Hi, I’m using Keras API for tabular data with embedding, conv1D and sequential with onehot encoding to get a merged model more efficient (after a concatenation layer). I’m very interesting in adding a Keras gradient boosting model but I guess it won’t be possible to get the output layer (and input) to concatenate multiple outputs and create a merged model ? It seems we can only fit the gradient model (without compile step) as any other gradient boosting models (Xgb, Lgb, Cat…). Do I miss something ? Thank you very much for your information

I read the intermediate colab page, but my question was about the gradient boosting model which is much more efficient than a random forest for tabular data.
In the page, It seems that only random forest can be concatenated with functional API . In addition, my understanding (but I need to go deeper in the example provided in the page) is that the training is performed in 2 steps (not in //). My objective is to use multiple heads  made of functional API (head for embedding  layer,  head for Conv1D…) to compute a tabular dataset in // and concatenate the // heads into a final stream (still API) for a prediction ( with some dense layers and finally a softmax activation for example). Of course, a head with gradient boosting would improve the result of the concatenation.
I did it with a head made of Decision Forest model (from Keras deep_neural_decision_forests) but it was not very powerfull .
By the way, it seems (not important) that you should change vocabulary_size in the data pipeline by vocab_size because it leads to an error in google colab notebook.