Spark version 3.4.0 Python 3.9.16 tensorflow 2.17.0 Hi,
I encountered an issue while building a VAE (Variational Autoencoder <https://en.wikipedia.org/wiki/Variational_autoencoder>) model using the following configuration: I am doing this work as part of imputation of fraud data - Input dimension: 250 - Latent dimension: 32 - Method name: build_vae_model This error occurred when calling build_vae_model within the impute_data_vae module, leading to a failure with the following error description: *Error:* A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras.layers` and `keras.operations`). You are likely doing something like: ``` x = Input(...) ... tf_fn(x) # Invalid. ``` What you should do instead is wrap `tf_fn` in a layer: ``` class MyLayer(Layer): def call(self, x): return tf_fn(x) x = MyLayer()(x) ``` As a next step, I will be adjusting the build_vae_model method to wrap the TensorFlow function(s) inside appropriate Keras layers. It is becoming very time consuming. If anyone has faced a similar issue or has recommendations on the best practices for handling, I will appreciate it. Thanks Mich Talebzadeh, Architect | Data Engineer | Data Science | Financial Crime PhD <https://en.wikipedia.org/wiki/Doctor_of_Philosophy> Imperial College London <https://en.wikipedia.org/wiki/Imperial_College_London> London, United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
