! Below is an example whereby a network with 2 inputs, 1 output and 
! two hidden layers each with 3 nodes is trained to output the XOR of the two inputs:

! The training files should start with the number of inputs, followed by the number of outputs, each on a new lines. 
! The rest of the data should then follow, with inputs before their outputs. 
! For example, if there are two inputs and one output, the following file suggests that 1 and 0 should result in 1:

! following file suggests that 1 and 0 should result in 1:

use neural

type(network) :: testNet

call neural_init_network(testNet, (/ 2, 3, 5, 1 /))
                                 !  4 so ung voi 4 layers va so node cua tung layer 
                                 ! layer 1=input, layer 2,3 hidden, layer va so node trong layer, layer 4=output
call neural_train_network_file(testNet, "xor.dat", 10000)
                 ! xor.dat la input data filename:
                 ! Dong 1: So bien dau vao (X1,X2,..,Xm)
                 ! Dong 2: So bien dau ra (y1, Y2, Yk)
                 ! m+k dong tiep theo: m gia tri cua X va k gia tri cua Y
                 ! Neu dung luong mau bang N thi se co N khoi m+k tren day.

 call neural_save_network(testNet, "network.dat")
 call neural_load_network(testNet, "network.dat")

print *, "1 and 1 = ", neural_use_network(testNet, (/ 1.0d00, 1.0d00 /))  ! Test for x1=1, x2=1
print *, "0 and 1 = ", neural_use_network(testNet, (/ 0.0d00, 1.0d00 /))  ! Test for x1=0, x2=1

End