]], ["a", "b", "c"])
def testFlattenAndPack(self):
structure = ((3, 4), 5, (6, 7, (9, 10), 8))
flat = ["a", "b", "c", "d", "e", "f", "g", "h"]
self.assertEqual(nest.flatten(structure), [3, 4, 5, 6, 7, 9, 10, 8])
self.assertEqual(nest.pack_sequence_as(structure, flat),
(("a", "b"), "c", ("d", "e", ("f", "g"), "h")))
point = collections.namedtuple("Point", ["x", "y"])
structure = (point(x=4, y=2), ((point(x=1, y=0), ), ))
flat = [4, 2, 1, 0]
self.assertEqual(nest.flatten(structure), flat)
restructured_from_flat = nest.pack_sequence_as(structure, flat)
self.assertEqual(restructured_from_flat, structure)
self.assertEqual(restructured_from_flat[0].x, 4)
self.assertEqual(restructured_from_flat[0].y, 2)
self.assertEqual(restructured_from_flat[1][0][0].x, 1)
self.assertEqual(restructured_from_flat[1][0][0].y, 0)
self.assertEqual([5], nest.flatten(5))
self.assertEqual([np.array([5])], nest.flatten(np.array([5])))
self.assertEqual("a", nest.pack_sequence_as(5, ["a"]))
self.assertEqual(np.array([5]),
nest.pack_sequence_as("scalar", [np.array([5])]))
with self.assertRaisesRegexp(ValueError, "Structure is a scalar"):
nest.pack_sequence_as("scalar", [4, 5])
with self.assertRaisesRegexp(TypeError, "flat_sequence"):
nest.pack_sequence_as([4, 5], "bad_sequence")
with self.assertRaises(ValueError):
nest.pack_sequence_as([5, 6, [7, 8]], ["a", "b", "c"])
def __init__(self, dataset):
"""Creates a new iterator over the given dataset.
For example:
```python
dataset = tf.contrib.data.Dataset.range(4)
for x in Iterator(dataset):
print(x)
```
Args:
dataset: A `tf.contrib.data.Dataset` object.
Raises:
RuntimeError: When invoked without eager execution enabled.
"""
if not context.in_eager_mode():
raise RuntimeError(
"{} objects only make sense when eager execution is enabled".format(
type(self)))
ds_variant = dataset.make_dataset_resource()
self._output_types = dataset.output_types
self._flat_output_types = nest.flatten(dataset.output_types)
self._flat_output_shapes = nest.flatten(dataset.output_shapes)
self._resource = gen_dataset_ops.iterator(
container="",
shared_name=_iterator_shared_name(),
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
gen_dataset_ops.make_iterator(ds_variant, self._resource)
def __init__(self, dataset):
"""Creates a new iterator over the given dataset.
For example:
```python
dataset = tf.contrib.data.Dataset.range(4)
for x in Iterator(dataset):
print(x)
```
Args:
dataset: A `tf.contrib.data.Dataset` object.
Raises:
RuntimeError: When invoked without eager execution enabled.
"""
if not context.in_eager_mode():
raise RuntimeError(
"{} objects only make sense when eager execution is enabled".
format(type(self)))
ds_variant = dataset.make_dataset_resource()
self._output_types = dataset.output_types
self._flat_output_types = nest.flatten(dataset.output_types)
self._flat_output_shapes = nest.flatten(dataset.output_shapes)
self._resource = gen_dataset_ops.iterator(
container="",
shared_name=_iterator_shared_name(),
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes)
gen_dataset_ops.make_iterator(ds_variant, self._resource)
def testFlattenDictOrder(self):
"""`flatten` orders dicts by key, including OrderedDicts."""
ordered = collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)])
plain = {"d": 3, "b": 1, "a": 0, "c": 2}
ordered_flat = nest.flatten(ordered)
plain_flat = nest.flatten(plain)
self.assertEqual([0, 1, 2, 3], ordered_flat)
self.assertEqual([0, 1, 2, 3], plain_flat)
def make_dataset_resource(self):
return gen_dataset_ops.sloppy_interleave_dataset(
self._input_dataset.make_dataset_resource(),
self._map_func.captured_inputs,
self._cycle_length,
self._block_length,
f=self._map_func,
output_types=nest.flatten(self.output_types),
output_shapes=nest.flatten(self.output_shapes))
def __init__(self, input_dataset, map_func, cycle_length, block_length):
"""See `tf.contrib.data.sloppy_interleave()` for details."""
