Is it okay to have non-trainable params in machine learning?
Usually, non-trainable params does not mean some weights to which you assign random or predefined values (there would be no way to know the correct values, as they depend on the other weight values).
It means some architectural decisions that you have made a priori. For example, in a neural network , such parameters would be the number of layers and the number of nodes per layer. These can be decided by educated guesses, or, usually, by trial-and-error (cross-validation).
Having such non-trainable parameters is not only ok, but unavoidable with most training algorithms.
When building a model for machine learning, is it okay to have non-trainable params? Or does this create errors in the model? I'm confused as to what non-trainable params actually are and how to fix your model based on that.