How to create a model that can give an output with a range of 0 to 1 with a sigmoid activation function where the value closer to 0 means the lesser chance that the input number is not prime and the closer to 1 means the greter chance that the input number is prime.
This means that the output layer will contain one neuron with a sigmoid activation function.
But I don't know what the input looks like.
So, from this problem, I would like to ask, what does the dataset look like?
Is it a one-dimensional list of prime numbers? like: [2,3,5,...,9997]
Or is it actually an unsupervised learning problem?
What do the input, hidden, and output layers look like?
So in the end, I was expecting this kind of input-output:
#1 Input: 77772 Output: 0.01 #2 Input: 27777 Output: 0.69