fixing various errors on the file classification_with_grn_and_vsn

[Humbulani1234]

Dataset preparation errors

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The example file from structured_data classification_with_grn_and vsn.py I think it is using the wrong dataset, i.e., the data_url: https://archive.ics.uci.edu/static/public/20/census+income.zip leads to a download of an incorrect dataset. The correct data_url, I believe should be: https://archive.ics.uci.edu/static/public/117/census+income+kdd.zip

To extract the downloaded .tar.gz file, created during keras.utils.get_file, a fix has been added.

A fix was also added to clean up the directory that the files where extracted to during download in order to run the script again without errors:

Additionally, the original script has the code snippet:

train_data_path = os.path.join(
    os.path.expanduser("~"), ".keras", "datasets", "adult.data"
)
test_data_path = os.path.join(
    os.path.expanduser("~"), ".keras", "datasets", "adult.test"
)

The above snippet doesn't account for the directory created during keras.utils.get_file extraction process census+income+kdd.zip which leads to an incorrect path for both train_data_path and test_data_path, and a fix has been added.

Additional training errors

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After covering the above dataset's preparation process, the script also has an additional error encountered during model training, detailed below and an attempted solution provided:

2024-12-19 21:02:15.350619: W tensorflow/core/framework/op_kernel.cc:1816] OP_REQUIRES failed at cast_op.cc:122 : UNIMPLEMENTED: Cast string to float is not supported
2024-12-19 21:02:15.350683: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: UNIMPLEMENTED: Cast string to float is not supported
Traceback (most recent call last):
  File "/home/humbulani/tensorflow-env/keras-io-master/examples/structured_data/classification_with_grn_and_vsn.py", line 513, in <module>
    model.fit(
  File "/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/tensorflow/python/framework/ops.py", line 5983, in raise_from_not_ok_status
    raise core._status_to_exception(e) from None  # pylint: disable=protected-access
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.UnimplementedError: Exception encountered when calling Functional.call().

{{function_node __wrapped__Cast_device_/job:localhost/replica:0/task:0/device:CPU:0}} Cast string to float is not supported [Op:Cast] name:

Attempted solution:

I believe I have precisely traced the error to the following, here is a pdb script:

> /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/models/functional.py(245)_convert_inputs_to_tensors()
-> converted = []
(Pdb) p self._inputs
[<KerasTensor shape=(None,), dtype=float32, sparse=False, name=age>, <KerasTensor shape=(None,), dtype=float32, sparse=False, name=capital_gains>, <KerasTensor shape=(None,), dtype=float32, sparse=False, name=capital_losses>, ...]

(Pdb) p flat_inputs
[<tf.Tensor: shape=(265,), dtype=float32, numpy=
array([63., 52.,  2., 45.,  0., 43., 67., 26., 29., 53., 31., 59., 57.,...>, <tf.Tensor: shape=(265,), dtype=string, numpy=
array([b' Not in universe', b' Private', b' Not in universe', b' Private',
       b' Not in universe', b' Private', b' Not in universe',...>...]

The function _convert_inputs_to_tensors creates a zip iterator pairing together flat_inputs and self._inputs, and as per the pdb output above the first element (age) from flat_inputs and self._inputs has float32 dtype, however the second element (capital_gains) has a float32 dtype and a string dtype causing the discrepancy, and hence the error.

The main issue is that inputs datatype to the method Functional.call is a OrderedDict and in the function _standardize_inputs the line flat_inputs = tree.flatten(inputs) is not actually ordering/sorting the OrderedDict as per doc for the function tree.flatten. This contributes to the mismatch between self._inputs, the models inputs, and flat_inputs. Hence a fix has been provided in the script function process to convert features to dict.

Fix provided, and I think tree.flatten functionality must be assessed and rectified.

Environment

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Tensorflow == 2.16.2
Keras == 3.7.0
Python == 3.11.10

[Humbulani1234]

Find generated .ipynb and .md files.

[Humbulani1234]

Will resend