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Tried the NN stuff myself, seems to work okay
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Marco
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Mar 17, 2012
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function [delta der] = backprop (w, z, a, targety, layers) | ||
layersum=cumsum(layers); | ||
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%delta=zeros(1, layersum(length(layersum))-layersum(1)); | ||
delta=zeros(1, layersum(length(layersum))); | ||
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der=zeros(size(w)); | ||
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%for i=layersum(length(layersum)):-1:(layersum(1)+1) | ||
for i=layersum(length(layersum)):-1:1 | ||
%% if it is an output neuron | ||
if (i>layersum(length(layersum)-1)) | ||
%delta(i-layersum(1)) = z(i+1)-targety(i-layersum(length(layersum)-1)); | ||
delta(i) = z(i+1)-targety(i-layersum(length(layersum)-1)); | ||
else | ||
ai=a(i+1); | ||
ha=1/((1+abs(ai))^2); | ||
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sum=0; | ||
for k=i+1:layersum(length(layersum)) | ||
sum=sum+delta(k)*w(k,i+1); | ||
end | ||
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delta(i) = ha * sum; | ||
end | ||
end | ||
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%% compute the partial derivatives | ||
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for i=1:1:layersum(length(layersum)) | ||
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% if i<layersum(1)+1 | ||
% for j=0:1:layersum(length(layersum)) | ||
% der(i,j+1)=delta(i-layersum(1)) * z(j+1); | ||
% end | ||
% else | ||
for j=0:1:layersum(length(layersum)) | ||
der(i,j+1)=delta(i) * z(j+1); | ||
end | ||
% end | ||
end | ||
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end |
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function [z a] = nn(w, layers, inputs) | ||
layernos=cumsum(layers); | ||
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%% set z0 to 1 | ||
%% z must be treated as zero-based | ||
z=ones(1, size(w,2)); | ||
a=zeros(1, size(w,2)); | ||
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%% input equals output for input neurons | ||
for i=1:layers(1) | ||
z(i+1)=inputs(i); | ||
end | ||
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%% for each hidden and output neuron | ||
%% i is the neuron number | ||
for i=(layernos(1)+1):size(w,1) | ||
sum=0; | ||
first=1; | ||
%% find first neuron of this layer | ||
for j=2:length(layernos) | ||
if i<=layernos(j) | ||
first=layernos(j-1)+1; | ||
end | ||
end | ||
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%% iterate over all input neurons | ||
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for j=0:(first-1) | ||
sum=sum+z(j+1)*w(i, j+1); | ||
end | ||
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a(i+1)=sum; | ||
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%% for output neurons, output is linear, otherwise use the function. | ||
if (i<=layernos(length(layernos)-1)) | ||
z(i+1)=sum/(1+abs(sum)); | ||
else | ||
z(i+1)=sum; | ||
end | ||
end | ||
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end |
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