To compute new features from existing inputs within a neural network, use a Lambda layer. This layer allows you to apply any custom function to an input tensor, creating new, derived features. This lets your model learn from more meaningful, calculated values instead of just raw inputs. I have tried with dummy dataset and able to implement an input layer as a weight to a second input layer. I am attaching gist file for your reference.