Neural Network Recognising in Scilab - neural-network

Question

I try to run this example, which uses ANN Toolbox for Scilab
https://burubaxair.wordpress.com/2014/03/12/artificial-neural-networks-in-scilab/
This is code:
T = [
1 1 1 1 1
0 0 1 0 0
0 0 1 0 0
0 0 1 0 0
0 0 1 0 0
0 0 1 0 0
0 0 1 0 0
]';
U = [
1 0 0 0 1
1 0 0 0 1
1 0 0 0 1
1 0 0 0 1
1 0 0 0 1
1 0 0 0 1
0 1 1 1 0
]';
N = [35 10 2];
W = ann_FF_init(N);
x = [1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
0, 1, 1, 1, 0]';
t_t = [1 0]';
t_u = [0 1]';
t = [t_t, t_u];
lp = [0.01, 1e-4];
epochs = 3000;
W = ann_FF_Std_batch(x,t,N,W,lp,epochs);
y = ann_FF_run(x,N,W)
disp(y)
But i receive an error:
-->exec('D:\Учёба\Задачи\Recognition.sce', -1)
!--error 15
Подматрица задана некорректно (Submatrix is incorrect).
at line 37 of function ann_FF_grad_BP called by :
at line 25 of function ann_FF_Std_batch called by :
W = ann_FF_Std_batch(x,t,N,W,lp,epochs);
at line 33 of exec file called by :
exec('D:\Учёба\Задачи\Recognition.sce', -1)
An error may be in T and U matrix, but i don't understand why. Could you tell be what i do wrong? Thank you!

Answer 1

You've made 2 errors in your code:
You should not mix testing and training sets.
Testing input must be a single column.
Your first error was x = [ 1.... ] because it contained a single image, whereas you specified in N that you had two output neurons.
As stated in the example, you should have x = [T,U];
Your second error was to give x as a test to ann_FF_run. This function takes test input as a single column. But since you trained your NN with x just before it was a 5x7 matrix. Just change it to a column vector.
Here a corrected and commented code :
T = [...
1 1 1 1 1 ...
0 0 1 0 0 ...
0 0 1 0 0 ...
0 0 1 0 0 ...
0 0 1 0 0 ...
0 0 1 0 0 ...
0 0 1 0 0 ...
]';
U = [...
1 0 0 0 1 ...
1 0 0 0 1 ...
1 0 0 0 1 ...
1 0 0 0 1 ...
1 0 0 0 1 ...
1 0 0 0 1 ...
0 1 1 1 0 ...
]';
// setting the traing set of two image
xtrain = [T,U];
// so each image as 35 pixels so N(1) is 35
// and we have two images so N($) is 2
N = [35 10 2];
// training the NN
W = ann_FF_init(N);
// The expected response for T : 1 for T, 0 for U
t_t = [1 0]';
// The expected response for T : 1 for T, 0 for U
t_u = [0 1]';
// the overall response
t = [t_t, t_u];
// some parameters
lp = [0.01, 1e-4];
epochs = 3000;
// getting the weight of the trained NN
W = ann_FF_Std_batch(xtrain,t,N,W,lp,epochs);
// testing the traing set.
y = ann_FF_run(xtrain,N,W)
disp('Testing the traing set')
disp(y) //should get something close to t ~ [1 0 ; 0 1]
// testing a distord U
xtest1 = matrix([1, 0, 0, 0, 1;
1, 1, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
1, 0, 0, 0, 1;
0, 1, 1, 1, 1]',-1,1);
y = ann_FF_run(xtest1,N,W)
disp('Testing a distored U')
disp(y) //should get something close to t_u ~ [0 1]
//testing something different from T and U. should get nothing
xtest2 = matrix([1, 0, 0, 0, 1;
1, 1, 0, 0, 1;
1, 0, 1, 0, 1;
1, 0, 1, 0, 1;
0, 0, 1, 1, 1;
0, 0, 1, 0, 1;
0, 1, 1, 1, 1]',-1,1);
y = ann_FF_run(xtest2,N,W)
disp('Testing something neither T nor U')
disp(y)
and the output from scilab's console
Testing the traing set
0.8538757 0.1075397
0.1393287 0.8957439
Testing a distored U
0.1078667
0.9007755
Testing something neither T nor U
0.3433933
0.6306797