Hello all,
i have some kind of task where i want to use a Netowrk with one hidden layer.
the simpliest version of task is to find argmax of each summed row in matrix.
(ofcourse in practice this task is a bit different - outuput is not argmax but some experemental answer that should be very similiar to it )
for example input
[ 1 3 4
5 1 1
2 2 2 ]
wir make sum on each row
1+ 3 +4 = 8 and etc.
5 + 1 +1 = 7
2 +2+2 = 6
and as output wir should receive
[2 1 0 ] - this means that row 1 - is maximum
all inputs are normalized t 0 - 1
Neural network works not very fine already on 3x3 matrix - it has max precision ~70% on Train ~1000 inputs .
But i need in live 30 x 6 matrix or something simmilar . on this matrix Neural network dont find any solution and diverge from minimum on each epoch . I have tryed to use 10.000 data for training but NN diverge even faster.
Network has structur : 1 hidden layer with 2*input layers
Have somebody ideas what should i change to build this NN with satisfiable probabilty ? Or I have even another question is it in princip possible to find argmax with NN
