tf.nest.pack_sequence_as

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Returns a given flattened sequence packed into a given structure.

tf.nest.pack_sequence_as(
    structure, flat_sequence, expand_composites=False
)

Refer to tf.nest for the definition of a structure.

If structure is an atom, flat_sequence must be a single-item list; in this case the return value is flat_sequence[0].

If structure is or contains a dict instance, the keys will be sorted to pack the flat sequence in deterministic order. This is true also for OrderedDict instances: their sequence order is ignored, the sorting order of keys is used instead. The same convention is followed in flatten. This correctly repacks dicts and OrderedDicts after they have been flattened, and also allows flattening an OrderedDict and then repacking it back using a corresponding plain dict, or vice-versa. Dictionaries with non-sortable keys cannot be flattened.

Examples:

1. Python dict:

   structure = { "key3": "", "key1": "", "key2": "" }
   flat_sequence = ["value1", "value2", "value3"]
   tf.nest.pack_sequence_as(structure, flat_sequence)
     {'key3': 'value3', 'key1': 'value1', 'key2': 'value2'}
     

2. For a nested python tuple:

   structure = (('a','b'), ('c','d','e'), 'f')
   flat_sequence = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
   tf.nest.pack_sequence_as(structure, flat_sequence)
     ((1.0, 2.0), (3.0, 4.0, 5.0), 6.0)
     

3. For a nested dictionary of dictionaries:

   structure = { "key3": {"c": ('alpha', 'beta'), "a": ('gamma')},
                 "key1": {"e": "val1", "d": "val2"} }
   flat_sequence = ['val2', 'val1', 3.0, 1.0, 2.0]
   tf.nest.pack_sequence_as(structure, flat_sequence)
     {'key3': {'c': (1.0, 2.0), 'a': 3.0}, 'key1': {'e': 'val1', 'd': 'val2'} }
     

4. Numpy array (considered a scalar):

   structure = ['a']
   flat_sequence = [np.array([[1, 2], [3, 4]])]
   tf.nest.pack_sequence_as(structure, flat_sequence)
     [array([[1, 2],
            [3, 4]])]
     

5. tf.Tensor (considered a scalar):

   structure = ['a']
   flat_sequence = [tf.constant([[1., 2., 3.], [4., 5., 6.]])]
   tf.nest.pack_sequence_as(structure, flat_sequence)
     [<tf.Tensor: shape=(2, 3), dtype=float32,
      numpy= array([[1., 2., 3.], [4., 5., 6.]], dtype=float32)>]
     

6. tf.RaggedTensor: This is a composite tensor thats representation consists of a flattened list of 'values' and a list of 'row_splits' which indicate how to chop up the flattened list into different rows. For more details on tf.RaggedTensor, please visit https://www.tensorflow.org/api_docs/python/tf/RaggedTensor.

With expand_composites=False, we treat RaggedTensor as a scalar.

  structure = { "foo": tf.ragged.constant([[1, 2], [3]]),
                "bar": tf.constant([[5]]) }
  flat_sequence = [ "one", "two" ]
  tf.nest.pack_sequence_as(structure, flat_sequence,
  expand_composites=False)
    {'foo': 'two', 'bar': 'one'}
    
  

With expand_composites=True, we expect that the flattened input contains the tensors making up the ragged tensor i.e. the values and row_splits tensors.

  structure = { "foo": tf.ragged.constant([[1., 2.], [3.]]),
                "bar": tf.constant([[5.]]) }
  tensors = tf.nest.flatten(structure, expand_composites=True)
  print(tensors)
    [<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[5.]],
     dtype=float32)>,
     <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 2., 3.],
     dtype=float32)>,
     <tf.Tensor: shape=(3,), dtype=int64, numpy=array([0, 2, 3])>]
  verified_tensors = [tf.debugging.check_numerics(t, &#x27;invalid tensor: ')
                      if t.dtype==tf.float32 else t
                      for t in tensors]
  tf.nest.pack_sequence_as(structure, verified_tensors,
                           expand_composites=True)
    {&#x27;foo': <tf.RaggedTensor [[1.0, 2.0], [3.0]]>,
     &#x27;bar': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[5.]],
     dtype=float32)>}