super(SloppyInterleaveDataset, self).__init__()
self._input_dataset = input_dataset
@function.Defun(*nest.flatten(input_dataset.output_types))
def tf_map_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
dataset = map_func(*nested_args)
else:
dataset = map_func(nested_args)
if not isinstance(dataset, dataset_ops.Dataset):
raise TypeError("`map_func` must return a `Dataset` object.")
self._output_types = dataset.output_types
self._output_shapes = dataset.output_shapes
return dataset.make_dataset_resource()
self._map_func = tf_map_func
self._map_func.add_to_graph(ops.get_default_graph())
self._cycle_length = ops.convert_to_tensor(
cycle_length, dtype=dtypes.int64, name="cycle_length")
self._block_length = ops.convert_to_tensor(
block_length, dtype=dtypes.int64, name="block_length")
def testConcatenateDatasetDifferentShape(self):
input_components = (
np.tile(np.array([[1], [2], [3], [4]]), 20),
np.tile(np.array([[12], [13], [14], [15]]), 4))
to_concatenate_components = (
np.tile(np.array([[1], [2], [3], [4], [5]]), 20),
np.tile(np.array([[12], [13], [14], [15], [16]]), 15))
input_dataset = dataset_ops.Dataset.from_tensor_slices(input_components)
dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices(
to_concatenate_components)
concatenated = input_dataset.concatenate(dataset_to_concatenate)
self.assertEqual(
[ts.as_list()
for ts in nest.flatten(concatenated.output_shapes)], [[20], [None]])
iterator = concatenated.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.test_session() as sess:
sess.run(init_op)
for i in range(9):
result = sess.run(get_next)
if i < 4:
for component, result_component in zip(input_components, result):
self.assertAllEqual(component[i], result_component)
else:
for component, result_component in zip(to_concatenate_components,
result):
self.assertAllEqual(component[i - 4], result_component)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testConcatenateDatasetDifferentShape(self):
input_components = (np.tile(np.array([[1], [2], [3], [4]]), 20),
np.tile(np.array([[12], [13], [14], [15]]), 4))
to_concatenate_components = (np.tile(
np.array([[1], [2], [3], [4], [5]]),
20), np.tile(np.array([[12], [13], [14], [15], [16]]), 15))
input_dataset = dataset_ops.Dataset.from_tensor_slices(
input_components)
dataset_to_concatenate = dataset_ops.Dataset.from_tensor_slices(
to_concatenate_components)
concatenated = input_dataset.concatenate(dataset_to_concatenate)
self.assertEqual(
[ts.as_list() for ts in nest.flatten(concatenated.output_shapes)],
[[20], [None]])
iterator = concatenated.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.test_session() as sess:
sess.run(init_op)
for i in range(9):
result = sess.run(get_next)
if i < 4:
for component, result_component in zip(
input_components, result):
self.assertAllEqual(component[i], result_component)
else:
for component, result_component in zip(
to_concatenate_components, result):
self.assertAllEqual(component[i - 4], result_component)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def testRestructureDataset(self):
components = (array_ops.placeholder(dtypes.int32),
(array_ops.placeholder(dtypes.int32, shape=[None]),
array_ops.placeholder(dtypes.int32, shape=[20, 30])))
dataset = dataset_ops.Dataset.from_tensors(components)
i32 = dtypes.int32
test_cases = [((i32, i32, i32), None), (((i32, i32), i32), None),
((i32, i32, i32), (None, None, None)),
((i32, i32, i32), ([17], [17], [20, 30]))]
for new_types, new_shape_lists in test_cases:
# pylint: disable=protected-access
new = dataset_ops._RestructuredDataset(dataset, new_types,
new_shape_lists)
# pylint: enable=protected-access
self.assertEqual(new_types, new.output_types)
if new_shape_lists is not None:
for expected_shape_list, shape in zip(
nest.flatten(new_shape_lists),
nest.flatten(new.output_shapes)):
if expected_shape_list is None:
self.assertIs(None, shape.ndims)
else:
self.assertEqual(expected_shape_list, shape.as_list())
fail_cases = [((i32, dtypes.int64, i32), None),
((i32, i32, i32, i32), None),
((i32, i32, i32), ((None, None), None)),
