Solving Math using Neural Network.

Question

I was reading an article for Neural Networks from the below URL, and I was checking the example.

https://www.sololearn.com/learn/731/?ref=app
https://www.sololearn.com/learn/744/?ref=app

Example -
https://code.sololearn.com/cZSguJFA16ve/?ref=app

but I can't understand the Line No 7 in the example which says
self.weights = 2 * random.random((2, 1)) - 1

How this formula derived for adding random weight?
I mean does this formula will always same for any mathematic operation, if not how we can derive formula?

Answer 1

That's just how the weights are initialized.

x * numpy.random.random((y, z))

means:

Create a 2 dimensional array of pseudo random numbers between 0 and 1 with y rows * z columns and multiply each number in the array with x.

Answer 2

Anna yh I got that, but I want to know how this formula derived.
I mean if I want to create a code for square of a number similar as above example this formula won't work.

so for me, how this things are working is more important, what would be the formula for square of number.

Answer 3

If I adjust the outputs to be the result of (a+b)² it works quite well.
For the new input it says 281.5 as a result. The exact result would be 289, which is quite close in my opinion.

Answer 4

Nice work

Answer 5

Matthias I would be interested to know the reason why the neural network is not able to learn the weights for (a+b)^2 exactly although it is provided perfect training data... just like for (a+b)*2?

Would a code update work?
Would you require more training data?
Should the network structure be more complex?

Thanks for any input if you read this still 👍

Answer 6

Pe Kie Unfortunately I can't explain it well, I only know very bit of machine learning 🙈

There was a comment of another user explaining it quite well, but it seems it got deleted :/

What it was basically saying is, that the structure of used network corresponds to linear functions. For quadratic function we need a different network.
Something like this.
But take it with care, I know nothing xD