Cloning a TextVectorization Layer with Split Function Doesn't Work about tf-keras

[rlcauvin]

Inspecting the code for TextVectorization (https://github.com/keras-team/keras/blob/master/keras/layers/preprocessing/text_vectorization.py#L491) and deserialize_keras_object (https://github.com/keras-team/keras/blob/master/keras/saving/serialization_lib.py#L392), I see that there is no way the proper logic for deserializing the split function will run. The deserialization code looks for if module_objects is not None:, but TextVectorization.from_config() doesn't pass a module_objects parameter to deserialize_keras_object, so that code block doesn't execute.

As a workaround, I extended the tf.keras.layers.TextVectorization class with:

class PatchedTextVectorization(tf.keras.layers.TextVectorization):

  @classmethod
  def from_config(cls, config):
    if not isinstance(config["standardize"], str):
      config["standardize"] = tf.keras.saving.deserialize_keras_object(config["standardize"])
    if not isinstance(config["split"], str):
      config["split"] = tf.keras.saving.deserialize_keras_object(config["split"], module_objects = [])

    return cls(**config)

Cloning an instance of PatchedTextVectorization constructed with the split function works fine. You can see I shoehorned module_objects = [] into its invocation of tf.keras.saving.deserialize_keras_object.

[tilakrayal]

@sachinprasadhs,
I was able to reproduce the issue on tensorflow v2.14, v2.15. Kindly find the gist of it here.

[sachinprasadhs]

@rlcauvin , From TensorFlow 2.16, Keras 3 will be the backend for tf.keras, I see this is working fine with Keras 3, that should fix your issue, is there any specific reason you're using tf.keras with 2.15?
You can use tensorflow 2.15 and Keras 3 as well.
install tensorflow first and then install keras 3.

[github-actions]

This issue is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.

[rlcauvin]

Thank you, @sachinprasadhs. Using !pip install -U keras to install Keras 3 worked for that isolated case. However, I want to use tensorflow_decision_forests. Unfortunately, import tensorflow_decision_forests as tfdf results in the error below. Please see the modified Google Colab notebook for the full code.

[/usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/keras/core.py](https://localhost:8080/#) in <module>
     75   # tf>1.12
---> 76   import keras.src.engine.data_adapter as data_adapter
     77 except ImportError:

ModuleNotFoundError: No module named 'keras.src.engine'

During handling of the above exception, another exception occurred:

ModuleNotFoundError                       Traceback (most recent call last)
3 frames
[<ipython-input-3-b4486e63aff0>](https://localhost:8080/#) in <cell line: 1>()
----> 1 import tensorflow_decision_forests as tfdf
      2 import tensorflow as tf
      3 from typing import Text

[/usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/__init__.py](https://localhost:8080/#) in <module>
     62 check_version.check_version(__version__, compatible_tf_versions)
     63 
---> 64 from tensorflow_decision_forests import keras
     65 from tensorflow_decision_forests.component import py_tree
     66 from tensorflow_decision_forests.component.builder import builder

[/usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/keras/__init__.py](https://localhost:8080/#) in <module>
     51 from typing import Callable, List
     52 
---> 53 from tensorflow_decision_forests.keras import core
     54 from tensorflow_decision_forests.keras import wrappers
     55 

[/usr/local/lib/python3.10/dist-packages/tensorflow_decision_forests/keras/core.py](https://localhost:8080/#) in <module>
     77 except ImportError:
     78   # tf<=1.12
---> 79   import keras.engine.data_adapter as data_adapter
     80 get_data_handler = data_adapter.get_data_handler
     81 

ModuleNotFoundError: No module named 'keras.engine'

[nkovela1]

Fixed in this commit! Thank you, closing this issue.