Hi, this is similar to a previous issue I had opened here.
I basically have a model with two conditional parameters. Previously I had only one, and I found that feeding it twice was the best way to get the model to avoid confusion. Without feeding it twice to the model, the predictions would 'confuse the conditions' i.e. predict similar outputs for any condition.
I now have introduced another condition, that can either be a 1 or a 0 since it acts as a switch or boolean. To avoid the confusion that affected the previous model, I have fed the condition twice this time as well. This time this is not enough. This is the diagram of the model:
As you can see the two (None,1) layers (conditional) are fed twice but at the same time. This method still does not give good results.
Hi, this is similar to a previous issue I had opened here. I basically have a model with two conditional parameters. Previously I had only one, and I found that feeding it twice was the best way to get the model to avoid confusion. Without feeding it twice to the model, the predictions would 'confuse the conditions' i.e. predict similar outputs for any condition.
I now have introduced another condition, that can either be a 1 or a 0 since it acts as a switch or boolean. To avoid the confusion that affected the previous model, I have fed the condition twice this time as well. This time this is not enough. This is the diagram of the model:
As you can see the two (None,1) layers (conditional) are fed twice but at the same time. This method still does not give good results.
Any idea on how this can be improved?