Strange neural network output

Hi, I am trying to use the Neural Network Toolbox but I have troubles in calculating the output of a network. I will try to explain my problem: I have defined a very simple ANN with one hidden layer and linear activation functions. So if I have an input x, then I expect the output of the hidden layer to be

h = w * x + b

where w are the weights and b the biases. Then I expect my output to be

o = w' * h + b'

where w' are the weights between the hidden layer and the output and b' the biases.

Now the problem is that if I do

o = net(x)

this doesn't happen. Here is my code:

net = feedforwardnet([layer1], 'traincgp');

net = configure(net, Dtrain, Dtrain);

net.trainParam.epochs = 0;

net.IW{1,1} = weights12;
net.LW{2,1} = my_weights;
net.b{1} = bias12;

for ii=1:size(net.layers, 1)
    net.layers{ii}.transferFcn = 'purelin';
end;
net = train(net, Dtrain, Dtrain);

As you can see I am training for 0 epochs since this is just a test and I am also using Dtrain both as input and target since I am training an autoencoder. As I said, the problem is that if I calculate the output as I wrote before I get one result, while if I do

output = net(input)

I get another one. What should I do to have the same result?

Answer 1

[Greg Heath]

 Just modify the following

 close all, clear all, clc, tic
[ x, t ] = simplefit_dataset;
[ I N ] = size(x), [O N ] = size(t)
net = fitnet;
net.input.processFcns  = { 'removeconstantrows' };
net.output.processFcns =  { 'removeconstantrows' };
rng('default')
net   = train(net,x,t);
NMSE1 = mse(t-net(x))/var(t)  %   1.7057e-05

 IW = net.IW{1,1}          % [ 10 1 ]
b1 = net.b{1}              % [ 10 1 ]
b2 = net.b{2}              % [ 1  1 ]
LW = net.LW{2,1}           % [ 1  10 ]
B1 = repmat(b1,I,N)        % [ 10 94 ]
B2 = repmat(b2,O,N)        % [1   94 ]
y = B2+LW*tanh(B1+IW*x);   % [1   94 ]
NMSE2 = mse(t-y)/var(t)    %   1.7057e-05

Hope this helps.

Thank you for formally accepting my answer

 I will let you figure out how to handle 

 1. The default mapminmax normalization
 2. Multiple inputs and outputs.

[minomic]

Thank you for your answer. In the meantime I managed to solve the problem by avoiding using 'feedforwardnet' but building the network from scratch with the function 'network'. Anyway I am going to accept this answer since I am sure it works as well.

Cheers,

minomic