Whether a neural network has learned anything or not, it is a function that maps some input to an output. Training is the process of tweaking the weights so that the output is something that we want. Thus there is always in input of some sorts.
The problem you have presented, of generating even numbers, is much like a Generative Adversarial Network (GAN). In a GAN there are 2 networks: a Generator that tries to generate a sample from a target distribution and a Discriminator that tries to tell real samples from fake samples. The classic analogy being a criminal making counterfeit money and a copy trying to tell what is real money or not.
The generator input is usually a random number (or a matrix of random numbers). The generator then learns to transform a particular random input to a particular point in the target space.
So to answer your question, no there can't be a neural network with 0 inputs as there must always be an input of some kind. Even if the network was to generate a sequence instead of one instance, it would still need something to start with.
For your example, there would have to be some input for the network to start with. A really simple NN that could solve your problem might look like:
_input = [RandomInteger()]
neuralNetworkWeights = 
result = _input * neuralNetworkWeights
result is always even