((i32, i32, i32), (None, None, None, None)),
((i32, i32, i32), (None, [None], [21, 30]))]
for new_types, new_shape_lists in fail_cases:
with self.assertRaises(ValueError):
# pylint: disable=protected-access
new = dataset_ops._RestructuredDataset(dataset, new_types,
new_shape_lists)
def testRestructureDataset(self):
components = (array_ops.placeholder(dtypes.int32),
(array_ops.placeholder(dtypes.int32, shape=[None]),
array_ops.placeholder(dtypes.int32, shape=[20, 30])))
dataset = dataset_ops.Dataset.from_tensors(components)
i32 = dtypes.int32
test_cases = [((i32, i32, i32), None),
(((i32, i32), i32), None),
((i32, i32, i32), (None, None, None)),
((i32, i32, i32), ([17], [17], [20, 30]))]
for new_types, new_shape_lists in test_cases:
# pylint: disable=protected-access
new = dataset_ops._RestructuredDataset(
dataset, new_types, new_shape_lists)
# pylint: enable=protected-access
self.assertEqual(new_types, new.output_types)
if new_shape_lists is not None:
for expected_shape_list, shape in zip(
nest.flatten(new_shape_lists), nest.flatten(new.output_shapes)):
if expected_shape_list is None:
self.assertIs(None, shape.ndims)
else:
self.assertEqual(expected_shape_list, shape.as_list())
fail_cases = [((i32, dtypes.int64, i32), None),
((i32, i32, i32, i32), None),
((i32, i32, i32), ((None, None), None)),
((i32, i32, i32), (None, None, None, None)),
((i32, i32, i32), (None, [None], [21, 30]))]
for new_types, new_shape_lists in fail_cases:
with self.assertRaises(ValueError):
# pylint: disable=protected-access
new = dataset_ops._RestructuredDataset(
dataset, new_types, new_shape_lists)
def testFlattenAndPack_withDicts(self):
# A nice messy mix of tuples, lists, dicts, and `OrderedDict`s.
named_tuple = collections.namedtuple("A", ("b", "c"))
mess = (
"z",
named_tuple(3, 4),
{
"c": (
1,
collections.OrderedDict([
("b", 3),
("a", 2),
]),
),
"b": 5
},
17
)
flattened = nest.flatten(mess)
self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 17])
structure_of_mess = (
14,
named_tuple("a", True),
{
"c": (
0,
collections.OrderedDict([
("b", 9),
("a", 8),
]),
),
"b": 3
},
"hi everybody",
)
unflattened = nest.pack_sequence_as(structure_of_mess, flattened)
self.assertEqual(unflattened, mess)
# Check also that the OrderedDict was created, with the correct key order.
unflattened_ordered_dict = unflattened[2]["c"][1]
self.assertIsInstance(unflattened_ordered_dict, collections.OrderedDict)
self.assertEqual(list(unflattened_ordered_dict.keys()), ["b", "a"])
def testMapStructure(self):
structure1 = (((1, 2), 3), 4, (5, 6))
structure2 = (((7, 8), 9), 10, (11, 12))
structure1_plus1 = nest.map_structure(lambda x: x + 1, structure1)
nest.assert_same_structure(structure1, structure1_plus1)
self.assertAllEqual([2, 3, 4, 5, 6, 7], nest.flatten(structure1_plus1))
structure1_plus_structure2 = nest.map_structure(
lambda x, y: x + y, structure1, structure2)
self.assertEqual((((1 + 7, 2 + 8), 3 + 9), 4 + 10, (5 + 11, 6 + 12)),
structure1_plus_structure2)
self.assertEqual(3, nest.map_structure(lambda x: x - 1, 4))
self.assertEqual(7, nest.map_structure(lambda x, y: x + y, 3, 4))
with self.assertRaisesRegexp(TypeError, "callable"):
nest.map_structure("bad", structure1_plus1)
with self.assertRaisesRegexp(ValueError, "same nested structure"):
nest.map_structure(lambda x, y: None, 3, (3, ))
with self.assertRaisesRegexp(TypeError, "same sequence type"):
nest.map_structure(lambda x, y: None, ((3, 4), 5), {
"a": (3, 4),
"b": 5
})
with self.assertRaisesRegexp(ValueError, "same nested structure"):
nest.map_structure(lambda x, y: None, ((3, 4), 5), (3, (4, 5)))
with self.assertRaisesRegexp(ValueError, "same nested structure"):
nest.map_structure(lambda x, y: None, ((3, 4), 5), (3, (4, 5)),
check_types=False)
with self.assertRaisesRegexp(ValueError,
"Only valid keyword argument"):
nest.map_structure(lambda x: None, structure1, foo="a")
with self.assertRaisesRegexp(ValueError,
"Only valid keyword argument"):
nest.map_structure(lambda x: None,
structure1,
check_types=False,
foo="a")
def tf_map_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the input_dataset.
