sushreebarsa

This issue seems to be TF v1.x which is not actively supported. Defining custom ops that handle 1-based indexing internally could work in this case. Please check the [migration](https://www.tensorflow.org/guide/migrate) doc....

@bryant1410 freeze_graph.py is primarily for TensorFlow v1. For newer versions, we should consider alternative freezing methods like tf.saved_model.save. Please refer to the [migration](https://www.tensorflow.org/guide/migrate) guide. If you still have a concern...

@GoldFeniks Could you please let us know if the issue has been resolved for you ? Thank you!

@Mistobaan This is not yet implemented nor any workaround available it seems, Please refer the existing projects like tensorflow/serving that build TensorFlow as an external dependency and could you try...

@danijar Maybe we can Try-Except Block Around session.run() by Enclose session.run() calls within a try-except block to catch the KeyboardInterrupt exception. TensorFlow 2.x uses a different approach for training, with...

@jpmartin2 If compatibility with tfcompile isn't crucial, we can explore using TensorFlow's SaveModel format for saving and loading models. It supports graphs without explicit inputs. TF 1.x is not actively...

@smrtslckr TensorFlow's tf.feature_column.categorical_column_with_hash_bucket provides a hashing mechanism for categorical features, but not specifically designed for embedding layers. But tf.feature_column is not recommended for new code. Instead, feature preprocessing can be...

@leandro-gracia-gil Other TensorFlow functions like tf.stack and tf.concat already support heterogeneous tensor inputs and automatically perform type casting if necessary. It is a TF 1.x related issue which needs to...

@babak-badnava Could you please upgrade to the latest TF version as we are unable to reproduce the same on the new code base. Kindly have a look at this migration...

@edgimar It is raising this [error](https://colab.research.google.com/gist/sushreebarsa/3f787d1c833de9a1732b281564601535/12019.ipynb). Please have a look at the [migration](https://www.tensorflow.org/guide/migrate) document and try to upgrade to the latest stable version. If you still face the issue then...