LSTM, Keras : How many layers should the inference model have?
I don't know what you exactly mean by inference model. 
The number of layers of a model is an hyperparameter that you tune during training. Let's say that you train an LSTM model with 3 layers, then the model used for inference must have the same number of layers and use the weights resulting from the training.
Otherwise, if you add non trained layer when inference, the results won't make any sense.
Hope this helps
Should the inference model in a chatbot model with keras lstm, have the same amount of layers as the main model or it doesnt matter?