for arg, shape in zip(args, nest.flatten(input_dataset.output_shapes)):
arg.set_shape(shape)
nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
if nest.is_sequence(nested_args):
dataset = map_func(*nested_args)
else:
dataset = map_func(nested_args)
if not isinstance(dataset, dataset_ops.Dataset):
raise TypeError("`map_func` must return a `Dataset` object.")
self._output_types = dataset.output_types
self._output_shapes = dataset.output_shapes
return dataset.make_dataset_resource()
def testMapStructure(self):
structure1 = (((1, 2), 3), 4, (5, 6))
structure2 = (((7, 8), 9), 10, (11, 12))
structure1_plus1 = nest.map_structure(lambda x: x + 1, structure1)
nest.assert_same_structure(structure1, structure1_plus1)
self.assertAllEqual(
[2, 3, 4, 5, 6, 7],
nest.flatten(structure1_plus1))
structure1_plus_structure2 = nest.map_structure(
lambda x, y: x + y, structure1, structure2)
self.assertEqual(
(((1 + 7, 2 + 8), 3 + 9), 4 + 10, (5 + 11, 6 + 12)),
structure1_plus_structure2)
self.assertEqual(3, nest.map_structure(lambda x: x - 1, 4))
self.assertEqual(7, nest.map_structure(lambda x, y: x + y, 3, 4))
with self.assertRaisesRegexp(TypeError, "callable"):
nest.map_structure("bad", structure1_plus1)
with self.assertRaisesRegexp(ValueError, "same nested structure"):
nest.map_structure(lambda x, y: None, 3, (3,))
with self.assertRaisesRegexp(TypeError, "same sequence type"):
nest.map_structure(lambda x, y: None, ((3, 4), 5), {"a": (3, 4), "b": 5})
with self.assertRaisesRegexp(ValueError, "same nested structure"):
nest.map_structure(lambda x, y: None, ((3, 4), 5), (3, (4, 5)))
with self.assertRaisesRegexp(ValueError, "same nested structure"):
nest.map_structure(lambda x, y: None, ((3, 4), 5), (3, (4, 5)),
check_types=False)
with self.assertRaisesRegexp(ValueError, "Only valid keyword argument"):
nest.map_structure(lambda x: None, structure1, foo="a")
with self.assertRaisesRegexp(ValueError, "Only valid keyword argument"):
nest.map_structure(lambda x: None, structure1, check_types=False, foo="a")
def __init__(self,
cell,
attention_mechanism,
is_manual_attention,
manual_alignments,
attention_layer_size=None,
alignment_history=False,
cell_input_fn=None,
output_attention=True,
initial_cell_state=None,
name=None):
"""Construct the `AttentionWrapper`.
Args:
cell: An instance of `RNNCell`.
attention_mechanism: A list of `AttentionMechanism` instances or a single
instance.
attention_layer_size: A list of Python integers or a single Python
integer, the depth of the attention (output) layer(s). If None
(default), use the context as attention at each time step. Otherwise,
feed the context and cell output into the attention layer to generate
attention at each time step. If attention_mechanism is a list,
attention_layer_size must be a list of the same length.
alignment_history: Python boolean, whether to store alignment history
from all time steps in the final output state (currently stored as a
time major `TensorArray` on which you must call `stack()`).
cell_input_fn: (optional) A `callable`. The default is:
`lambda inputs, attention: array_tf.concat([inputs, attention], -1)`.
output_attention: Python bool. If `True` (default), the output at each
time step is the attention value. This is the behavior of Luong-style
attention mechanisms. If `False`, the output at each time step is
the output of `cell`. This is the beahvior of Bhadanau-style
attention mechanisms. In both cases, the `attention` tensor is
propagated to the next time step via the state and is used there.
