ValueError in the tutorial on Colab environment

Hello

The ValueError error Insufficient elements in branch_graphs[0].outputs. is found in both Neural Graph Learning tutorials (sentiment and document classification).

Both happen during the training of graph_reg_model.fit() process. The error messages are shown below, I think it is due to the same issue.

• Graph regularization for document classification using natural graphs

Epoch 1/100
      1/Unknown - 0s 303ms/step

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-20-0c7d19de6181> in <module>()
     10     loss='sparse_categorical_crossentropy',
     11     metrics=['accuracy'])
---> 12 graph_reg_model.fit(train_dataset, epochs=HPARAMS.train_epochs, verbose=1)

25 frames

/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/cond_v2.py in _make_indexed_slices_indices_types_match(op_type, branch_graphs)
    650                      "Expected: %i\n"
    651                      "Actual: %i" %
--> 652                      (current_index, len(branch_graphs[0].outputs)))
    653 
    654   # Cast indices with mismatching types to int64.

ValueError: Insufficient elements in branch_graphs[0].outputs.
Expected: 11
Actual: 10

• Graph regularization for sentiment classification using synthesized graphs

Epoch 1/10
      1/Unknown - 2s 2s/step

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-30-e49eed0ffe51> in <module>()
      3     validation_data=validation_dataset,
      4     epochs=HPARAMS.train_epochs,
----> 5     verbose=1)

25 frames

/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/cond_v2.py in _make_indexed_slices_indices_types_match(op_type, branch_graphs)
    650                      "Expected: %i\n"
    651                      "Actual: %i" %
--> 652                      (current_index, len(branch_graphs[0].outputs)))
    653 
    654   # Cast indices with mismatching types to int64.

ValueError: Insufficient elements in branch_graphs[0].outputs.
Expected: 18
Actual: 17

Thanks

[basirshariat]

I upgraded to 2.2.0rc1 and i get this error when the fit of the base model is called:

/python3.7/site-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility ’ but received input with shape ’ + str(shape))

ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 1433 but received input with shape [None, 1]

[arjung]

This should no longer be an issue since TF 2.2.0 has been released. Sorry about the version complications, things will be much simpler now.

https://github.com/tensorflow/neural-structured-learning/commit/edead04aa3be59f07baea52fc2cf22c320cdaff5 updates the neural graph learning tutorials to use the default TF version in colab.