def tensor_list(elements,
element_dtype=None,
element_shape=None,
use_tensor_array=False):
"""Creates an tensor list and populates it with the given elements.
This function provides a more uniform access to tensor lists and tensor
arrays, and allows optional initialization.
Note: this function is a simplified wrapper. If you need greater control,
it is recommended to use the underlying implementation directly.
Args:
elements: Iterable[tf.Tensor, ...], the elements to initially fill the list
with
element_dtype: Optional[tf.DType], data type for the elements in the list;
required if the list is empty
element_shape: Optional[tf.TensorShape], shape for the elements in the list;
required if the list is empty
use_tensor_array: bool, whether to use the more compatible but restrictive
tf.TensorArray implementation
Returns:
Union[tf.Tensor, tf.TensorArray], the new list.
Raises:
ValueError: for invalid arguments
"""
_validate_list_constructor(elements, element_dtype, element_shape)
if use_tensor_array:
return data_structures.tf_tensor_array_new(elements, element_dtype,
element_shape)
else:
return data_structures.tf_tensor_list_new(elements, element_dtype,
element_shape)
def test_tf_tensor_array_new_illegal_input(self):
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, 4.0])
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, 4],
element_dtype=dtypes.float32)
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, [4, 5]])
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, 4], element_shape=(2, ))
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([], element_shape=(2, ))
# TAs can infer the shape.
self.assertIsNot(
data_structures.tf_tensor_array_new([],
element_dtype=dtypes.float32),
None)
def test_tf_tensor_array_new_illegal_input(self):
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, 4.0])
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, 4], element_dtype=dtypes.float32)
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, [4, 5]])
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([3, 4], element_shape=(2,))
with self.assertRaises(ValueError):
data_structures.tf_tensor_array_new([], element_shape=(2,))
# TAs can infer the shape.
self.assertIsNot(
data_structures.tf_tensor_array_new([], element_dtype=dtypes.float32),
None)
def test_tf_tensor_array_new(self):
l = data_structures.tf_tensor_array_new([3, 4, 5])
t = l.stack()
with self.cached_session() as sess:
self.assertAllEqual(sess.run(t), [3, 4, 5])
def test_tf_tensor_array_new(self):
l = data_structures.tf_tensor_array_new([3, 4, 5])
t = l.stack()
with self.cached_session() as sess:
self.assertAllEqual(sess.run(t), [3, 4, 5])