This flag only controls whether the attention mechanism is propagated
up to the next cell in an RNN stack or to the top RNN output.
initial_cell_state: The initial state value to use for the cell when
the user calls `zero_state()`. Note that if this value is provided
now, and the user uses a `batch_size` argument of `zero_state` which
does not match the batch size of `initial_cell_state`, proper
behavior is not guaranteed.
name: Name to use when creating tf.
Raises:
TypeError: `attention_layer_size` is not None and (`attention_mechanism`
is a list but `attention_layer_size` is not; or vice versa).
ValueError: if `attention_layer_size` is not None, `attention_mechanism`
is a list, and its length does not match that of `attention_layer_size`.
"""
super(AttentionWrapper, self).__init__(name=name)
self.is_manual_attention = is_manual_attention
self.manual_alignments = manual_alignments
if isinstance(attention_mechanism, (list, tuple)):
self._is_multi = True
attention_mechanisms = attention_mechanism
for attention_mechanism in attention_mechanisms:
if not isinstance(attention_mechanism, AttentionMechanism):
raise TypeError(
"attention_mechanism must contain only instances of "
"AttentionMechanism, saw type: %s" %
type(attention_mechanism).__name__)
else:
self._is_multi = False
if not isinstance(attention_mechanism, AttentionMechanism):
raise TypeError(
"attention_mechanism must be an AttentionMechanism or list of "
"multiple AttentionMechanism instances, saw type: %s" %
type(attention_mechanism).__name__)
attention_mechanisms = (attention_mechanism, )
if cell_input_fn is None:
cell_input_fn = (
lambda inputs, attention: tf.concat([inputs, attention], -1))
else:
if not callable(cell_input_fn):
raise TypeError(
"cell_input_fn must be callable, saw type: %s" %
type(cell_input_fn).__name__)
if attention_layer_size is not None:
attention_layer_sizes = tuple(attention_layer_size if isinstance(
attention_layer_size, (list,
tuple)) else (attention_layer_size, ))
if len(attention_layer_sizes) != len(attention_mechanisms):
raise ValueError(
"If provided, attention_layer_size must contain exactly one "
"integer per attention_mechanism, saw: %d vs %d" %
(len(attention_layer_sizes), len(attention_mechanisms)))
self._attention_layers = tuple(
layers_core.Dense(attention_layer_size,
name="attention_layer",
use_bias=False)
for attention_layer_size in attention_layer_sizes)
self._attention_layer_size = sum(attention_layer_sizes)
else:
self._attention_layers = None
self._attention_layer_size = sum(
attention_mechanism.values.get_shape()[-1].value
for attention_mechanism in attention_mechanisms)
self._cell = cell
self._attention_mechanisms = attention_mechanisms
self._cell_input_fn = cell_input_fn
self._output_attention = output_attention
self._alignment_history = alignment_history
with tf.name_scope(name, "AttentionWrapperInit"):
if initial_cell_state is None:
self._initial_cell_state = None
else:
final_state_tensor = nest.flatten(initial_cell_state)[-1]
state_batch_size = (final_state_tensor.shape[0].value
or tf.shape(final_state_tensor)[0])
error_message = (
"When constructing AttentionWrapper %s: " % self._base_name
+ "Non-matching batch sizes between the memory "
"(encoder output) and initial_cell_state. Are you using "
"the BeamSearchDecoder? You may need to tile your initial state "
"via the tf.contrib.seq2seq.tile_batch function with argument "
"multiple=beam_width.")
