Changes in flow detection neural network?

Question

Do you have any advice, what architecture of neural network is the best for following task?

Let input be some (complex function), the neural network gains a flow of its values, so I guess there will be some kind of RNN or CNN?

The output is classifier like is the function same or not

• If the neural network thinks, that the input is still the same function, the output is 0.
• If the input function changes, the output will be 1.

The input function is of course not one value or simple math function (what will be trivial) but may be really sophisticated. So the neural network learns abstraction about same and different over any complex flow ?

How would you approach to that task ?

Answer 1

A network is able to fit to a certain function over several iteration while training. Now you want the model to be able to detect a change in a list of inputs from the function. This is not possible without first training the model on some data.

Say you want to use a simple function

# Sample function
f(x) = x

Say you create inputs for function using sets of 3 integers for x.

# Data
f1 = [[1, 2, 3],
      [4, 5, 6],
      ...      ]

# Some random values
f2 = [[1, 4, 19],
      [16, 35, 36],
      ...      ]

Now if you use data f1 labelled as 0 and data f2 labelled as 1, in the best case the ANN will only learn to differentiate between data from function f1 and some other data.

To detect change, first the model has to fit to a certain function, this requires it to be trained over a number of epochs. Then it will be able to detect if the values don't match the function, but such a model will only be able to differentiate a single function.