While training the neural network (or any other supervised learning algorithms), we supply input variables and corresponding outputs. The input variables can be continuous or discrete (binary in many cases).
What happens if after training with a binary input data, we supply a continuous value for the same input at the time of evaluation? Does the algorithm internally treat all variables as continuous variables?
For example, suppose one of the inputs is Young/Old encoded in the form of 0/1 in the training dataset. What happens if we supply a value of, say, 0.2 at the prediction stage? Does it/should it make any sense to the network?