with tf.control_dependencies(
self._batch_size_checks(state_batch_size,
error_message)):
self._initial_cell_state = nest.map_structure(
lambda s: tf.identity(s,
name="check_initial_cell_state"),
initial_cell_state)
def testFlattenUpTo(self):
input_tree = (((2, 2), (3, 3)), ((4, 9), (5, 5)))
shallow_tree = ((True, True), (False, True))
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [(2, 2), (3, 3), (4, 9),
(5, 5)])
self.assertEqual(flattened_shallow_tree, [True, True, False, True])
input_tree = ((("a", 1), (("b", 2), (("c", 3), (("d", 4))))))
shallow_tree = (("level_1", ("level_2", ("level_3", ("level_4")))))
input_tree_flattened_as_shallow_tree = nest.flatten_up_to(
shallow_tree, input_tree)
input_tree_flattened = nest.flatten(input_tree)
self.assertEqual(input_tree_flattened_as_shallow_tree, [("a", 1),
("b", 2),
("c", 3),
("d", 4)])
self.assertEqual(input_tree_flattened,
["a", 1, "b", 2, "c", 3, "d", 4])
## Shallow non-list edge-case.
# Using iterable elements.
input_tree = ["input_tree"]
shallow_tree = "shallow_tree"
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
input_tree = ("input_tree_0", "input_tree_1")
shallow_tree = "shallow_tree"
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
# Using non-iterable elements.
input_tree = (0, )
shallow_tree = 9
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
input_tree = (0, 1)
shallow_tree = 9
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
## Both non-list edge-case.
# Using iterable elements.
input_tree = "input_tree"
shallow_tree = "shallow_tree"
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
# Using non-iterable elements.
input_tree = 0
shallow_tree = 0
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
## Input non-list edge-case.
# Using iterable elements.
input_tree = "input_tree"
shallow_tree = ("shallow_tree", )
expected_message = (
"If shallow structure is a sequence, input must also "
"be a sequence. Input has type: <(type|class) 'str'>.")
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
input_tree = "input_tree"
shallow_tree = ("shallow_tree_9", "shallow_tree_8")
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
# Using non-iterable elements.
input_tree = 0
shallow_tree = (9, )
expected_message = (
"If shallow structure is a sequence, input must also "
"be a sequence. Input has type: <(type|class) 'int'>.")
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
input_tree = 0
shallow_tree = (9, 8)
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
# Using dict.
input_tree = {"a": ((2, 2), (3, 3)), "b": ((4, 9), (5, 5))}
shallow_tree = {"a": (True, True), "b": (False, True)}
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [(2, 2), (3, 3), (4, 9),
(5, 5)])
self.assertEqual(flattened_shallow_tree, [True, True, False, True])
self._attention_layers = None
self._attention_layer_size = sum(
attention_mechanism.values.get_shape()[-1].value
for attention_mechanism in attention_mechanisms)
self._cell = cell
self._attention_mechanisms = attention_mechanisms
self._cell_input_fn = cell_input_fn
self._output_attention = output_attention
self._alignment_history = alignment_history
with tf.name_scope(name, "AttentionWrapperInit"):
if initial_cell_state is None:
self._initial_cell_state = None
else:
final_state_tensor = nest.flatten(initial_cell_state)[-1]
state_batch_size = (
final_state_tensor.shape[0].value
or tf.shape(final_state_tensor)[0])
error_message = (
"When contructing AttentionWrapper %s: " % self._base_name +
"Non-matching batch sizes between the memory "
"(encoder output) and initial_cell_state. Are you using "
"the BeamSearchDecoder? You may need to tile your initial state "
"via the tf.contrib.seq2seq.tile_batch function with argument "
"multiple=beam_width.")
