# Creating a neural network to solve inequality

I am just testing out a simple neural network with a single neuron. To classify if a number X between 1..10 is greater than a number N. N is a constant for example N=3.

Given my input X and a constant neuron 1. My output is (w1*X+w2) where w are weights.

But what I'm finding is that some values of N lead to faster training than others.

In particular the training leads to an equation w1*X+w2>0 and the neural network learns by gradually getting better values for the weights. Different values of N will give different ratios w1/w2.

This ratio it seems is related to how fast the neural network will learn.

Will it always be easier/harder to classify if a number N>=5 than say N>=2 or N>=9 ?

Also there is a redundancy in the equation w1*X+w2>0 which since we can multiply w1 and w2 by a constant. How can we remove this redundancy?

• `Will it always be easier/harder to classify if a number N>=5 than say N>=2 or N>=9 ?` -- No. it will also depend on many other factors, like how many neurons you have, the topology of the network, the noisiness of the data, etc. Commented Nov 19, 2016 at 16:02
• That's the problem or the neural networks, they work but you can't interpret the result, know if you need another layer, more neurons.... You need to test all those parameters. That's why one of my professor told me once that because of this, some competition between scholl turns out to be at those who have the best computers. Commented Nov 19, 2016 at 16:16