Strange neural network output

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minomic
minomic 2015-6-22
评论： minomic 2015-6-23
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?

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采纳的回答

Greg Heath
Greg Heath 2015-6-23
编辑：Greg Heath 2015-6-23
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.
  1 个评论
minomic
minomic 2015-6-23
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

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