martende

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