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I am totally new to Artificial Neural Networks. Let’s say that the model you are trying to turn into an artificial neural network has an output that is triggered only by the exceedance of a threshold: $y\geq y_{1}. $Therefore, you need to find a way to use this inequality as an activation function. Is this feasible?

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  • $\begingroup$ Elaborate a little bit more. All you talking about input data, that will only activate a 1 if the input data is above a certain value. Or are you talking about output: if the output is higher than a certain treshold, it will output 1, else 0. $\endgroup$ May 22, 2017 at 19:32
  • $\begingroup$ Sorry to have not made this clear. The 1 will be produced in the output only if the input is already 1. $\endgroup$
    – FaCoffee
    May 22, 2017 at 20:40

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This is feasable. This is also called a Binary/Step activation function. You must only use this activation function on the output neurons.

The Step function will round down an answer that is lower than 0.5 to 0, and an answer that is higher than 0.5 to 1. However, please note that you do not need to use a binary activation function to output 1 - I advise you to just use TanH or sigmoid and backpropagate a whole bunch of iterations.

However, in other comments, you mentioned that you know what y1 is. That is not of importance, the network will act as a black box and will figure out a treshold itself. Don't set up your own activation function just to get the right output - that avoids the whole point of backpropagation.

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