Please Fill Blanks Valid One Input One Output Nn Neurons Hidden Layer Clearvars Clc Addpat Q43779703
Please fill the blanks:
% Only valid for one input, one output and Nn neurons in thehidden layer
clearvars
clc
addpath(‘../Basic_blocks’)
% Main parameters
mu=.01; % Step size
Ns=100000; % Number of samples
Nh=2; % Number of neurons hidden layer
Ni=1; % Number of inputs
No=1; % Number of outputs
in=3;
% Defining the input and the desired signals
x=(rand(1,Ns)-.5)*4;
d=1+sin(in*pi/4*x);
% Defining the variables (weights and bias)
W1=zeros(Nh,Ni,Ns+1); % Weights hidden layer
W2=zeros(No,Nh,Ns+1); % Weights output layer
W1(:,:,1)=rand(Nh,Ni); % Initialization
W2(:,:,1)=rand(No,Nh); % Initialization
b1=zeros(Nh,Ns+1); % Bias hidden layer
b1(:,1)=(rand(Nh,1)-.5)*4; % Iitialization
b2=zeros(No,Ns+1); % Bias output layer
b2(:,1)=(rand(No,1)-.5)*4; % Initialization
tipo=’linear’; % Output nonlinearity
error=zeros(1,Ns); % Error signal
% Loop along the samples including the forward and backwardsteps
for k=1:Ns
y0=[x(k)];
[y1 y2 v1v2]=forward(W1(:,:,k),W2(:,:,k),b1(:,k),b2(:,k),y0,tipo);
e(k)=d(k)-y2;
[delta2 delta1]=backward(W2(:,:,k),y1,y2,e(k),tipo);
W2(:,:,k+1)=W2(:,:,k)+2*mu*delta2*y1′;
b2(k+1)=b2(k)+mu*2*delta2;
W1(:,:,k+1)=W1(:,:,k)+mu*2*delta1*y0;
b1(:,k+1)=b1(:,k)+mu*2*delta1;
end
% How to present results
test=-2:.02:2;
reg=zeros(size(test));
for k=1:length(test)
[y1 reg(k) v1v2]=forward(W1(:,:,Ns),W2(:,:,Ns),b1(:,Ns),b2(Ns),test(k),’linear’);
end
plot(test,1+sin(in*pi/4*test),’r’),hold
plot(test,reg,’–b’),hold off
grid
title(‘Error: q=1,Nh=2’)
Expert Answer
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