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FFNet.m
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classdef FFNet < handle
properties
N0;
N1;
N2;
W1;
W2;
b1;
b2;
d2;
d1;
d0;
r0;
r1;
r2;
end
methods
function obj = FFNet(N0,N1,N2)
obj.N0 = N0;
obj.N1 = N1;
obj.N2 = N2;
obj.W2 = zeros(N2,N1);
obj.W1 = 0.05*randn(N1,N0)/sqrt(N0);
obj.b2 = 0.05*randn(N2,1);
obj.b1 = 0.05*randn(N1,1);
end
function [cst,grad] = FFCost(obj,theta,patterns,DataFactory)
if nargin==4
patterns = DataFactory();
end
M = patterns{1};
V = patterns{2};
N0 = obj.N0;
N1 = obj.N1;
N2 = obj.N2;
W1 = reshape(theta(1:(N0*N1)), N1, N0);
W2 = reshape(theta((N0*N1+1):(N0*N1+N1*N2)), N2, N1);
b1 = theta((N0*N1+N1*N2+1):(N0*N1+N1*N2+N1));
b2 = theta((N0*N1+N1*N2+N1+1):(N0*N1+N1*N2+N1+N2));
W1grad = zeros(size(W1));
W2grad = zeros(size(W2));
b1grad = zeros(size(b1));
b2grad = zeros(size(b2));
nsamples = size(M,2);
x1 = W1*M + repmat(b1,1,nsamples);
r1 = tanh(x1);
x2 = W2*r1 + repmat(b2,1,nsamples);
r2 = x2;
%% Backpropagation
d2 = (r2-V);
f2 = 1;
e2 = d2.*f2;
d1 = W2'*e2;
f1 = 1-r1.^2;
e1 = d1.*f1;
d0 = W1'*e1;
n2ns = N2*nsamples;
W2grad = e2*r1'/n2ns;
W1grad = e1*M'/n2ns;
b2grad = sum(e2,2)/n2ns;
b1grad = sum(e1,2)/n2ns;
cst = 0.5*sum(sum(d2.^2,2))/n2ns;
grad = [W1grad(:) ; W2grad(:) ; b1grad(:) ; b2grad(:)];
end
function theta = GetTheta(obj)
theta = [obj.W1(:) ; obj.W2(:) ; obj.b1(:) ; obj.b2(:)];
end
function SetTheta(obj,theta)
N0 = obj.N0;
N1 = obj.N1;
N2 = obj.N2;
obj.W1 = reshape(theta(1:(N0*N1)), N1, N0);
obj.W2 = reshape(theta((N0*N1+1):(N0*N1+N1*N2)), N2, N1);
obj.b1 = theta((N0*N1+N1*N2+1):(N0*N1+N1*N2+N1));
obj.b2 = theta((N0*N1+N1*N2+N1+1):(N0*N1+N1*N2+N1+N2));
end
function r2 = FProp(obj,r0)
x1 = obj.W1*r0 + obj.b1;
r1 = tanh(x1);
x2 = obj.W2*r1 + obj.b2;
r2 = x2;
obj.r0 = r0;
obj.r1 = r1;
obj.r2 = r2;
end
function d0 = BProp(obj,d2)
f2 = 1;
e2 = d2.*f2;
d1 = obj.W2'*e2;
f1 = 1-obj.r1.^2;
e1 = d1.*f1;
d0 = obj.W1'*e1;
end
end
end