SimplifiedWorkflow.m

clear; clc; close all;
%% Generate data, label for Fitting
n=20;
x=linspace(-2,2,n);
y=linspace(-2,2,n);
n1=numel(x); n2=n1;
count=0;
for j=1:n2
    for k=1:n1
        count=count+1;
        data(1,count)=x(j);
        data(2,count)=y(k);
        u=x(j)^2+y(k)^2;
        label(1,count)=(log(1+(x(k)-4/3)^2+3*(x(k)+y(j)-x(k)^3)^2));
        label(2,count)=exp(-u/2).*cos(2*u);
    end
end

%% Network Structure Set Up
NN.Cost='MSE';
InputDimension=2; 
OutputDimension=2; 
LayerStruct=[InputDimension,10,10,10,OutputDimension];
NN=Initialization(LayerStruct,NN);
%%
option.MaxIteration=600;
NN=OptimizationSolver(data,label,NN,option);
%% Validation
Report=FittingReport(data,label,NN);
Prediction=Report.Prediction;
%% Visualization
figure
slice=2;
scatter3(data(1,:),data(2,:),label(slice,:),'black')
hold on
[X,Y]=meshgrid(x,y);
n1=numel(x); n2=numel(y);
surf(X,Y,reshape(Prediction(slice,:),n1,n2))
title('Neural Network Fit')
legend('data','Fitting')