# How to train this neural network?

I seek the neural network (NN) which satisfies the 100 equations (i=1,2...100)

$$\sum_{j=1}^{2000} NN(A_{ij},B_{ij},C_{ij})=Q_i$$.

Where A,B,C are 100x2000 matrices

So I know Q, A, B and C How can one find the NN? (ie train it using this data) I have a second datset to test it on. I also have MATLAB neural network toolbox, but that is not a necessity for me to use that.

• Questions about specific software packages are off-topic here. – Yuval Filmus Feb 18 '19 at 11:11
• Is your neural network out of the ordinary? If so, can you explain in what way? Otherwise, you train the network just you train any other network. – Yuval Filmus Feb 18 '19 at 11:11
• But I dont have a target, I only have sums of targets. – KALLE THE BAWSMAN Feb 18 '19 at 11:12
• I don't really understand your question – in your first equation, the network has a single parameter, and in the second, two parameters. Are you talking about two different situations? Also, it seems like you can find a linear function NN satisfying your constraints (if at all possible) by solving linear equations. So neural networks aren't really needed at all. – Yuval Filmus Feb 18 '19 at 11:17
• Two situations. This function is not linear. – KALLE THE BAWSMAN Feb 18 '19 at 11:19

Let $$w$$ denote the weights of the neural network. Define the objective function $$\Psi$$ by
$$\Psi(w) = \sum_i \left(\left(\sum_j NN_w(A_{ij},B_{ij},C_{ij})\right) - Q_i\right)^2.$$
Then, find $$w$$ that minimizes $$\Psi(w)$$ using gradient descent. You can find the gradient of $$\Psi(w)$$ using backpropagation through the neural network. You can use all the standard methods for speeding up training of neural networks: stochastic gradient descent, Adam or momentum or Adagrad, etc.