Deep nets for finding optimal values for inputs

I’m working on building a model to forecast demand for 1000s of different things. Price will be a variable included in the model, so after I’m done tuning my best model, I’d like to find “optimal” prices to use.

I created a couple of examples in Keras to test the idea and it seems to be working on a small scale. You can view my notebook here.

My method is somewhat simple

  1. Build a model with price as a variable
  2. Freeze the model weights
  3. Create a single trainable weight (weights for multiple items) and insert it right after the price input
  4. Also multiply that weight with the final output of first trained model that estimated sales to get revenue
  5. Train model (that single weight) to minimize the negative output (which maximizes expected revenue)

I was hoping anyone here could give some criticism or advice. My model will be a bit larger scale with way more inputs and I will probably be using a recurrent net as well since I’m forecasting time series. I’d like to avoid any pitfalls or statistical blunders that might pop up from naive model assumptions.