with tf.control_dependencies(
self._batch_size_checks(state_batch_size, error_message)):
self._initial_cell_state = nest.map_structure(
lambda s: tf.identity(s, name="check_initial_cell_state"),
initial_cell_state)
def testNestedStructure(self):
components = (np.array([1, 2, 3]), (np.array([4., 5.]), np.array([6., 7.])),
np.array([8, 9, 10]))
dataset = dataset_ops.Dataset.from_tensors(components)
self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
dtypes.int64), dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.shuffle(10, 10)
self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
dtypes.int64), dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.repeat(-1)
self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
dtypes.int64), dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.filter(lambda x, y, z: True)
self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
dtypes.int64), dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.take(5)
self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
dtypes.int64), dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.map(lambda x, y, z: ((x, z), (y[0], y[1])))
self.assertEquals(((dtypes.int64, dtypes.int64),
(dtypes.float64, dtypes.float64)), dataset.output_types)
self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes)
dataset = dataset.flat_map(
lambda x, y: dataset_ops.Dataset.from_tensors(((x[0], x[1]),
(y[0], y[1])))
)
self.assertEquals(((dtypes.int64, dtypes.int64),
(dtypes.float64, dtypes.float64)), dataset.output_types)
self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes)
dataset = dataset.batch(32)
self.assertEquals(((dtypes.int64, dtypes.int64),
(dtypes.float64, dtypes.float64)), dataset.output_types)
self.assertEquals((([None, 3], [None, 3]), ([None, 2], [None, 2])),
nest.pack_sequence_as(dataset.output_shapes, [
s.as_list()
for s in nest.flatten(dataset.output_shapes)
]))
iterator = dataset.make_one_shot_iterator()
(w, x), (y, z) = iterator.get_next()
self.assertEquals(dtypes.int64, w.dtype)
self.assertEquals(dtypes.int64, x.dtype)
self.assertEquals(dtypes.float64, y.dtype)
self.assertEquals(dtypes.float64, z.dtype)
self.assertEquals([None, 3], w.shape.as_list())
self.assertEquals([None, 3], x.shape.as_list())
self.assertEquals([None, 2], y.shape.as_list())
self.assertEquals([None, 2], z.shape.as_list())
iterator = dataset.make_initializable_iterator()
(w, x), (y, z) = iterator.get_next()
self.assertEquals(dtypes.int64, w.dtype)
self.assertEquals(dtypes.int64, x.dtype)
self.assertEquals(dtypes.float64, y.dtype)
self.assertEquals(dtypes.float64, z.dtype)
self.assertEquals([None, 3], w.shape.as_list())
self.assertEquals([None, 3], x.shape.as_list())
self.assertEquals([None, 2], y.shape.as_list())
self.assertEquals([None, 2], z.shape.as_list())
# Define a separate set of components with matching leading
# dimension for the from-slices constructor.
components_for_slices = (np.array([1, 2, 3]), (np.array(
[4., 5., 6.]), np.array([7., 8., 9.])), np.array([10, 11, 12]))
dataset = dataset_ops.Dataset.from_tensor_slices(components_for_slices)
self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
dtypes.int64), dataset.output_types)
self.assertEquals(([], ([], []), []), dataset.output_shapes)
def testFlattenUpTo(self):
input_tree = (((2, 2), (3, 3)), ((4, 9), (5, 5)))
shallow_tree = ((True, True), (False, True))
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [(2, 2), (3, 3), (4, 9), (5, 5)])
self.assertEqual(flattened_shallow_tree, [True, True, False, True])
input_tree = ((("a", 1), (("b", 2), (("c", 3), (("d", 4))))))
shallow_tree = (("level_1", ("level_2", ("level_3", ("level_4")))))
input_tree_flattened_as_shallow_tree = nest.flatten_up_to(shallow_tree,
input_tree)
input_tree_flattened = nest.flatten(input_tree)
self.assertEqual(input_tree_flattened_as_shallow_tree,
[("a", 1), ("b", 2), ("c", 3), ("d", 4)])
self.assertEqual(input_tree_flattened, ["a", 1, "b", 2, "c", 3, "d", 4])
## Shallow non-list edge-case.
# Using iterable elements.
input_tree = ["input_tree"]
shallow_tree = "shallow_tree"
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
input_tree = ("input_tree_0", "input_tree_1")
shallow_tree = "shallow_tree"
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
# Using non-iterable elements.
input_tree = (0,)
shallow_tree = 9
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
input_tree = (0, 1)
shallow_tree = 9
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
## Both non-list edge-case.
# Using iterable elements.
input_tree = "input_tree"
shallow_tree = "shallow_tree"
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
# Using non-iterable elements.
input_tree = 0
shallow_tree = 0
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [input_tree])
self.assertEqual(flattened_shallow_tree, [shallow_tree])
## Input non-list edge-case.
# Using iterable elements.
input_tree = "input_tree"
shallow_tree = ("shallow_tree",)
expected_message = ("If shallow structure is a sequence, input must also "
"be a sequence. Input has type: <(type|class) 'str'>.")
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
input_tree = "input_tree"
shallow_tree = ("shallow_tree_9", "shallow_tree_8")
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
# Using non-iterable elements.
input_tree = 0
shallow_tree = (9,)
expected_message = ("If shallow structure is a sequence, input must also "
"be a sequence. Input has type: <(type|class) 'int'>.")
