Ask doesn't handle namedtuples properly

System information

  • Have I written custom code (as opposed to using a stock example script provided in TensorFlow): true
  • TensorFlow installed from (source or binary): collab notebook
  • TensorFlow version (use command below): v2.2.0-rc4-0-g70087ab4f4
  • Python version: 3.6.9

Describe the current behavior Handling of namedtuples by datasets is not coherent: elementspec does not reflect the input type

Describe the expected behavior The elementspec should reflect the structured input

Standalone code to reproduce the issue


Answer questions aaudiber

@AdrienCorenflos As far as I can tell, element_spec does work correctly for named tuples:

import tensorflow as tf
import collections

Point = collections.namedtuple('Point', ['x', 'y'])
dataset =[1, 2, 3], [4, 5, 6]))

Point(x=TensorSpec(shape=(), dtype=tf.int32, name=None), y=TensorSpec(shape=(), dtype=tf.int32, name=None))

One easy mistake to make is passing a list of tuples or named tuples to from_tensor_slices. from_tensor_slices expects its input to be a structure of tensors, and will coerce Python lists (along with their contents) into tensors. For example, [(1, 2), (3, 4)] is seen as a 2d tensor of integers, equivalent to [[1, 2], [3, 4]]. The same applies to [Point(1, 2), Point(3, 4)]. This could make it look like named tuples aren't being respected properly if you call[Point(1, 2), Point(3, 4)]). The argument [Point(1, 2), Point(3, 4)] will be interpreted as equivalent to [[1, 2], [3, 4]].

I think this behavior is pretty unintuitive (it looked like a bug at first to me too). However, we can't change the behavior without breaking backwards compatibility, so I think the action item here is to improve the documentation to make it clear that the input is treated as a structure of Tensors, not a list of dataset elements.


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