MATLAB: Implementation of the feedforwardnet Neural Network

aiartificial intelligenceDeep Learning Toolboxfeedforwardnetmachine learningneural networktraining

I have implemented a very simple neural network to estimate a sine function. The following is the code for generating and training the network:
% Generate Data
dataSize = 1000;
x = linspace(0, 2*pi, dataSize);
y = sin(x);
hold off
plot(x,y)
hold on
% Add noise to Data
yInput = y+randn(1,dataSize)./5;
% No need to seperate training, test and validation data, that occurs automatically in train function.
% Generate Network.  A Very simple two layer model, with two nodes in input layer.
net = feedforwardnet([2]);
% Train Network
net = train(net,x,yInput);
% Show result of trained network
yNN = net(x);
figure
plot(x,yNN, '*')
hold on
plot(x,y, '.')
Now my question, how is this network actually implemented. According to literature, I should be able to recreate the network, copying the weights and biases, with the following function:
function [y] = mynet(net, x_val)
%MYNET A manual implementation of the feedforward network, to demonstrate functionality.
    W1 = net.IW{1};
    b1 = net.b{1};
    W2 = net.LW{2};
    b2 = net.b{2};
    y = purelin(W2*tansig(W1*x_val + b1)+b2);
end
However, the original net(x) function, and mynet(x) produce completely different results. Although the weights and biases are exactly the same, the functions are also directly copied over, you can extract them from the network with:
>> net.layers{1}.transferfcn
ans =
    'tansig'
>> net.layers{2}.transferfcn
ans =
    'purelin'
Can anyone suggest where my implementation of the neural network is wrong. I am really hoping that it is a simple mistake, but I just cant see it at the moment.
Many thanks in advance

Best Answer

How are you handling the preprocessing functions and postprocessing functions?
net = feedforwardnet([2]);
net.inputs{1}.processFcns
ans = 1x2 cell array
    {'removeconstantrows'}    {'mapminmax'}

net.outputs{2}.processFcns
ans = 1x2 cell array
    {'removeconstantrows'}    {'mapminmax'}
See the description of the inputs, outputs, etc. subobjects on this documentation page for more information.
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