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
input_tree = 0
shallow_tree = (9, 8)
with self.assertRaisesRegexp(TypeError, expected_message):
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_shallow_tree, list(shallow_tree))
# Using dict.
input_tree = {"a": ((2, 2), (3, 3)), "b": ((4, 9), (5, 5))}
shallow_tree = {"a": (True, True), "b": (False, True)}
flattened_input_tree = nest.flatten_up_to(shallow_tree, input_tree)
flattened_shallow_tree = nest.flatten_up_to(shallow_tree, shallow_tree)
self.assertEqual(flattened_input_tree, [(2, 2), (3, 3), (4, 9), (5, 5)])
self.assertEqual(flattened_shallow_tree, [True, True, False, True])
def testNestedStructure(self):
components = (np.array([1, 2,
3]), (np.array([4., 5.]), np.array([6., 7.])),
np.array([8, 9, 10]))
dataset = dataset_ops.Dataset.from_tensors(components)
self.assertEquals(
(dtypes.int64, (dtypes.float64, dtypes.float64), dtypes.int64),
dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.shuffle(10, 10)
self.assertEquals(
(dtypes.int64, (dtypes.float64, dtypes.float64), dtypes.int64),
dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.repeat(-1)
self.assertEquals(
(dtypes.int64, (dtypes.float64, dtypes.float64), dtypes.int64),
dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.filter(lambda x, y, z: True)
self.assertEquals(
(dtypes.int64, (dtypes.float64, dtypes.float64), dtypes.int64),
dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.take(5)
self.assertEquals(
(dtypes.int64, (dtypes.float64, dtypes.float64), dtypes.int64),
dataset.output_types)
self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
dataset = dataset.map(lambda x, y, z: ((x, z), (y[0], y[1])))
self.assertEquals(
((dtypes.int64, dtypes.int64), (dtypes.float64, dtypes.float64)),
dataset.output_types)
self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes)
dataset = dataset.flat_map(lambda x, y: dataset_ops.Dataset.
from_tensors(((x[0], x[1]), (y[0], y[1]))))
self.assertEquals(
((dtypes.int64, dtypes.int64), (dtypes.float64, dtypes.float64)),
dataset.output_types)
self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes)
dataset = dataset.batch(32)
self.assertEquals(
((dtypes.int64, dtypes.int64), (dtypes.float64, dtypes.float64)),
dataset.output_types)
self.assertEquals(
(([None, 3], [None, 3]), ([None, 2], [None, 2])),
nest.pack_sequence_as(
dataset.output_shapes,
[s.as_list() for s in nest.flatten(dataset.output_shapes)]))
iterator = dataset.make_one_shot_iterator()
(w, x), (y, z) = iterator.get_next()
self.assertEquals(dtypes.int64, w.dtype)
self.assertEquals(dtypes.int64, x.dtype)
self.assertEquals(dtypes.float64, y.dtype)
self.assertEquals(dtypes.float64, z.dtype)
self.assertEquals([None, 3], w.shape.as_list())
self.assertEquals([None, 3], x.shape.as_list())
self.assertEquals([None, 2], y.shape.as_list())
self.assertEquals([None, 2], z.shape.as_list())
iterator = dataset.make_initializable_iterator()
(w, x), (y, z) = iterator.get_next()
self.assertEquals(dtypes.int64, w.dtype)
self.assertEquals(dtypes.int64, x.dtype)
self.assertEquals(dtypes.float64, y.dtype)
self.assertEquals(dtypes.float64, z.dtype)
self.assertEquals([None, 3], w.shape.as_list())
self.assertEquals([None, 3], x.shape.as_list())
self.assertEquals([None, 2], y.shape.as_list())
self.assertEquals([None, 2], z.shape.as_list())
# Define a separate set of components with matching leading
# dimension for the from-slices constructor.
components_for_slices = (np.array([1, 2, 3]), (np.array([4., 5., 6.]),
np.array([7., 8., 9.])),
np.array([10, 11, 12]))
dataset = dataset_ops.Dataset.from_tensor_slices(components_for_slices)
self.assertEquals(
(dtypes.int64, (dtypes.float64, dtypes.float64), dtypes.int64),
dataset.output_types)
self.assertEquals(([], ([], []), []), dataset.output_shapes)
